*2.4. Cytotoxicity Assays*

Vero E6 cells were seeded at an initial density of 4 × 105 cells/mL in 6-well plates, in culture medium (EMEM 25mM HEPES buffer) supplemented with 1% L-glutamine, 10% fetal bovine serum (FBS), 1% NaPy, 1% NEAA, and 0.1% gentamycin. Cell cultures were then incubated at 37 ◦C in a humidified, 5% CO2 atmosphere in the absence or presence of serial dilutions of test compounds. Cell viability was determined after 7 days at 37 ◦C by the crystal violet staining method.

#### *2.5. Orthohantavirus Antiviral Screening Assay*

Vero-E6 cells were seeded in 6-well plates at an initial density of 4 × 105 cells/mL. Cell cultures were then infected and incubated at 37 ◦C in a humidified, 5% CO2 atmosphere for 7 days in the absence or presence of serial dilutions of test compounds. The antiviral activity was determined by chemiluminescence focus reduction assay (C-FRA), as described previously [22] with small modifications. Briefly, after 7 days of incubation, overlay medium were discarded and cell monolayers were fixed with ice-cold methanol for 8 min at room temperature. After fixing, the methanol was removed, and the cells let dry. Cells were rinsed twice with washing buffer, and then a rabbit anti-Malacky antibody was added at 1:1000 dilution for 1 h at 37 ◦C in a humidified, 5% CO2 atmosphere. Cells were then washed five times with washing buffer and a secondary goat anti-rabbit IgG antibody conjugated to horseradish peroxidase (HRP) was added for an additional hour in 5% CO2 atmosphere. After washing five times with washing buffer cells were incubated with chemiluminescent substrate immediately before the detection. Infected cell foci were detected with the CCD camera for 3 min (or Chemidoc for 30 sec).

#### *2.6. Yield Reduction assay*

Vero E6 cells were inoculated with HTNV at an m.o.i. of 0.05 in maintenance medium and tested compounds at non-cytotoxic concentrations. Following a 1-h adsorption period at 37 ◦C and 5% CO2 on a rocking platform, the inoculum was removed and replaced with fresh medium containing 20 μM concentration of compounds (2j, 2l, 2n). After 3 days, the cell supernatant was harvested and centrifuged (at 3000 rpm, at 4 ◦C, for 10 min) to remove debris and measured for the presence of infectious orthohantavirus hantavirus. The infectious progeny virus in the cell supernatant was evaluated by chemiluminescence focus reduction assay, as described above [22]. RBV was used as the reference compound.

#### *2.7. Statistical Analysis*

Data are represented as mean ± standard deviation (SD) unless otherwise stated. For the yield reduction assay, statistical comparisons were performed using the unpaired *t*-test, and *p*-values less than 0.05 were considered to be statistically significant. All analyses were performed with GraphPad Prism 6.

#### **3. Results and Discussion**

In Figure 3, we report the effects of compounds 5,6-dichloro-2-phenyl-2H-benzo[d][1,2,3]triazoles (2n, 2l, 2j, 2f, 2k) on the replication of HTNV in comparison to RBV, a known broad-spectrum antiviral agent. Series 1, 3, and 4 derivatives turned out as completely inactive and are not reported here (see Table S1).

**Figure 3.** Anti-hantaviral effect of 2n, 2l, 2j, 2f, 2k, and RBV in vitro. Each symbol shows mean values for three independent determinations + SD.

Compounds 1a-c, 1e-j, 2a-n, 3f, 3j-n, and 4f, 4j-n, were evaluated for their potential inhibitory activity against HTNV using a chemiluminescence focus reduction assay (C-FRA). Three compounds (2j, 2l, 2n) showed interesting inhibitory activity (i.e., ≤5 μM; Figure 3). In parallel, moderate to low cytotoxicity was detected for almost all compounds, with CC50 values mostly in the high micromolar range (>30 μM) against all tested cell lines (see Table S1).

From a structure–activity relationship perspective, the most relevant results concerned the potent and selective activity of two urea derivatives (2l and 2n) and one amide derivative (2j), all belonging to the series of the 5,6-dichloro-2-phenyl-derivatives. By contrast, none of the molecules of the series 5,6-dichloro-1-phenyl-derivatives exhibited anti-HTNV activity, with only the exception of the compound 1h (EC50 = 21 μM). Regarding the presence of the two chlorine atoms in positions C5 and C6 of benzotriazole, their elimination (3f, j-n) or substitution with methyl groups (4f, j-n) results in total loss of antiviral activity. Moreover, we can highlight that derivative 2l (R= NHCONH-propyl) showed an EC50 value of 5 μM, but when decreasing or increasing the steric hindrance of the side chain with ethyl-group (2k) or butyl-group (2m), it turned out in a total loss of antiviral activity.

Derivative 2n, recently described by our research group as an interesting inhibitor of HRSV entry [16], showed a remarkable EC50 value of 4 μM against HTNV, resulting in being ten-fold more potent than RBV (EC50 = 37 μM); Figure 4.

**Figure 4.** Focus reduction assay for 2n and ribavirin.VeroE6 cells were infected with HTNV (m.o.i. 0.05). The infected cultures were treated with 2n (panel **A**) and RBV (panel **B**; used as reference) at indicated doses. Ci = untreated control; CC = uninfected control. Antiviral activity was determined by focus reduction assay at day 7 post-infection.

2n also resulted in being more active in in vitro assays against HTNV than two orthohantavirus candidate drugs: ETAR (1-beta-d-ribofuranosyl-3-ethynyl-[1,2,4] triazole) (EC50 = 10 μM) and Favipiravir (EC50 =150.8 μM), respectively 2- and 37-fold more potent [13,23].

Accordingly, to obtain a detailed insight on the efficacy of 2j, 2l, and 2n against HTNV, a yield reduction assay was performed. The reduction of virus titer in the presence of the active compound during a single round of viral infection was determined. Treatment with non-cytotoxic 20 μM concentration of 2j, 2l and 2n caused a significant reduction of viral titer (Figure 5). A significant decline in viral titer of HTNV was also observed at 50 μM treatment of RBV (\* *p*-value <0.05, unpaired *t*-test).

**Figure 5.** Yield of infectious Hantaan (HTNV) viruses produced in infected VeroE6 cells treated with selected benzotriazoles (2j, 2l, 2n) (20 μM) and RBV (50 μM). Results are expressed as means ± standard deviations from 3 separate experiments done in triplicate. \* indicates the *p*-value <0.05.

VeroE6 cells were infected with HTNV (m.o.i. 0.05). The infected cultures were treated with 2j, 2l, 2n at indicated doses, and RBV was used as a reference compound. Viral yields in the culture supernatant were determined by viral titer reduction assay at day 3 post-infection.

In our assay, benzotriazole derivatives 2n, 2l, 2j (20 μM) determined a very interesting reduction of viral titer compared to control cells infected in the absence of inhibitors. The production of the virus was significantly reduced upon treatment with 2l (3,08 log10). The same trend of reduction in viral loads was detected for 2n (2,41 log10) and 2j (2.14 log10). Conversely, in order to obtain a titer decrease, less than 2 logs (1,78 log10) if compared to untreated control, the reference molecule ribavirin needed to be added at 50 μM concentration.


**2. The basic prerequisite – Variance and correlation**

PCA is useful when you have data on a large number of quantitative variables and wish to collapse them into a smaller number of artificial variables that will account for most of the variance in the data. The method is mainly concerned with identifying variances and correlations in the data. Let us focus our attention to the meaning of these concepts. Consider the dataset given in Table 1. This dataset will serve to illustrate how PCA works in practice.

Table 1. **Example dataset, 5 variables obtained for 20 observations.**

The variance of a given variable *x* is defined as the average of the squared differences from the mean:

$$
\sigma\_{\boldsymbol{\alpha}}^{2} = \frac{1}{n} \sum\_{l=1}^{n} \left( \boldsymbol{\chi}\_{l} - \overline{\boldsymbol{\pi}} \right)^{2} \tag{1}
$$

The square root of the variance is the standard deviation and is symbolized by the small Greek sigma *x* . It is a measure of how spread out numbers are.

182 Principal Component Analysis

We think that the well understanding of this Chapter will facilitate that of the following chapters and novel extensions of PCA proposed in this book (sparse PCA, Kernel PCA,

PCA is useful when you have data on a large number of quantitative variables and wish to collapse them into a smaller number of artificial variables that will account for most of the variance in the data. The method is mainly concerned with identifying variances and correlations in the data. Let us focus our attention to the meaning of these concepts. Consider the dataset given in Table 1. This dataset will serve to illustrate how PCA works in practice.

ID X1 X2 X3 X4 X5 1 24 21.5 5 2 14 2 16.7 21.4 6 2.5 17 3 16.78 23 7 2.2 15 4 17.6 22 8.7 3 20 5 22 25.7 6.4 2 14.2 6 15.3 16 8.7 2.21 15.3 7 10.2 19 4.3 2.2 15.3 8 11.9 17.1 4.5 2 14 9 14.3 19.1 6 2.2 15 10 8.7 14.3 4.1 2.24 15.5 11 6.7 10 3.8 2.23 16 12 7.1 13 2.8 2.01 12 13 10.3 16 4 2 14.5 14 7.1 13 3.9 2.4 16.4 15 7.9 13.6 4 3.1 20.2 16 3 8 3.4 2.1 14.7 17 3 9 3.3 3 20.2 18 1 7.5 3 2 14 19 0.8 7 2.8 2 15.8 20 1 4 3.1 2.2 15.3

**2. The basic prerequisite – Variance and correlation**

Table 1. **Example dataset, 5 variables obtained for 20 observations.**

2

*x* . It is a measure of how spread out numbers are.

The variance of a given variable *x* is defined as the average of the squared differences from

1 1 *n x i i*

The square root of the variance is the standard deviation and is symbolized by the small

*n*

2

(1)

*x x*

Multilinear PCA, …).

the mean:

Greek sigma

The variance and the standard deviation are important in data analysis because of their relationships to correlation and the normal curve. Correlation between a pair of variables measures to what extent their values co-vary. The term covariance is undoubtedly associatively prompted immediately. There are numerous models for describing the behavioral nature of a simultaneous change in values, such as linear, exponential and more. The linear correlation is used in PCA. The linear correlation coefficient for two variables *x* and *y* is given by:

$$\rho(\mathbf{x}, y) = \frac{\frac{1}{m} \sum\_{i=1}^{n} (\mathbf{x}\_i - \overline{\mathbf{x}})(y\_i - \overline{y})}{\sigma\_x \sigma\_y} \tag{2}$$

where *x* and *y* denote the standard deviation of *x* and *y*, respectively. This definition is the most widely-used type of correlation coefficient in statistics and is also called Pearson correlation or product-moment correlation. Correlation coefficients lie between -1.00 and +1.00. The value of -1.00 represents a perfect negative correlation while a value of +1.00 represents a perfect positive correlation. A value of 0.00 represents a lack of correlation. Correlation coefficients are used to assess the degree of collinearity or redundancy among variables. Notice that the value of correlation coefficient does not depend on the specific measurement units used.

