*4.1. Algebraic Attack*

The security of a public-key cryptosystem depends on the security of the private-key. So, the first attack will be factorization *H* to find the private-keys *G*, *H*, and *M*. If the code parameters *n*, *k*, *d* are large enough, this attack is impracticable, because it is difficult to recover the factors of *H* . This means the security is ensured with the private-key. The security of the new system is also based on decoding in the code *H* , while *H* is not only non-equivalent to the code *H* in the cryptosystem, but after multiplying *HT* by *M* from the right, the error-correction capability of public-key *H* is unknown. Furthermore, the vector *h* is secret. Thus, the best attack may not carry out the complete decoding.


**4. Discussion**

Our findings suggest a potentially novel mechanism for the stress-protective effects of URB597 through β-catenin activation in the NAc in a rat model for PTSD and depression.

We found that exposing rats to shock and reminders induced anxiety-like behavior (i.e., increased freezing and startle response) and depressive-like behavior (i.e., decreased social behavior, induced anhedonia, and despair-like behavior), and also impaired memory function in the social recognition and water maze tasks. Administering the FAAH inhibitor URB597 1 h after shock exposure prevented these effects, as had been previously demonstrated [3,17]. Most of the effects of URB597 on behavior were found to be CB1rdependent, as co-administration of the CB1r antagonist AM251 with URB597 prevented the ameliorating effects of URB597.

Importantly, exposure to shock and reminders decreased β-catenin levels in the NAc and mPFC. Decreased β-catenin levels in the NAc were associated with the behavioral phenotype, including enhanced freezing and startle response, anhedonia, and impaired performance in the WTM. This association led us to focus on upregulating and downregulating β-catenin levels in the NAc. Overexpression of NAc β-catenin in rats exposed to shock and reminders resulted in intact behavior (restores freezing and startle response, no indication of depressive-like behavior, intact memory function) and restored expression of β-catenin, mGluR5, and CB1r in the NAc. It is interesting that overexpressing β-catenin in the NAc also restored these same normal levels in the mPFC. Future studies might test the overexpression of β-catenin in the mPFC. Another future study is to examine the potential therapeutic effects of URB597 and the involvement of β-catenin in female rats.

Increased β-catenin levels were reported to promote resilient responses to stress in the NAc [26]. Mice with stabilized β-catenin in the hippocampus showed resilience to some anxious/depressive manifestations when subjected to the corticosterone model of depression [49]. NAc β-catenin upregulation in mice exposed to social defeat stress resulted in a pro-resilient phenotype, demonstrating less social avoidance and better performance in the FST and elevated plus maze (Dias et al., 2014). Similarly, intra-NAc LiCl (2 μg/side), which upregulates β-catenin activity via inhibition of GSK-3β, facilitated inhibitory extinction [26].

In rats exposed to shock and reminders, we found that β-catenin overexpression restored the decrease in mGluR5 and the increase in CB1r expression in the NAc and mPFC. Decreased mGluR5 levels were observed in animal models of depression and the mPFC of depressed individuals [34,35], and blocking mGluR5 has therapeutic effects in PTSD patients and animal models [34,50]. Increased CB1r in the BLA and CA1 were observed in animal models for PTSD [4,15,17], and increased CB1r availability in the amygdalahippocampal-cortico-striatal circuit in human subjects with PTSD [10].

Other studies have shown a stress-induced decrease in β-catenin levels in the mPFC and NAc that was accompanied by highly susceptible behavioral responses [22,51–53]. Mice with dysfunctional NAc Wnt signaling demonstrated increased depression-like behavior and susceptibility to social defeat stress [23], and β-catenin inactivation in the astrocytespecific glutamate transporter (GLAST)-expressing cells enhanced anxious/depressive-like responses [49]. In depressed patients, lowered β-catenin protein levels, but not mRNA levels were found in the NAc, suggesting that depression may be associated with reduced activity of β-catenin, and perhaps not a defect at the transcriptional level [49,54].

Importantly, URB597 did not prevent the effects of exposure to shock and reminders on behavior in rats with viral-mediated NAc downregulation of β-catenin. This indicates that β-catenin is crucial for URB597 to exert its ameliorating effects on behavior. In a previous study, we showed that downregulating β-catenin using sulindac prevented the facilitating effect of the CB1/2 agonist WIN55,212-2 on extinction [26]. Taken together, these findings suggest a strong functional interaction between CB1r and β-catenin. Indeed, cannabinoids regulate neuronal precursor proliferation via β-catenin; the activation of CB1r enhances the activity of PI3K/AKT; this results in AKT-mediated phosphorylation of GSK-3β, followed by the stabilization of β-catenin that translocates into the nucleus; in the

nucleus, β-catenin regulates transcription and gene expression such as cyclin D1 that is involved in cell proliferation regulation [55]. β-catenin may activate TCF/Lef transcription factors [56] and microRNAs [57], which promote anti-stress responses. This could be a possible explanation for the therapeutic-like effects of URB597, acting through CB1r to modulate β-catenin and produce pro-resilient responses.