When correlations among several variables are computed, they are typically summarized in the form of a correlation matrix. For the five variables in Table 1, we obtain the results reported in Table 2.

#### Table 2. **Correlations among variables**

In this Table a given row and column intersect shows the correlation between the two corresponding variables. For example, the correlation between variables X1 and X2 is 0.94.

As can be seen from the correlations, the five variables seem to hang together in two distinct groups. First, notice that variables X1, X2 and X3 show relatively strong correlations with one another. This could be because they are measuring the same "thing". In the same way, variables X4 and X5 correlate strongly with each another, a possible indication that they measure the same "thing" as well. Notice that those two variables show very weak correlations with the rest of the variables.

Given that the 5 variables contain some "redundant" information, it is likely that they are not really measuring five different independent constructs, but two constructs or underlying factors. What are these factors? To what extent does each variable measure each of these factors? The purpose of PCA is to provide answers to these questions. Before presenting the mathematics of the method, let's see how PCA works with the data in Table 1.

The Basics of Linear Principal Components Analysis 185

This quantity measures how spread out the points are around the centroid. We will need

5 2 2 2 '' ' 1

(7)

(, ) ( ) *p ii i i ij i j j*

Two subjects are close one to another when they take similar values for all variables. We can use this distance to measure the overall dispersion of the data around the centroid or to

There are different problems when variables are measured in different units. The first problem is the meaning of the variance: how to sum quantities with different measurement units? The second problem is that the distance between points can be greatly influenced. To illustrate this point, let us consider the distances between subjects 7, 8 and 9. Applying

2 22 2

2 22 2

2 22 2

2 22 2

subjects. Indeed, we could by this way render a tall man as shorter as we want!

Subject 7 is closer to subject 8 than to subject 9. Multiplying the values of variable X5 by 10

Now we observe that subject 7 is closer to subject 9 than to subject 8. It is hard to accept how the measurement units of the variables can change greatly the comparison results among

As seen, PCA is sensitive to scale. If you multiply one variable by a scalar you get different results. In particular, the principal components are dependent on the units used to measure the original variables as well as on the range of values they assume (variance). This makes comparison very difficult. It is for these reasons we should *often* standardize the variables prior to using PCA. A common standardization method is to subtract the mean and divide

> \* *i i*

*x* are the mean and standard deviation of X, respectively.

variance of the data set is the number of observed variables being analyzed.

Thus, the new variables all have zero mean and unit standard deviation. Therefore the total

*x X X X*

(12)

7 8 *dss* ( , ) (10.2 11.9) (19 17.1) .... (15.3 14) 8.27 (8)

7 9 *dss* ( , ) (10.2 14.3) (19 19.1) .... (15.3 15) 19.8 (9)

7 8 *dss* ( , ) (10.2 11.9) (19 17.1) .... (153 140) 175.58 (10)

7 9 *dss* ( , ) (10.2 14.3) (19 19.1) .... (153 150) 28.71 (11)

We define the distance between subjects si and si' using the Euclidian distance as follows:

*d ss s s X X*

this quantity when determining principal components.

cluster the points as in classification methods.

Eq.(7), we obtain the following results:

yields:

where *X* and

**3.2 How work when data are in different units?**

by the standard deviation. This yields the following:

In linear PCA each of the two artificial variables is computed as the linear combination of the original variables.

$$Z = \alpha\_1 X\_1 + \alpha\_2 X\_2 + \dots + \alpha\_5 X\_5 \tag{3}$$

where *j* is the weight for variable j in creating the component Z. The value of Z for a subject represents the subject's score on the principal component.

Using our dataset, we have:

$$Z\_1 = 0.579X\_1 + 0.577X\_2 + 0.554X\_3 + 0.126X\_4 + 0.098X\_5 \tag{4}$$

$$Z\_2 = -0.172X\_1 - 0.14X\_2 + 0.046X\_3 + 0.685X\_4 + 0.693X\_5 \tag{5}$$

Notice that different coefficients were assigned to the original variables in computing subject scores on the two components. X1, X2 and X3 are assigned relatively large weights that range from 0.554 to 0.579, while variables X4 and X5 are assigned very small weights ranging from 0.098 to 0.126. As a result, component Z1 should account for much of the variability in the first three variables. In creating subject scores on the second component, much weight is given to X4 and X5, while little weight is given to X1, X2 and X3. Subject scores on each component are computed by adding together weighted scores on the observed variables. For example, the value of a subject along the first component Z1 is 0.579 times the standardized value of X1 plus 0.577 times the standardized value of X2 plus 0.554 times the standardized value of X3 plus 0.126 times the standardized value of X4 plus 0.098 times the standardized value of X5.

At this stage of our analysis, it is reasonable to wonder how the weights from the preceding equations are determined. Are they optimal in the sense that no other set of weights could produce components that best account for variance in the dataset? How principal components are computed?

### **3. Heterogeneity and standardization of data**

#### **3.1 Graphs and distances among points**

Our dataset in **Table 1** can be represented into two graphs: one representing the subjects, and the other the variables. In the first, we consider each subject (individual) as a vector with coordinates given by the 5 observations of the variables. Clearly, the cloud of points belongs to a R5 space. In the second one each variable is regarded as a vector belonging to a R20 space.

We can calculate the centroide of the cloud of points which coordinates are the 5 means of the variables, that is 1 5 *g* ( ,...., ) *X X* . Again, we can compute the overall variance of the points by summing the variance of each variable:

$$I = \frac{1}{n} \sum\_{i=1}^{n} \sum\_{j=1}^{p} \left( X\_{ij} - \overline{X}\_{j} \right)^{2} = \frac{1}{n} \sum\_{i=1}^{n} d^{2} \left( s\_{i \prime} g \right) = \sum\_{j=1}^{p} \sigma\_{j}^{2} \tag{6}$$

184 Principal Component Analysis

In linear PCA each of the two artificial variables is computed as the linear combination of

11 22 55 *ZX X X*

Notice that different coefficients were assigned to the original variables in computing subject scores on the two components. X1, X2 and X3 are assigned relatively large weights that range from 0.554 to 0.579, while variables X4 and X5 are assigned very small weights ranging from 0.098 to 0.126. As a result, component Z1 should account for much of the variability in the first three variables. In creating subject scores on the second component, much weight is given to X4 and X5, while little weight is given to X1, X2 and X3. Subject scores on each component are computed by adding together weighted scores on the observed variables. For example, the value of a subject along the first component Z1 is 0.579 times the standardized value of X1 plus 0.577 times the standardized value of X2 plus 0.554 times the standardized value of X3 plus 0.126 times the standardized value of X4 plus 0.098

At this stage of our analysis, it is reasonable to wonder how the weights from the preceding equations are determined. Are they optimal in the sense that no other set of weights could produce components that best account for variance in the dataset? How principal

Our dataset in **Table 1** can be represented into two graphs: one representing the subjects, and the other the variables. In the first, we consider each subject (individual) as a vector with coordinates given by the 5 observations of the variables. Clearly, the cloud of points belongs to a R5 space. In the second one each variable is regarded as a vector belonging to a

We can calculate the centroide of the cloud of points which coordinates are the 5 means of the variables, that is 1 5 *g* ( ,...., ) *X X* . Again, we can compute the overall variance of the

> 1 1 (,) *n n p p*

*I X X dsg n n*

*i j i j*

 2 2 2 1 1 1 1

*ij j i j*

(6)

*j* is the weight for variable j in creating the component Z. The value of Z for a subject

112345 *ZXXXXX* 0.579 0.577 0.554 0.126 0.098 (4)

2 12 3 4 5 *Z XX X X X* 0.172 0.14 0.046 0.685 0.693 (5)

... (3)

represents the subject's score on the principal component.

the original variables.

Using our dataset, we have:

times the standardized value of X5.

**3. Heterogeneity and standardization of data**

points by summing the variance of each variable:

**3.1 Graphs and distances among points**

components are computed?

R20 space.

where This quantity measures how spread out the points are around the centroid. We will need this quantity when determining principal components.

We define the distance between subjects si and si' using the Euclidian distance as follows:

$$d^2(\mathbf{s}\_{i'}, \mathbf{s}\_{i'}) = \left\| \mathbf{s}\_i - \mathbf{s}\_{i'} \right\|^2 = \sum\_{j=1}^{p-5} (\mathbf{X}\_{ij} - \mathbf{X}\_{i'j})^2 \tag{7}$$

Two subjects are close one to another when they take similar values for all variables. We can use this distance to measure the overall dispersion of the data around the centroid or to cluster the points as in classification methods.

#### **3.2 How work when data are in different units?**

There are different problems when variables are measured in different units. The first problem is the meaning of the variance: how to sum quantities with different measurement units? The second problem is that the distance between points can be greatly influenced. To illustrate this point, let us consider the distances between subjects 7, 8 and 9. Applying Eq.(7), we obtain the following results:

$$d^2(\mathbf{s}\_7, \mathbf{s}\_8) = (10.2 - 11.9)^2 + (19 - 17.1)^2 + \dots + (15.3 - 14)^2 = 8.27\tag{8}$$

$$d^2(\mathbf{s}\_7, \mathbf{s}\_9) = (10.2 - 14.3)^2 + (19 - 19.1)^2 + \dots + (15.3 - 15)^2 = 19.8\tag{9}$$

Subject 7 is closer to subject 8 than to subject 9. Multiplying the values of variable X5 by 10 yields:

$$d^2(\mathbf{s}\_7, \mathbf{s}\_8) = (10.2 - 11.9)^2 + (19 - 17.1)^2 + \dots + (153 - 140)^2 = 175.58 \tag{10}$$

$$d^2(\mathbf{s}\_7, \mathbf{s}\_9) = (10.2 - 14.3)^2 + (19 - 19.1)^2 + \dots + (153 - 150)^2 = 28.71\tag{11}$$

Now we observe that subject 7 is closer to subject 9 than to subject 8. It is hard to accept how the measurement units of the variables can change greatly the comparison results among subjects. Indeed, we could by this way render a tall man as shorter as we want!

As seen, PCA is sensitive to scale. If you multiply one variable by a scalar you get different results. In particular, the principal components are dependent on the units used to measure the original variables as well as on the range of values they assume (variance). This makes comparison very difficult. It is for these reasons we should *often* standardize the variables prior to using PCA. A common standardization method is to subtract the mean and divide by the standard deviation. This yields the following:

$$X\_i^\* = \frac{X\_i - X}{\sigma\_x} \tag{12}$$

where *X* and *x* are the mean and standard deviation of X, respectively.

Thus, the new variables all have zero mean and unit standard deviation. Therefore the total variance of the data set is the number of observed variables being analyzed.

The Basics of Linear Principal Components Analysis 187

1 11 1 12 2 1 1 ... *p p z x x x Xu*

determined such that the overall variance of the resulting points is as large as possible. Of course, one could make the variance of *z1* as large as possible by choosing large values for

> 2 22 2 11 12 1 1 ... 1

Eq.(14) is also the projections of the *n* subjects on the first component. PCA finds *u*1 so that

The matrix 1 *C XX*' *n* is the correlation matrix of the variables. The optimization problem

1 1

2 2 1 1 1 11

> 1 2 1

*u u* 1 '

*Max u Cu*

This program means that we search for a unit vector *u*1 so as to maximize the variance of the projection on the first component. The technique for solving such optimization problems

> 1 1 1 1 11 *u Cu u u* ' ( ' 1)

> > *Cu u* 1 11

By premultiplying each side of this condition by 1 *u*' and using the condition 1 1 *u u*' 1 we

1 1 1 11 1 *u Cu u u* ' '

and (20) are the maximum eigenvalue and the corresponding eigenvector of the correlation matrix C. Thus the optimum coefficients of the original variables generating the first principal component z1 are the elements of the eigenvector corresponding to the largest

The second principal component is calculated in the same way, with the condition that it is uncorrelated (orthogonal) with the first principal component and that it accounts for the

eigenvalue of the correlation matrix. These elements are also known as loadings.

1 11 ( ) ' '

*Var z z z u X Xu n nn*

*p* is a column vector of weights. The principal component *z1* is

*p* . To prevent this, weights are calculated with the constraint that

*p u* (15)

(17)

(18)

and solving the equation 1 1 / 0 *u*

(19)

(20)

1 that satisfy conditions (19)

is maximal (16)

(14)

their sum of squares is one, that is *u*1 is a unit vector subject to the constraint:

1

(linearly constrained) involves a construction of a Lagrangian function.

Taking the partial derivative 1 1 1 11 / *u Cu u*

largest part of the remaining variance.

It is known from matrix algebra that the parameters *u*1 and

*n i i*

where 1 11 12 1 ( , ,... )' *u*

the weights 11 12 1 , ,...,

is:

yields:

get:

Throughout, we assume that the data have been centered and standardized. Graphically, this implies that the centroid or center of gravity of the whole dataset is at the origin. In this case, the PCA is called normalized principal component analysis, and will be based on the correlation matrix (and not on variance-covariance matrix). The variables will lie on the unit sphere; their projection on the subspace spanned by the principal components is the "correlation circle". Standardization allows the use of variables which are not measured in the same units (e.g. temperature, weight, distance, size, etc.). Also, as we will see later, working with standardized data makes interpretation easier.


**Author details**

Shelley Heusser Private Practice, Johannesburg, South Africa

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*"How Can I Have my Cake and Eat it?" A Contemporary Dissociation-Based Self-State Model… DOI: http://dx.doi.org/10.5772/intechopen.94118*


Imaging in Low Back Pain

*Haider N. Al-Tameemi*

## **Abstract**

Medical imaging plays an important role in the evaluation of low back pain (LBP). The choice of certain radiological method over other depends on many factors like patient's presentation, presence of contraindication, availability, relative cost of the test, and the expected impact of the results on management. Radiological evaluation helps the physician reach the most likely cause of LBP, confirm the provisional diagnosis, provide alternative one, or narrow the differential diagnosis. Plain X-ray radiograph is useful in initial general assessment. Magnetic resonance imaging (MRI) is the imaging modality of choice in the evaluation of LBP because of elegant demonstration of anatomical details and many pathologies. Computerized tomography (CT) can provide high-resolution images of the bony structures and is particularly invaluable in trauma. Other imaging modalities are rarely used usually as problem-solving or in selected conditions. For example, sonography may have a role in the evaluation of soft tissue lesions and the sacroiliac joints. Angiography is useful for vascular evaluation. Isotope imaging may be used in the elucidation of of hidden cause of pain (tumors or fracture). Conventional myelography and discography are virtually obsolete in current clinical practice because of the presence of much safer and accurate new modalities. Finally, interventional radiology has an increasing role in treating certain conditions.

**Keywords:** imaging, radiology, X-ray, MRI, CT scan, sonography, angiography

## **1. Introduction**

The main role of imaging in patients with low back pain (LBP) is to help physicians reach the most likely cause of the pain. The use of one or more of the different radiological investigations in a suitable manner will enhance the detection of the underlying cause of the LBP in a timely way. Moreover, radiology has a role not only in the diagnosis but also in the treatment of some conditions that lead to LBP. The rapidly advancing interventional radiology is increasingly utilized as an adjuvant or sole therapeutic option of a variety of conditions like vascular malformations and tumors.

There are many imaging modalities that can be used in the evaluation of LBP. Selection of the appropriate modality depends on different factors, like the patient's conditions, clinical state, availability, and cost of the test and the presence of certain contraindications.

We will discuss each imaging modality from different points of view starting with the most to the least commonly and widely used ones.

## **2. Imaging modalities used in evaluation of LBP**

## **2.1 Plain X-ray radiograph**

Plain X-ray radiograph is a simple radiological examination that can be used to give initial general picture. Generally it has a limited role in the evaluation of acute LBP [1]. Fluoroscopy (dynamic X-ray) has an important role in guiding interventional procedures and both diagnosis and pain management [2].

## *2.1.1 Advantages*

## *2.1.2 Limitations*

After appropriate positioning of the patient (usually supine, sometimes prone or lateral), the examination is performed in not more than a few seconds in general.

## *2.1.4 Diagnostic value*

Generally, plain X-ray can show:

**115**

*Imaging in Low Back Pain*

*DOI: http://dx.doi.org/10.5772/intechopen.90544*

number, and alignment.

**Figure 1.**

*with spinal degeneration.*

sclerosis near disc (**Figure 1**).

3.Some congenital anomalies of the spine like scoliosis, spina bifida (defect in the back of spine), and vertebral deformities in the form of abnormal shape,

*Lateral X-ray radiograph of the lumbosacral spine of a patient with LBP showing narrowing of multilevel intervertebral disc spaces with marginal osteophytes (white arrows) and juxta-endplate sclerosis, consistent*

4.Lumbosacral spinal degenerative changes in the form of disc space narrowing, marginal osteophytes (bony protrusions at the margins of vertebra), and bony

5.Signs of spinal infections (disc space narrowing with irregularity, bone de-

6.Diseases of sacroiliac joints like ankylosing spondylitis and Reiter's disease in the form of joint space narrowing, irregularity, erosions, and sclerosis.

7.Bone tumors (benign and malignant) affecting the spine or pelvis in the form

Since its first clinical application in medicine about four decades ago, magnetic resonance imaging (MRI) has revolutionized the imaging evaluation of the human

struction, vertebral compression, soft tissue swelling).

of lytic (black) or sclerotic (white) lesions.

**2.2 Magnetic resonance imaging (MRI)**

#### **Figure 1.**

*Novel Imaging and Spectroscopy*

**2.1 Plain X-ray radiograph**

*2.1.1 Advantages*

*2.1.2 Limitations*

*2.1.3 Techniques*

*2.1.4 Diagnostic value*

**2. Imaging modalities used in evaluation of LBP**

1.Low cost (much cheaper than CT and MRI)

1.Poor visualization of soft tissue structures.

2.Cannot show the details of the spinal canal.

or make interpretation of X-ray difficult.

5.Source of radiation to the patient.

Generally, plain X-ray can show:

sign of acute spasm or pain.

the vertebrae above and below.

3.Noninvasiveness (it involves no risky intervention)

5.No significant contraindication (apart from pregnancy)

tion about the type nor the severity of herniation [3].

tional procedures and both diagnosis and pain management [2].

Plain X-ray radiograph is a simple radiological examination that can be used to give initial general picture. Generally it has a limited role in the evaluation of acute LBP [1]. Fluoroscopy (dynamic X-ray) has an important role in guiding interven-

2.Availability (it is usually readily available in almost all hospitals and, most of

3.Cannot show the intervertebral disc material and hence will not give informa-

4.Factors like obesity and excessive bowel gases may obscure some abnormalities

After appropriate positioning of the patient (usually supine, sometimes prone or lateral), the examination is performed in not more than a few seconds in general.

1.General alignment of the spine, any asymmetry, or gross deformity. Straightening of the normal lumbosacral curvature may be considered as an indirect

2.Abnormal position of certain vertebra in the form of foreword (spondylolisthesis) or backward (retrolisthesis) shifting of the vertebral body relative to

time, in centers, including emergency and far health centers)

4.Acceptable resolution of bony structures of the spine and pelvis

**114**

*Lateral X-ray radiograph of the lumbosacral spine of a patient with LBP showing narrowing of multilevel intervertebral disc spaces with marginal osteophytes (white arrows) and juxta-endplate sclerosis, consistent with spinal degeneration.*

## **2.2 Magnetic resonance imaging (MRI)**

Since its first clinical application in medicine about four decades ago, magnetic resonance imaging (MRI) has revolutionized the imaging evaluation of the human body particularly the musculoskeletal system, brain, and spine. In patients with LBP, MRI is considered as the imaging modality of choice because of its excellent demonstration of both the anatomy of the lower back and most of regional pathologies [4].

## *2.2.1 Advantages*

## *2.2.2 Limitations*

## *2.2.3 Contraindications*

**117**

**Figure 2.**

*with hypointense cleft) of other discs.*

*Imaging in Low Back Pain*

*2.2.4 Techniques*

*DOI: http://dx.doi.org/10.5772/intechopen.90544*

roots within the intervertebral canal.

From the diagnostic point of view, MRI can help in:

ligaments (ligamentum flavum) can be seen (**Figure 2**).

*2.2.5 Diagnostic value*

The patient is asked to remove any removable metallic objects like rings and watches. The examination usually takes 10–20 min to complete according to the clinical indication and requested sequences. MRI examination is usually composed of two or more of "sequences." Body tissues and pathologies appear differently on each sequence, and the most commonly used are T1-weighted and T2-weighted sequences. Other sequences used are Short tau inversion recovery (STIR) (suppresses fat signal in the bone marrow and fatty areas), diffusion-weighted image (DWI) (to assess certain features of some lesions according to water molecules diffusion) and fluid attenuation inversion recovery (FLAIR) (suppresses signal of the fluid). Sometimes, when vascular assessment is important, magnetic resonance angiography (MRA) is also conducted. Spinal pathologies can cause both structural and morphological changes. Degeneration of the intervertebral disc, for instance, can be diagnosed when the normal high signal (white) of the disc is lost (disc appears black). Disc prolapse is diagnosed when a part of the disc is seen outside the normal contour of the disc bulging into the spinal canal or compressing the nerve

1.Comprehensive evaluation of the lower back (lumbosacral region) in which the most common causes of LBP arise. Variable pathologies from degenerative, neoplastic, infective, and congenital abnormalities can be elegantly demonstrated. Important and common causes of LBP like intervertebral disc herniation, nerve root compression, lumbar canal stenosis, and degeneration of the small joints at the back of the spine (facet or apophyseal joints) and intraspinal

*Sagittal (A) and axial (B) T2-weigted MRI images of the lumbosacral spine of a patient with LBP showing hypointense (dehydrated) intervertebral disc between the L4 and L5 vertebrae with posterior protrusion (white arrows) causing bilateral neural canal narrowing and root compression. Note the normal signal (hyperintense*

## *2.2.4 Techniques*

*Novel Imaging and Spectroscopy*

*2.2.1 Advantages*

*2.2.2 Limitations*

1.No radiation

2.Noninvasiveness

3.Excellent imaging quality of the spine and pelvis

1.Presence of contraindications (see Section 3.5).

ages that are diagnostically not useful.

tic tests like discography [5].

*2.2.3 Contraindications*

1.Cardiac pacemakers.

throughout pregnancy.

be the source of LBP like the spinal cord and nerve roots

(CT scan and lesser extent X-ray radiography are better).

that slightly reduce diagnostic imaging quality.

sedative/antianxiety drugs may be prescribed.

body particularly the musculoskeletal system, brain, and spine. In patients with LBP, MRI is considered as the imaging modality of choice because of its excellent demonstration of both the anatomy of the lower back and most of regional pathologies [4].

4.Most accurate modality in the demonstration of the neural structures that can

2.Imaging quality is affected by the patient's motion, so examination needs a cooperative and stable patient. Agitated and unstable patients result in bad im-

3.Long examination time, usually 10-20 minutes (longer than X-ray and CT scan).

4.Not so accurate in certain conditions like cortical fractures and calcification

5.Clinical MRI mismatch. Many abnormal findings specially disc degeneration or mild herniation are seen incidentally on spinal MRI of asymptomatic persons and vice versa where MRI can be completely normal in the presence of significant LBP. This has raised the issue of importance of clinical and electrophysiological correlation and the controversial need for more specific diagnos-

2.Metallic object (shells, bullet, orthopedic fixation devices). However, if the fixation devices used were made of MRI-compatible metals (more expensive than regular ones), MRI examination can be safely done despite some artifacts

3.Early pregnancy. However, the chemical material sometimes may be injected intravenously (gadolinium) to enhance the images, and this should not be used

4.Claustrophobia. Seen in about 5–10% of population, when the person is unwillingly afraid of being in closed space. This problem may be resolved by reassurance and careful description of the procedure to the patient. Sometimes the patient will need to be examined using a special type of MRI device called "open type" that has wider aperture and more space around the patient. Rarely

**116**

The patient is asked to remove any removable metallic objects like rings and watches. The examination usually takes 10–20 min to complete according to the clinical indication and requested sequences. MRI examination is usually composed of two or more of "sequences." Body tissues and pathologies appear differently on each sequence, and the most commonly used are T1-weighted and T2-weighted sequences. Other sequences used are Short tau inversion recovery (STIR) (suppresses fat signal in the bone marrow and fatty areas), diffusion-weighted image (DWI) (to assess certain features of some lesions according to water molecules diffusion) and fluid attenuation inversion recovery (FLAIR) (suppresses signal of the fluid). Sometimes, when vascular assessment is important, magnetic resonance angiography (MRA) is also conducted. Spinal pathologies can cause both structural and morphological changes. Degeneration of the intervertebral disc, for instance, can be diagnosed when the normal high signal (white) of the disc is lost (disc appears black). Disc prolapse is diagnosed when a part of the disc is seen outside the normal contour of the disc bulging into the spinal canal or compressing the nerve roots within the intervertebral canal.

## *2.2.5 Diagnostic value*

From the diagnostic point of view, MRI can help in:

1.Comprehensive evaluation of the lower back (lumbosacral region) in which the most common causes of LBP arise. Variable pathologies from degenerative, neoplastic, infective, and congenital abnormalities can be elegantly demonstrated. Important and common causes of LBP like intervertebral disc herniation, nerve root compression, lumbar canal stenosis, and degeneration of the small joints at the back of the spine (facet or apophyseal joints) and intraspinal ligaments (ligamentum flavum) can be seen (**Figure 2**).

#### **Figure 2.**

*Sagittal (A) and axial (B) T2-weigted MRI images of the lumbosacral spine of a patient with LBP showing hypointense (dehydrated) intervertebral disc between the L4 and L5 vertebrae with posterior protrusion (white arrows) causing bilateral neural canal narrowing and root compression. Note the normal signal (hyperintense with hypointense cleft) of other discs.*

## **2.3 Computerized tomography (CT) scan**

The main physical principle underlying CT scan is X-radiation. The use of CT scan in the assessment of the patients with LBP is limited to certain conditions that either need further evaluation after MRI, patients who cannot be examined by MRI, or when the clinical scenario necessitates CT from the start (like trauma).

## *2.3.1 Advantages*

## *2.3.2 Limitations*

**119**

**Figure 3.**

*trauma, showing burst fracture of L4 vertebra.*

*Imaging in Low Back Pain*

rapid (less than minute).

*2.3.4 Diagnostic value*

*2.3.3 Techniques*

*DOI: http://dx.doi.org/10.5772/intechopen.90544*

Considering a case of LBP, CT scan can show:

sequel of old trauma or surgery.

malignant (metastases, multiple myeloma) lesions.

The patient is routinely examined in supine position (back on the table), and the exposure done while the patient is asked not to move. The examination is relatively

1.Abnormal bone densities of the lower spine and pelvis including osteolytic (blacker) and osteoblastic (whiter) lesions, with a lot of possible causes, from incidental nonsignificant (bone islands), benign (hemangiomas, osteomas) to

2.Abnormal configuration and deformity, whether of congenital origin or as

3.Traumatic findings like linear fracture, partial or complete vertebral compression, and burst vertebra (**Figure 3**). CT scan can detect even tiny fractures of the bony cortex particularly of the posterior spinal elements (laminae and pedicles) which are usually difficult to be seen and may be missed on X-ray radiographs [3].

4.Size, shape, and exact location of the metallic foreign body or shell or bullet.

*Sagittal reconstructed image (bone window) of lumbosacral spinal CT scan of a patient with LBP after*

5.Bone erosion secondary to inflammation, infection, or tumor.

## *Imaging in Low Back Pain DOI: http://dx.doi.org/10.5772/intechopen.90544*

## *2.3.3 Techniques*

*Novel Imaging and Spectroscopy*

etc. [6].

*2.3.1 Advantages*

*2.3.2 Limitations*

spinal canal stenosis.

4.Lesser cost than MRI.

**2.3 Computerized tomography (CT) scan**

2.Complete evaluation of the spinal canal including the presence of any narrowing, stenosis or abnormal widening. Measurement of the cross-sectional area

3.Detection of any mass within or outside the spinal canal that may have effects on the spinal cord, nerve roots, soft tissues, or bones including vertebral bony tumors, nerve sheath tumors, lipomas, vascular malformations,

4.Accurate depiction of variable congenital spinal malformations that may present as LBP, like scoliosis, spina bifida, myelomeningocele, dermoids, splitting of the cord, low-lying tethered spinal cord, and absence of the sacrum.

The main physical principle underlying CT scan is X-radiation. The use of CT scan in the assessment of the patients with LBP is limited to certain conditions that either need further evaluation after MRI, patients who cannot be examined by MRI,

1.Excellent depiction of the bony anatomy and bony changes at the lower back including lumbosacral spine and pelvis [7]. Bony changes can be readily seen

2.Can compete with MRI in the visualization of lumbar disc prolapse and bony

or when the clinical scenario necessitates CT from the start (like trauma).

on CT even before appearance on X-ray radiographs.

3.Relatively more widely available and accessible than MRI.

6.Short time of examination (usually less than minute).

fixation devices (but some image compromise).

patient (almost 100 times that of chest X-ray).

early bone marrow infiltration [8].

5.Less affected by the patient's motion during examination than MRI.

7.Can be safely done for patients with metallic implants, shells, and magnetic

1.High risk of radiation exposure [7]. This is indeed the most important disadvantage of CT scan. CT scan delivers a huge amount of radiation dose to the

2.Poor demonstration of intraspinal neural content like cord and nerve root and

3.Artifacts from metallic fixation devices or shells may badly affect the quality of

image and can obscure some anatomical and pathological findings.

of the canal is preferred to objectively assess stenosis.

**118**

The patient is routinely examined in supine position (back on the table), and the exposure done while the patient is asked not to move. The examination is relatively rapid (less than minute).

## *2.3.4 Diagnostic value*

Considering a case of LBP, CT scan can show:

#### **Figure 3.**

*Sagittal reconstructed image (bone window) of lumbosacral spinal CT scan of a patient with LBP after trauma, showing burst fracture of L4 vertebra.*

## **2.4 Sonography**

Sonography (or ultrasound scan, echography) utilizes sound waves of very high frequency that are normally not audible by human beings. Its application in the evaluation of patients with LBP is limited because the ultrasound waves are badly affected by tissues like the bone and air.

## *2.4.1 Advantages*

## *2.4.2 Limitations*

## *2.4.3 Techniques*

No special preparation is usually required. Routinely, a gray-scale (B-mode) scan is applied. Color Doppler scan is used when the assessment of blood flow and vascular imaging is required. Advanced techniques like 3D, 4D, and elastography are increasingly utilized both in research and specialized centers in musculoskeletal imaging.

**121**

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*2.4.4 Diagnostic value*

**2.5 Isotope imaging**

*2.5.1 Advantages*

cancer [10].

1.Not cheap examination.

on site just before examination.

or fusion PET/CT or PET/MRI.

*2.5.2 Limitations*

*DOI: http://dx.doi.org/10.5772/intechopen.90544*

that may be related to the LBP.

face erosion, and soft tissue swellings [9].

metabolic activity of the targeted tissue/organ.

imaging like X-ray, CT scan, or MRI.

Using different technologies, ultrasound can help physician improve the man-

1.Can easily show the content and vascularity of any superficial soft tissue mass

2.Can show some details of some congenital anomalies at the lower back like cysts or fatty (lipomas, dermoid) and vascular (hemangiomas) lesion.

3.Can show some abnormal features of sacroiliac joints like effusion, bone sur-

4.It may help detect some pelvic conditions that may be directly or indirectly related to the cause of LBP including gynecological condition (inflammatory, tumors, cysts, etc.), bowel masses, and other peritoneal pelvic lesions.

Isotope imaging involves the administration of certain radioactive material into the body (usually intravenously), and then images are created according to the

1.Provision of highly useful information about the "metabolic" or "physiological" activity of the lesion, which cannot be obtained by any of the conventional

2.Can detect hidden causes of pain originating from the bony components of the lower spine and pelvic like stress fracture or metastases in patient with known

2.It is not readily available as it is commonly present in specialized centers; PET scanners required very sophisticated measures to prepare the isotope material

4.Has very limited spatial resolution so that interpretation should be correlated with anatomical images. Therefore, most of currently used devices are merged with the conventional anatomical modalities resulting, for example, in hybrid

3.It is a source for radiation to the patients as well as to medical personals.

It usually provides additional diagnostic information to other "structural" imaging modalities (like CT or MRI) to narrow the differential or reach the final diagnosis. Considering the LBP, the most commonly useful isotope imaging modali-

ties used are bone scan and posterior emission tomography (PET).

agement of the LBP in both diagnostic and therapeutic aspects:

## *2.4.4 Diagnostic value*

*Novel Imaging and Spectroscopy*

**2.4 Sonography**

*2.4.1 Advantages*

1.Cheap

4.Noninvasiveness

*2.4.2 Limitations*

examiners.

machines.

*2.4.3 Techniques*

6.Many (but not all) features of spinal degeneration like intervertebral disc space

7.Calcification. CT scan is the best imaging modality in defecting calcification in paraspinal soft tissues, ligaments, muscles, or within a mass. The presence of calcification has important diagnostic impact as it helps narrow the differential diagnostic list or may reach final diagnosis like hydatid cyst or para-articular

Sonography (or ultrasound scan, echography) utilizes sound waves of very high

frequency that are normally not audible by human beings. Its application in the evaluation of patients with LBP is limited because the ultrasound waves are badly

2.Readily available (almost everywhere, anytime), repeated easily

1.Highly operator-dependent. Examination in general and of the musculoskeletal system in particular needs expertise and adequate training. Both false-positive and false-negative results are common by inexperienced

2.Highly device-dependent. The quality of the ultrasound machine has high impact on the quality and subsequent diagnostic outcome of the examination, particularly of the musculoskeletal ones. Many advanced technologies (both software and hardware) are currently emerging that help improve diagnosis which are typically supplied to the mid or even only high-level (expensive)

No special preparation is usually required. Routinely, a gray-scale (B-mode) scan is applied. Color Doppler scan is used when the assessment of blood flow and vascular imaging is required. Advanced techniques like 3D, 4D, and elastography are increasingly utilized both in research and specialized centers in musculoskeletal

3.Demonstrates only selected anatomical details of lower back.

3.Generally safe (no exposure to ionizing radiation)

5.No significant risk or contraindication

narrowing, marginal osteophytes, and facet joints sclerosis.

calcification in some chronic arthropathies.

affected by tissues like the bone and air.

**120**

imaging.

Using different technologies, ultrasound can help physician improve the management of the LBP in both diagnostic and therapeutic aspects:

## **2.5 Isotope imaging**

Isotope imaging involves the administration of certain radioactive material into the body (usually intravenously), and then images are created according to the metabolic activity of the targeted tissue/organ.

It usually provides additional diagnostic information to other "structural" imaging modalities (like CT or MRI) to narrow the differential or reach the final diagnosis. Considering the LBP, the most commonly useful isotope imaging modalities used are bone scan and posterior emission tomography (PET).

## *2.5.1 Advantages*

## *2.5.2 Limitations*

## *2.5.3 Techniques*

Depending on the purpose of the examination, the isotope material (usually incorporated with another carrier substance) is administrated to the patient. The patient is then imaged by the device.

## *2.5.4 Diagnostic value*

## **2.6 Angiography**

Many methods are utilized in angiography (visualization of the vascular system), commonly by injecting a contrast material into the vessels and then taking images. In the assessment of patient with LBP, angiography generally has very limited role like in further evaluation or treatment of a vascular lesion [12].

## *2.6.1 Advantages*

## *2.6.2 Limitations*

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*Imaging in Low Back Pain*

*2.6.3 Techniques*

*2.6.4 Diagnostic value*

stage disease

lesion or canal stenosis.

**2.7 Myelography**

*2.7.1 Advantages*

*2.7.2 Limitations*

*2.7.3 Techniques*

*DOI: http://dx.doi.org/10.5772/intechopen.90544*

chemical contrast material (gadolinium).

vascular component of certain mass

either treating or reducing the bulk of mass

1.More accurate localization of the intraspinal lesion

infection, pain, and radiation exposure.

2.Provision of additional delineation of the spinal canal stenosis

and affected by artifacts and depend on the technique used.

3.Improvement in the diagnosis of some types of nerve root compression

1.The conventional and CT myelography techniques are invasive with risk of

2.The image quality and resolution of MR myelography (MRM) are not so high

This can be achieved invasively by injecting a radiopaque iodine-based contrast

material into the cerebrospinal fluid (CSF) space of the spinal canal through a

tumor, vascular malformation, hemangiomas)

progressive sclerosis."

These are much less in MRI (gadolinium) but this carries risk of "systemic

Conventional angiography involves injection of iodine-based contrast material through an arterial catheter under fluoroscopy. CTA is less invasive, involving the injection of the contrast material intravenously and the patient is imaged by CT scanner. MR angiography can be done both with and without the use of intravenous

1.Can help reaching the final diagnosis of a vascular lesion (whether vascular

2.Provides a road map which can be of great value to the surgeon before operating on certain mass by showing detailed vascular anatomy of the region and

3.Has a great value in the therapy of certain vascular lesions and tumors by

4.Can also help alleviating the pain in advanced cancer that is not responding to medical treatment and beyond surgical treatment specially in those in end-

The visualization of the spinal canal lumen is called myelography. It is usually done as a complimentary to other imaging modalities in the evaluation of spinal

These are much less in MRI (gadolinium) but this carries risk of "systemic progressive sclerosis."

## *2.6.3 Techniques*

*Novel Imaging and Spectroscopy*

patient is then imaged by the device.

so-called cold spots.

Depending on the purpose of the examination, the isotope material (usually incorporated with another carrier substance) is administrated to the patient. The

1.Generally, metabolically active lesions appear of higher signal than adjacent tissues, so-called "hot spots," and vice versa, inactive lesions have lower signal,

2.Can provide invaluable decision about the nature of a vague or suspicious lesion, for example, in the pelvis or lower spine, and whether it is significant and

3.Helps detect many lesions that are metabolically active (like cancer, active inflammation, healing fractures, postoperative) and that are not well or difficult

4.Monitoring the response of cancer after radiotherapy and/or chemotherapy and assessing the presence of a residual/recurrent tumor mass after surgical

Many methods are utilized in angiography (visualization of the vascular system), commonly by injecting a contrast material into the vessels and then taking images. In the assessment of patient with LBP, angiography generally has very limited role like in further evaluation or treatment of a vascular lesion [12].

2.Opportunity of the therapeutic option as some lesions can be treated directly by the angiography by, for example, injecting a special material to occlude and "embolize" the feeding vessel of the mass or vascular anomaly. This is useful for patients who cannot undergo the surgery and with difficult or risky

5.Risk of contrast material particularly the iodine-based material (in conventional angiography and CT angiography (CTA)) like allergy and renal damage.

to be seen by the common modalities (CT scan and MRI) [9, 11].

1.Provision of details about of the vascular component of the lesion

metabolically active or not by assessing its activity.

*2.5.3 Techniques*

*2.5.4 Diagnostic value*

resection.

**2.6 Angiography**

*2.6.1 Advantages*

surgical access.

2.Not widely available.

4.High radiation exposure both to patient and staff.

3.Needs expertise.

*2.6.2 Limitations*

1.Not cheap.

**122**

Conventional angiography involves injection of iodine-based contrast material through an arterial catheter under fluoroscopy. CTA is less invasive, involving the injection of the contrast material intravenously and the patient is imaged by CT scanner. MR angiography can be done both with and without the use of intravenous chemical contrast material (gadolinium).

## *2.6.4 Diagnostic value*

## **2.7 Myelography**

The visualization of the spinal canal lumen is called myelography. It is usually done as a complimentary to other imaging modalities in the evaluation of spinal lesion or canal stenosis.

## *2.7.1 Advantages*

## *2.7.2 Limitations*

## *2.7.3 Techniques*

This can be achieved invasively by injecting a radiopaque iodine-based contrast material into the cerebrospinal fluid (CSF) space of the spinal canal through a

needle inserted at the lower back, and the patient is imaged by either conventional X-ray (conventional myelography) or by CT scan (CT myelography) [10]. With the advent of MRI, it became possible to do myelography noninvasively (without need for any intraspinal injection). This so-called MRM is a great advance in spinal imaging. Nowadays, conventional myelography is mostly superseded by CT myelography which is in turn mainly reserved to those who cannot undergo MRI.

## *2.7.4 Diagnostic value*

The interpretation of myelography is usually done after the complete evaluation of other imaging modalities like X-ray, CT, and MRI. The main diagnostic values are:

## **2.8 Discography**

Discography means the visualization of the content of intervertebral disc after injection of a radiopaque iodine-based contrast material directly into the disc. It is considered as the most accurate method in deciding which disc level is responsible for the LBP and used as reference test in medical researches. However, because it is a very invasive and painful test with many complications and contraindications (like infection, bleeding and severe spinal compromise), its clinical use now is highly restricted for carefully selected patients (e.g., continuous pain with normal noninvasive imaging or sometimes before surgery) and only under experienced interventionalists [14].

**125**

Iraq

**Author details**

Haider N. Al-Tameemi

provided the original work is properly cited.

Radiology Unit, Department of Surgery, Faculty of Medicine, University of Kufa,

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to<EMAIL_ADDRESS>
*Imaging in Low Back Pain*

*DOI: http://dx.doi.org/10.5772/intechopen.90544*

*Imaging in Low Back Pain DOI: http://dx.doi.org/10.5772/intechopen.90544*

*Novel Imaging and Spectroscopy*

*2.7.4 Diagnostic value*

**2.8 Discography**

interventionalists [14].

are:

needle inserted at the lower back, and the patient is imaged by either conventional X-ray (conventional myelography) or by CT scan (CT myelography) [10]. With the advent of MRI, it became possible to do myelography noninvasively (without need for any intraspinal injection). This so-called MRM is a great advance in spinal imaging. Nowadays, conventional myelography is mostly superseded by CT myelography

The interpretation of myelography is usually done after the complete evaluation of other imaging modalities like X-ray, CT, and MRI. The main diagnostic values

1.Filling defects in the myelography can be caused by many intraspinal lesions. Therefore, accurate localization of these lesions whether they are extradural, intradural, extramedullary (outside the cord), or intramedullary (within the

2.MRM can improve the inter-observer and intra-observer agreement in the assessment of lumbar spinal canal stenosis [13], helping more consistent diagno-

Discography means the visualization of the content of intervertebral disc after injection of a radiopaque iodine-based contrast material directly into the disc. It is considered as the most accurate method in deciding which disc level is responsible for the LBP and used as reference test in medical researches. However, because it is a very invasive and painful test with many complications and contraindications (like infection, bleeding and severe spinal compromise), its clinical use now is highly restricted for carefully selected patients (e.g., continuous pain with normal noninvasive imaging or sometimes before surgery) and only under experienced

cord) has great diagnostic value in reaching the most probable cause.

sis when surgical intervention is considered.

which is in turn mainly reserved to those who cannot undergo MRI.

**124**

## **Author details**

Haider N. Al-Tameemi

Radiology Unit, Department of Surgery, Faculty of Medicine, University of Kufa, Iraq

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **References**

[1] Kerry S, Hilton S, Dundas D, Rink E, Oakeshott P. Radiography for low back pain: A randomized controlled trial and observational study in primary care. The British Journal of General Practice. 2002;**52**:469-474

[2] Palmer WE. Spinal injections for pain. Management. Radiology. 2016;**281**(3):669-688

[3] Jarvik J, Deyo R. Diagnostic evaluation of low back pain with emphasis on imaging. Annals of Internal Medicine. 2002;**137**:586-595

[4] Ciricillo SF, Weinstein PR. Lumbar spinal stenosis. The Western Journal of Medicine. 1993;**158**:171-177

[5] Amundsen T, Weber H, Lilleas F, et al. Lumbar spinal stenosis. Clinical and radiologic features. Spine. 1995;**20**:1178-1186

[6] Frassica FJ, Khanna JA, McCarthy EF. The role of MR imaging in soft tissue tumor evaluation: Perspective of the orthopedic oncologist and musculoskeletal pathologist. Magnetic Resonance Imaging Clinics of North America. 2000;**8**(4):915-927

[7] Tins B. Technical aspects of CT imaging of the spine. Insights Imaging. 2010;**1**:349-359

[8] Tins BJ, Lalam RK, Cassar-Pullicino VN, et al. Bone metastases. 2: Pelvis and appendicular skeleton. In: Davies AM, Sundaram M, James SL, editors. Imaging of Bone Tumors and Tumor-like Lesions. Berlin Heidelberg: Springer; 2009. pp. 346-365

[9] Ghosh A, Mondal S, Sinha D, Nag A, Chakraborty S. Ultrasonography as a useful modality for documenting sacroiliitis in radiographically negative inflammatory back pain: A comparative evaluation with MRI. Rheumatology.

2014;**53**:2030-2034. DOI: 10.1093/ rheumatology/keu220

[10] Johnson AW, Weiss CB Jr, Stento K, Wheeler DL. Stress fractures of the sacrum. An atypical cause of low back pain in the female athlete. The American Journal of Sports Medicine. 2001;**29**:498-508

[11] Prandini N, Lazzeri E, Rossi B, et al. Nuclear medicine imaging of bone infections. Nuclear Medicine Communications. 2006;**27**:633-644

[12] Guzman R, Dubach-Schwizer S, Heini P, Lovblad KO, Kalbermatten D, Gerhard Schroth G, et al. Preoperative transarterial embolization of vertebral metastases. European Spine Journal. 2005;**14**:263-268. DOI: 10.1007/ s00586-004-0757-6

[13] Al-Tameemi H, Al-Essawi S, Shukri M, Naji F. Using magnetic resonance myelography to improve Interobserver agreement in the evaluation of lumbar spinal canal stenosis and root compression. Asian Spine Journal. 2017;**11**(2):198-203. DOI: 10.4184/asj.2<IP_ADDRESS>

[14] Walker J, El Abd O, Isaac Z, Muzin S. Discography in practice: A clinical and historical review. Current Reviews in Musculoskeletal Medicine. 2008;**1**:69-83. DOI: 10.1007/ s12178-007-9009-9

**127**

**Chapter 9**

**Abstract**

Radiology

tions and expected improvements.

**2. Basic principles of the CBCT**

**2.1 Image acquisition**

**1. Introduction**

imaging, dentomaxillofacial radiology, incidental findings

Cone-Beam Computed

Tomography in Dentomaxillofacial

*Bence Tamás Szabó, Adrienn Dobai and Csaba Dobo-Nagy*

The daily application of cone-beam computed tomography (CBCT) has been increasing. Not only the number of referrals has been raising, but also the variety of the anatomical regions requested for imaging is also growing in the dentomaxillofacial area. Even though computed tomography (CT) has been widely used in the head and neck region, by the invention of CBCT, some of the drawbacks of CT were overcome and turned into the advantages of the CBCT appliances, such as lower patient dose. In this chapter, we provide a comprehensive picture of the everyday use of CBCT as a modality in the dentomaxillofacial region and its current limita-

**Keywords:** cone-beam computed tomography, dental digital radiography, diagnostic

The use of cone-beam computed tomography (CBCT) has been increasing in everyday clinical practice. The advantages of CBCT contribute to its spreading not only in the field of dentistry, but also in maxillofacial surgery, otorhinolaryngology, rheumatology, and traumatology. Conventional-computed tomography (CT) has been widely used in the head and neck and other anatomical regions; nevertheless, by the invention of CBCT device for maxillofacial imaging in the 1990s [1], some of the drawbacks of CT were overcome resulting in the application of CBCT as an alternative modality in these regions. CBCT devices offer a compact size, the ability for producing high-resolution volumetric data, and lower patient dose compared to multislice CT (MSCT) [2–4]. Nonetheless, it should be noted that CBCT devices operate in a wide range of dose values [5]; hence, in the particular clinical situation, the proper justification and optimization are crucial. In this chapter, we provide a comprehensive picture of the everyday use of CBCT as a modality in the dentomax-

illofacial region and its current limitations and expected improvements.

The CBCT device consists of an X-ray source and a flat-panel detector, which are connected by a C-arm in a fixed position, but their vertical position can be

## **Chapter 9**


**1. Introduction**

In 1882, when Robert Koch made his ground-breaking announcement that he had discovered, isolated and cultured the bacterium responsible for tuberculosis (TB), there were no curative options for people infected with TB. In Roman times, the personal physician of the emperor Marcus Aurelius (161–180) was prescribing sea trips, fresh air and milk to treat TB patients [1]. English, French and German physicians revisited this concept 17 centuries later. In 1840, the English physician George Bodington wrote an essay "On the treatment and cure of pulmonary consumption," where he recommended fresh, cold and open air, exercise, a healthy diet and wine to treat TB patients. He opposed the treatment popular at the time: confinement of TB patients and the use of drugs such as digitalis and antimony potassium tartrate [2]. The journal Lancet published a harsh review of this essay, describing it as "very crude ideas and unsupported assertions" [3], but later admitted that they had been wrong [4]. Other English physicians followed Bodington's ideas of fresh-air treatment for TB patients but were dismissed by the medical intelligentsia [5]. On the other side of the Channel, the French physician Amédée Latour prescribed sunshine, fresh air, exercise and rich food containing 1/8 oz of sea salt every morning to treat TB [6]. The German physician Hermann Brehmer advocated high altitude, fresh air, exercise and a rich diet with some alcohol for the treatment of TB patients [5]. He opened the first sanatorium in 1854 in Görbersdorf, Prussia, to implement his concept. Sanatoria were beneficial to TB patients with early stages of disease. Lower mortality rates were observed in sanatoria compared to TB patients treated at home [1,7]. Sanatoria would close one hundred years later with the introduction of the first multiantimycobacterial drug regimen to treat TB.

The first drug against the etiologic agent of TB, *Mycobacterium tuberculosis,* was streptomycin, isolated from the soil bacterium *Streptomyces griseus* and shown to have activity against *M. tuberculosis* in 1944 [8,9]. Streptomycin was tested on a 21-year-old woman with advanced pulmonary TB and gave "impressive therapeutic effects" [10,11]. Unfortunately, resistance to streptomycin developed quickly [12,13]. In 1946, Jorgen Lehmann published the discovery of the antimycobacterial activity of para-aminosalicylic acid (PAS) in vitro, in guinea pigs and in TB patients [14]. The addition of PAS to streptomycin treatment drastically reduced the emergence of streptomycin-resistant strains in TB patients but did not abolish it [15]. It would take the introduction of a new TB drug, isoniazid (INH), in 1952 to achieve a successful treatment for TB [16].

INH is one of the most effective drugs against *M. tuberculosis*, which is still used to this day to treat active and latent *M. tuberculosis* infections. INH is part of a four-drug regimen (INH, rifampicin (RIF), ethambutol (EMB) and pyrazinamide (PZA)) to treat drug-susceptible *M. tuberculosis* infection. While RIF targets the RNA polymerase RpoB and PZA's mechanism of action is still unclear, INH and EMB target the mycobacterial cell wall.

The *M. tuberculosis* cell wall has an intricate and unique structure composed of a thick peptidoglycan layer and an outer membrane made up mostly of various lipopolysaccharides and fatty acids with imbedded glycolipids and wax esters. This lipid-rich cell wall forms a low-permeability barrier that protects *M. tuberculosis* against most antibiotics. This is one of the many challenges facing TB drug development and the main reason drug target-based screening has been rather unsuccessful [17,18]. Bacteria require an intact cell membrane in order to survive; therefore, the biosynthesis of cell wall components could be considered a weakness to exploit with new and more potent drugs. The ever-expanding threat of drug resistance should motivate greater alacrity in developing these new therapies. Global drug surveillance data from the World Health Organization indicates that in 2018, half a million people were infected with rifampicin- or multidrug-resistant (MDR) TB, and 6% of them had extensively drug-resistant (XDR) TB [19]. While an MDR *M. tuberculosis* strain is "only" resistant to INH and RIF, an XDR strain is resistant to at least five TB drugs (INH, RIF and three second-line TB drugs). Treatment for drug-resistant TB is very long (minimum 20 months), requiring multiple drugs with potential severe adverse reactions. MDR TB treatment's success rate is approximately 55%, whereas XDR TB is barely 39%. New drugs and shorter drug regimens are actively sought to increase these success rates. This article covers TB drugs specifically targeting the mycobacterial cell wall currently used in clinics or in clinical development, focusing on their discovery, activity, toxicity, mode of action and resistance.


**Conflict of interest**

The author declares no conflict of interest.

*Build 3D Abstractions with Wireframes DOI: http://dx.doi.org/10.5772/intechopen.96141*

kind of approaches get an edge against datasets with low number of images, or when these present corrupted texture, blurring, and low definition images where the feature point descriptor fails to detect a fair number of keypoints. The reduced number of correspondences limit the thickness of the point cloud generated by the SfM pipelines, and therefore the accuracy of the estimated camera extrinsics. With inaccurate estimations for the cameras, exploiting homography constraints is not adequate to source line correspondences. Oppositely, [2] is able to reconstruct simple line-based sketches with fair precision and number of lines. It required lower number of images to obtain more complete abstractions than method [1]. The range of scenarios where it is advantageous to use method [1] for 3D abstraction includes sets of pictures of simple objects, with low texture, poor illumination, low resolution, blurring or under other conditions that make difficult the success of a point based algorithm. In these scenarios it outperforms the competition in terms of quantity of lines, precision and completeness of the abstraction. Another conclusion is that camera extrinsics are unavoidably required for 3D abstractions featuring many lines, because the estimation for the camera poses will not be accurate if the

line matching method returns matching outliers or line fragmentation.

using this method will team perfectly with a dense reconstruction.

pipelines for locating the cameras.

*Applications of Pattern Recognition*

**Acknowledgements**

**Conflict of interest**

**54**

The author declares no conflict of interest.

On the other hand, for datasets with moderate number of images, which clear textures, the second approach can be profitable. In this case, the geometric relations from the related points among the images will permit the feature point based pipeline to generate a moderately dense 3D point cloud. In this case, the poses of the cameras obtained by the point based pipeline can be trusted, and used as basis for line matching and linear projection to generate the 3D sketch. The results obtained with method [1] with datasets of hundreds of images are very good. An abstraction

Both approaches are valid for their range of applications. The first one is valid for difficult datasets with noise and low number of images. The second approach will shine with datasets with high texture and many pictures, because it will will profit of the high precision obtained from feature point based 3D reconstruction

The original research work [2] received financial support from the Xunta de Galicia through grant ED431C 2017/69 and Xunta the Galicia (Centro singular de investigación de Galicia accreditation 2016-2019) and the European Union (European Re gional Development Fund - ERDF) through grant ED431G/08.


**3. Types of sewage contaminants**

#### **3.1 Organic sewage contaminants**

Various sources, such as urban runoff, domestic wastewater, industrial discharges, and wet and dry atmospheric deposition, can contribute organic contaminants to wastewater. Sewage sludge typically contains large quantities of many kinds of organic contaminants because they tend to sorb on the suspended solids in wastewater. Sewage sludge contains more than 300 chemicals, representing several different chemical groups, according to the type of sewage that the household produces, whether it's municipal or industrial. The range of their concentrations is between pg. kg-1 and g kg-1. Phthalic acid esters (PAEs), organochlorinated pesticides, monocyclic aromatics, polychlorinated biphenyls (PCBs), phenols, polycyclic aromatic hydrocarbons (PAHs), chlorobenzenes (CBs), amines, nitrosamines, and polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs) are the contaminants that are most frequently found [5].

#### **3.2 Inorganic sewage contaminants**

The dangerous inorganic chemicals found in sewage sludge are generally heavy metals, having concentrations ranging from cadmium 1–3410, arsenic 3–230, copper 80–2300, zinc 101–49,000, nickel 2–179, lead 13–465 to chromium 10–990,000. Environmental risks are not eliminated by thermal procedures, which often transport, and pollutants can build up in both solid and liquid states. Using sewage that has not been treated, which is a source of numerous hazardous and lethal substances, it may be particularly harmful [6].

### **4. Role of heavy metals in sewage pollution**

There have been many harmful substances introduced into the agro-ecosystem, but heavy metals are particularly problematic because they do not degrade and are bio toxic. In addition to lowering crop yield and quality, heavy metals in soil can pose a hazard to the ecosystem's security and public health [7]. For the most part, because of their toxicity, bioaccumulation, and environmental endurance, heavy metals are well-known inorganic contaminants. Along with other contaminants, these inorganic ones slowly migrate to the nearby water and soil. Waterways can become contaminated by heavy metals in several ways. In places with such contamination, severe cases of diseases caused by heavy metals in living systems have been documented. In some countries, wastewater can be discharged into lakes, rivers, and other bodies of water. Such practices contaminate freshwater, harming the environment. Most the earth's natural species are severely affected by heavy metal contamination. The earth's crust contains heavy metals, but due to human activity, there are geochemical and biochemical imbalances involving naturally occurring compounds [8]. Because they may cause cancer and mutagenesis, heavy metal ions from the paper, leather, and textile industries

#### **Table 2.**

*Sewage sludge is one of the sources of heavy metals in water and soil.*

are a severe issue. Some contaminants are poisonous, damaging, and carcinogenic to ecosystems and people. Impurities in some heavy metals are extremely hazardous. Arsenic has long been recognized as a deadly substance. The very poisonous heavy metals chromium, lead, copper, mercury, cadmium, zinc, and nickel, among others, can have detrimental toxic effects. Additionally, the following nitrates, fluoride, selenides, chlorides, phosphates, and chromates have a high-level dangerous effect (**Table 2**) [1].


**2.1 Avocado fruit samples**

214 Infrared Spectroscopy – Life and Biomedical Sciences

There have been limited investigations of avocado maturity based on %DM using NIRS. Schmilovitch et al. (2001) used a dispersive NIR spectrophotometer in reflectance mode to assess the 'Ettinger' and 'Fuerte' cultivars (both relatively thin-skinned) in the range 1200 - 2400 nm. Preliminary results identified standard errors of prediction for both 'Ettinger' and 'Fuerte' as 0.9 and 1.3%, respectively, over a 14 – 24 %DM range. Clark et al. (2003) investigated the use of a fixed polychromatic/diode array (PDA) spectrophotometer for estimating %DM in whole New Zealand 'Hass' avocado fruit using both reflectance and interactance modes. They concluded that interactance mode was a better predictor of %DM compared with reflectance. Reflectance models required high numbers (12 to 20) of latent variables (LV), indicating the models struggled against spectral noise and so required incorporation of many small spectral features to improve accuracy. Clark et al. (2003) reported interactance validation statistics of R2 (coefficient of determination) prediction >0.83, and root mean square error of prediction (RMSEP) <1.8 %DM, over a range of 20 – 45 %DM, while the corresponding reflectance results were <0.75 and >1.9 %DM, respectively. Walsh et al. (2004), using a fixed PDA spectrophotometer (Ziess MMS1/NIR-enhanced spectrometer, Germany) in the 300 - 1100 nm range, reported calibration results of r (correlation coefficient) = 0.89, root mean square error of crossvalidation (RMSECV) = 1.14, with a standard deviation ratio (SDR = standard deviation of the data set divided by the RMSECV or RMSEP) = 2.2, for %DM of avocado fruit of unspecified cultivar. The SDR statistic is the measurement of the ability of an NIRS model to predict a constituent and enables comparison of model performance across populations with different standard deviations (Baillères et al., 2002; Golic & Walsh, 2006). The higher the SDR statistic the greater the power of the model to predict the chemical composition accurately (Cozzolino et al., 2004). SDR values between 2.0 and 2.4 for 'difficult' applications, such as high moisture materials including fruit and vegetables are regarded as adequate for rough screening; a value between 2.5 and 2.9 are regarded as adequate for screening; a value between 3.0 and 3.4 is regarded as satisfactory for quality control; a value between 3.4 and 4.0 is regarded as very good for process control; values above 4.1 are excellent for any application (Nicolaï et al., 2007; Schimleck et al., 2003; Williams,

This study assessed the potential of FT-NIR diffuse reflectance spectroscopy as an objective non-invasive method for determining internal quality attributes of whole 'Hass' avocado fruit. These include: (a) to predict maturity and thereby eating quality based on %DM; (b) to predict the risk of developing internal rot disorders (i.e., rot susceptibility) as an indication of shelf-life; (c) to detect bruises. The study also demonstrates the importance of the calibration model development process to incorporate seasonal and geographical variability

Miyanoto & Yoshinobu, 1995).

2008).

to ensure model robustness.

### **2.1.1 Fruit for dry matter model development**

'Hass' avocado fruit were obtained over the 2006, 2007 and 2008 growing seasons (Harvest months: May to November) from two commercial farms in the major production districts of Bundaberg, South East Queensland (Latitude: 24° 52' South, Longitude: 152° 21' East) and Childers, South East Queensland (Latitude: 25° 14' South, Longitude: 152° 16' East). Avocado fruit were harvested at three maturity stages through each season, corresponding to early, mid and late season harvests over the three growing seasons. This allowed for sufficient variability in the %DM range and other seasonal factors to be included in the calibration procedure. A minimum of 100 fruit were collected at each harvest giving a total of a minimum of 900 individual fruit for each growing region. All fruit were harvested at the hard green stage of ripeness.

### **2.1.2 Fruit for impact and rot model development**

'Hass' avocado fruit were obtained over the 2008 growing season from two farms in Queensland, Australia. The first farm is located near Ravenshoe on the Atherton Tablelands in North Queensland (Latitude: 17° 38' South, Longitude: 145° 29' East) and the second farm is located in the major production district of Toowoomba, South East Queensland (Latitude: 27° 33' South, Longitude: 151° 58' East). Fruit from Ravenshoe were used for the impact assessment trials (n = 102), while Toowoomba fruit (n = 125) were used for rot susceptibility (shelf life) trials. All fruit were harvested at the hard green stage of ripeness.

### **2.2 NIR data collection**

#### **2.2.1 Dry matter NIR data**

The spectra of whole, intact avocado fruit were collected using a commercially available Matrix-F, FT-NIR spectrophotometer (Bruker Optics, Ettlingen, Germany; operating software: OPUS™ version 5.1 - 6.5) in the 830 – 2500 nm range. Spectra were obtained in diffuse reflectance mode, using a standard 4 x 20 watt tungsten light source fibre-coupled emission head fitted to the spectrometer. The external emission head was placed directly above the avocado fruit (0o configuration). A light reducing box with a 60 mm diameter cut out window was used to hold the fruit, so that the fruit skin was directly exposed to the focal point of the emission head. A path-length of approximately 170 mm from the external emission head light source to the surface of the fruit provided a spectral scan diameter on the avocado of approximately 50 mm. In obtaining each sample spectrum, 32 scans at a resolution of 8 cm-1 were collected and averaged. Due to the large variability in the %DM within a fruit (Schroeder, 1985; Wedding et al., 2010; Woolf et al., 2003) two NIR spectra were collected from each fruit, one spectra from each opposing side midway from the peduncle and base (i.e., equatorial region). A white spectralon standard was used as the optical reference standard for the system prior to the collection of each set of sample spectra. Fruit spectra were acquired after sample temperature equilibration in an air-conditioned laboratory at approximately 22 - 24 C, and within two days of harvest.

The Application of Near Infrared Spectroscopy

Model performance was based on the R2 of the calibration (Rc

predicted and actual values) (Buning-Pfaue, 2003), and the SDR.

Fig. 1. Typical absorbance spectra for whole 'Hass' avocado fruit.

to weighting by the standard deviation prior to analysis.

**0**

**2.4.2 Impact and rot data analysis**

**0.4**

**0.8**

**Absorbance**

**1.2**

**1.6**

for the Assessment of Avocado Quality Attributes 217

each data set were separated into a calibration set and prediction set to develop the calibration and prediction models respectively. Fruit were assigned to the calibration set from the principal component analysis (PCA) results to provide a global representation of the attributes of the entire fruit population while eliminating repetition. All remaining fruit where used in the validation sets. Partial least squares (PLS) regression was used to build the prediction models of the diffuse reflectance spectral data, using segmented cross validation (20 segments in this case). Before calibration model development, the variation of the spectral data was analysed by PCA, and obvious spurious spectra eliminated. Data pretreatment and smoothing for the individual %DM models for each growing location in this study were based on a combination of: a 25 point Savitsky-Golay (SG) spectral smoothing (2nd order polynomial) and a second derivative transformation (25 point SG smoothing and 2nd order polynomial) for the Bundaberg models; and a 25 point SG spectral smoothing (2nd order polynomial) and a multiplicative scatter correction (MSC) transformation for the Childers models. For the combined Bundaberg and Childers model, data pretreatment and smoothing was based on a combination of a 25 point SG spectral smoothing (2nd order polynomial) and a first derivative transformation (25 point SG smoothing and 2nd order polynomial). Among all spectra collected, significant noise was found at the extremities of the spectral range (830 – 843 and 2414 - 2503 nm). Therefore all raw spectra used for analysis were truncated to a range of 843 - 2414 nm before model development. Typical absorbance spectra for 'Hass' avocado fruit are shown in Figure 1.

(Rv2) data sets; RMSECV; RMSEP in relation to the bias (average difference between

**800 1200 1600 2000 2400**

'The Unscrambler' version 10.1, (CAMO, Oslo, Norway) was used for discriminative analysis to separate the avocados into categories based on percentage rot and percentage bruise development of the scanned area. The 1 – 2 hours impact wavelengths were subjected

**Wavelength (nm)**

2) and validation/prediction

#### **2.2.2 Impact and rot assessment NIR data**

For both impact (bruise) and rot assessment trials, diffuse reflectance spectra of whole, intact 'Hass' avocado fruit were collected in the 830 – 2500 nm range using a Bruker Matrix-F, FT-NIR spectrophotometer as discussed in section 2.2.1. Spectra for rot susceptibility prediction were collected from each opposing half of the hard green fruit prior to fruit being placed into 20 C storage at 85 - 95% relative humidity. At eating ripe fruit were then assessed for rots based on a weight percentage of the flesh volume affected.

For impact assessment, hard green fruit were stored at 20 C and 85 - 95% relative humidity until fruit reached the sprung stage of ripeness. The sprung stage of ripeness is where the flesh deforms by 2 - 3 mm under extreme thumb pressure (White et al., 2001). Individual spectra were collected from a single side of the fruit on reaching the sprung stage of ripeness. Following initial spectra collection, fruit were dropped from a height of 100 cm against a slate paver (height: 400 mm, length: 400 mm, width: 40 mm) placed upright and supported by concrete blocks to simulate impact damage. Individual fruit were placed into a cotton mesh bag which was firmly suspended by two strings attached to the laboratory ceiling. Each fruit was positioned so that the scanned area would impact against the paver. The fruit in the mesh bag was pulled backwards away from the slate paver and released to swing in a pendulum motion to impact against the slate paver. Fruit were only allowed to impact the paver once. The height from the ground to the middle of the fruit was measured with the fruit sitting freely against the slate paver. The drop height was measured as the difference between the height at the top of the arch, and the height at the bottom of the arch where the fruit hit the paver.

The impacted area was re-scanned after 1 - 2 hours (maximum of 4) and again after 24 hours. Fruit were then placed back into 20 C storage at 85 - 95% relative humidity and assessed for bruises at eating ripe (approximately 5 days following impact). Bruise assessment was based on visual estimate of percentage bruise development of the flesh within the scanned area.

### **2.3 Chemical analysis**

The %DM reference measurement was obtained from the same area of the fruit that was used to obtain the NIR spectra. To determine the %DM, a 50 mm diameter core equal to the NIR scan area was taken perpendicular to the surface of the fruit, at a depth of approximately 10 - 15 mm. The skin (2 - 4 mm) was removed from the avocado flesh, and the flesh was diced to facilitate drying in a fan-forced oven at 60 - 65 C to constant weight (approximately 72 hours). The %DM is defined by the percentage ratio of the weight of the dried flesh sample to the original moist flesh sample. It should be emphasized that fruit spectra and %DM were acquired after sample temperature equilibration in an airconditioned laboratory at approximately 22 - 24 C and within two days of harvest.

#### **2.4 Data analysis**

#### **2.4.1 Dry matter data analysis**

Data analysis was carried out using the commercially available chemometric software package 'The Unscrambler™' version 9.8 (CAMO, Oslo, Norway). The sample spectra for

For both impact (bruise) and rot assessment trials, diffuse reflectance spectra of whole, intact 'Hass' avocado fruit were collected in the 830 – 2500 nm range using a Bruker Matrix-F, FT-NIR spectrophotometer as discussed in section 2.2.1. Spectra for rot susceptibility prediction were collected from each opposing half of the hard green fruit prior to fruit being placed into 20 C storage at 85 - 95% relative humidity. At eating ripe fruit were then assessed for

For impact assessment, hard green fruit were stored at 20 C and 85 - 95% relative humidity until fruit reached the sprung stage of ripeness. The sprung stage of ripeness is where the flesh deforms by 2 - 3 mm under extreme thumb pressure (White et al., 2001). Individual spectra were collected from a single side of the fruit on reaching the sprung stage of ripeness. Following initial spectra collection, fruit were dropped from a height of 100 cm against a slate paver (height: 400 mm, length: 400 mm, width: 40 mm) placed upright and supported by concrete blocks to simulate impact damage. Individual fruit were placed into a cotton mesh bag which was firmly suspended by two strings attached to the laboratory ceiling. Each fruit was positioned so that the scanned area would impact against the paver. The fruit in the mesh bag was pulled backwards away from the slate paver and released to swing in a pendulum motion to impact against the slate paver. Fruit were only allowed to impact the paver once. The height from the ground to the middle of the fruit was measured with the fruit sitting freely against the slate paver. The drop height was measured as the difference between the height at the top of the arch, and the height at the bottom of the arch

The impacted area was re-scanned after 1 - 2 hours (maximum of 4) and again after 24 hours. Fruit were then placed back into 20 C storage at 85 - 95% relative humidity and assessed for bruises at eating ripe (approximately 5 days following impact). Bruise assessment was based on visual estimate of percentage bruise development of the flesh

The %DM reference measurement was obtained from the same area of the fruit that was used to obtain the NIR spectra. To determine the %DM, a 50 mm diameter core equal to the NIR scan area was taken perpendicular to the surface of the fruit, at a depth of approximately 10 - 15 mm. The skin (2 - 4 mm) was removed from the avocado flesh, and the flesh was diced to facilitate drying in a fan-forced oven at 60 - 65 C to constant weight (approximately 72 hours). The %DM is defined by the percentage ratio of the weight of the dried flesh sample to the original moist flesh sample. It should be emphasized that fruit spectra and %DM were acquired after sample temperature equilibration in an air-

Data analysis was carried out using the commercially available chemometric software package 'The Unscrambler™' version 9.8 (CAMO, Oslo, Norway). The sample spectra for

conditioned laboratory at approximately 22 - 24 C and within two days of harvest.

**2.2.2 Impact and rot assessment NIR data**

where the fruit hit the paver.

within the scanned area.

**2.3 Chemical analysis**

**2.4 Data analysis**

**2.4.1 Dry matter data analysis**

rots based on a weight percentage of the flesh volume affected.

each data set were separated into a calibration set and prediction set to develop the calibration and prediction models respectively. Fruit were assigned to the calibration set from the principal component analysis (PCA) results to provide a global representation of the attributes of the entire fruit population while eliminating repetition. All remaining fruit where used in the validation sets. Partial least squares (PLS) regression was used to build the prediction models of the diffuse reflectance spectral data, using segmented cross validation (20 segments in this case). Before calibration model development, the variation of the spectral data was analysed by PCA, and obvious spurious spectra eliminated. Data pretreatment and smoothing for the individual %DM models for each growing location in this study were based on a combination of: a 25 point Savitsky-Golay (SG) spectral smoothing (2nd order polynomial) and a second derivative transformation (25 point SG smoothing and 2nd order polynomial) for the Bundaberg models; and a 25 point SG spectral smoothing (2nd order polynomial) and a multiplicative scatter correction (MSC) transformation for the Childers models. For the combined Bundaberg and Childers model, data pretreatment and smoothing was based on a combination of a 25 point SG spectral smoothing (2nd order polynomial) and a first derivative transformation (25 point SG smoothing and 2nd order polynomial). Among all spectra collected, significant noise was found at the extremities of the spectral range (830 – 843 and 2414 - 2503 nm). Therefore all raw spectra used for analysis were truncated to a range of 843 - 2414 nm before model development. Typical absorbance spectra for 'Hass' avocado fruit are shown in Figure 1. Model performance was based on the R2 of the calibration (Rc 2) and validation/prediction (Rv2) data sets; RMSECV; RMSEP in relation to the bias (average difference between predicted and actual values) (Buning-Pfaue, 2003), and the SDR.

Fig. 1. Typical absorbance spectra for whole 'Hass' avocado fruit.

### **2.4.2 Impact and rot data analysis**

'The Unscrambler' version 10.1, (CAMO, Oslo, Norway) was used for discriminative analysis to separate the avocados into categories based on percentage rot and percentage bruise development of the scanned area. The 1 – 2 hours impact wavelengths were subjected to weighting by the standard deviation prior to analysis.

The Application of Near Infrared Spectroscopy

14 19 24 29 34 **Reference %Dry Matter**

14 19 24 29 34 **Reference %Dry Matter**

14 19 24 29 34 **Reference %Dry Matter**

(a) (b)

(c) (d)

(e) (f)

2007 season, (e) Bundaberg 2008 season, and (f) Childers 2008 season.

Fig. 2. Model predictions plotted against actual constituent values for %DM for (a) Bundaberg 2006 season, (b) Childers 2006 season, (c) Bundaberg 2007 season, (d) Childers

Large seasonal effects have a major consequence for calibration models for horticultural produce, since the spectral deviations due to biological variability of future samples cannot be predicted (Peirs et al., 2003). The influence of seasonal variability was investigated for the Bundaberg and Childers growing locations over three years. For both growing locations, the

**Predicted %Dry Matter**

**Predicted %Dry Matter**

**Predicted %Dry Matter**

for the Assessment of Avocado Quality Attributes 219

**Predicted %Dry Matter**

**Predicted %Dry Matter**

**Predicted %Dry Matter**

14 19 24 29 34 **Reference %Dry Matter**

14 19 24 29 34 **Reference %Dry Matter**

14 19 24 29 34 **Reference %Dry Matter**


**Abstract**

Unlike adolescent idiopathic scoliosis, bracing was used in adults less and was used more as a way of reducing pain. There is little publication of adult scoliosis series in the literature. The use of very high-rigidity and high-precision CAD/ CAM technologies currently makes it possible to create corrective braces for the adult. The digital CAD/CAM cast in three blocks allows for precise correction at the pelvic, lumbar, and thoracic levels. This chapter presents the results of a series of 62 consecutive adult scoliotic patients treated with a corrective asymmetric detorsion brace of very high rigidity made in 2014–2016. Tolerance and angular correction results will be compared to those of 158 patients treated with the former bivalve polyethylene overlapping immobilization brace mainly used for lumbar scoliosis. The new Lyon adult ARTbrace is a detorsion brace adapted to all the curvatures which controls the sagittal plane. Despite a resistance four times greater than that of polyethylene of the same thickness, the tolerance of the Europlex'O is excellent as it is a "shock absorber," and the anterior opening facilitates the use for very old people. Consequently, the aim of this chapter is to consider if it is possible to envisage for some patients an alternative to surgery, thanks to the new technologies of bracing.

**Keywords:** adult scoliosis, bracing, de novo scoliosis, camptocormia, Lyon method, nonsurgical, PSSE, ARTbrace