There are other relevant pathways that might play a critical role in explaining our results. For example, AEA affects CB1r but also has other targets that might be involved in the effects of URB597 [i.e., AEA is a full agonist of TRPV1, which probably participates in ECB signaling [58]]. Garro-Martinez et al. (2020) suggested a link between β-catenin levels and 5-HT1A receptor functionality underlying the vulnerability or resilience to stress-related disorders [59].


**Transcriptome Profiling Provides Insight into the Genes in Carotenoid Biosynthesis during the Mesocarp and Seed Developmental Stages of Avocado (***Persea americana***)**

**Yu Ge 1 , Zhihao Cheng 1 , Xiongyuan Si 2 , Weihong Ma 1 , Lin Tan 1 , Xiaoping Zang 1 , Bin Wu 1 , Zining Xu 1 , Nan Wang 1 , Zhaoxi Zhou 1 , Xinge Lin 1 , Xiangshu Dong 3,\* and Rulin Zhan 1,\***

Received: 12 August 2019; Accepted: 21 August 2019; Published: 23 August 2019

**Abstract:** Avocado (*Persea americana* Mill.) is an economically important crop because of its high nutritional value. However, the absence of a sequenced avocado reference genome has hindered investigations of secondary metabolism. For next-generation high-throughput transcriptome sequencing, we obtained 365,615,152 and 348,623,402 clean reads as well as 109.13 and 104.10 Gb of sequencing data for avocado mesocarp and seed, respectively, during five developmental stages. High-quality reads were assembled into 100,837 unigenes with an average length of 847.40 bp (N50 = 1725 bp). Additionally, 16,903 differentially expressed genes (DEGs) were detected, 17 of which were related to carotenoid biosynthesis. The expression levels of most of these 17 DEGs were higher in the mesocarp than in the seed during five developmental stages. In this study, the avocado mesocarp and seed transcriptome were also sequenced using single-molecule long-read sequencing to acquired 25.79 and 17.67 Gb clean data, respectively. We identified 233,014 and 238,219 consensus isoforms in avocado mesocarp and seed, respectively. Furthermore, 104 and 59 isoforms were found to correspond to the putative 11 carotenoid biosynthetic-related genes in the avocado mesocarp and seed, respectively. The isoform numbers of 10 out of the putative 11 genes involved in the carotenoid biosynthetic pathway were higher in the mesocarp than those in the seed. Besides, alphaand beta-carotene contents in the avocado mesocarp and seed during five developmental stages were also measured, and they were higher in the mesocarp than in the seed, which validated the results of transcriptome profiling. Gene expression changes and the associated variations in gene dosage could influence carotenoid biosynthesis. These results will help to further elucidate carotenoid biosynthesis in avocado.

**Keywords:** avocado; carotenoid biosynthesis; mesocarp; seed; de novo assembly from short read sequencing; full-length transcript sequencing; differentially expressed genes; gene dosage


**5. Conclusions**

In our study on the identification of priority knowledge gaps to better protect biodiversity and simultaneously secure local livelihoods, we focused on the loss of dry forests by subsistence agriculture in western Madagascar. However, we are aware that large international agribusinesses and the mining industry, as well as oil extraction practices, may locally present a bigger threat to this biome than slash-and-burn farming [39], particularly seeing how these industries has wiped forests from other countries like for example Ghana [141]. It is of great concern that Madagascar's president Andry Rajoelina's recently announced cabinet mainly consists of technicians and business leaders [98] and that he declared to follow Ghana in its recent advances in economic development [142]. Conservationists are highly concerned about his priority of short-term economic gains over long-term security of natural resources and ecosystem services [99,143,144]. Scientists including ourselves therefore call on the new president to keep to his promise aiming to curb corruption and to make Madagascar a model for conservation, while simultaneously urging the international community to continue its financial support of protecting biodiversity in Madagascar [40,143,145–147].

Not surprisingly given the interlinkages of the goals and targets of the SDGs, ABTs and 2 ◦C target of the Paris Climate Agreement [10,12,78], the results of our study demonstrate that even though we started with a focus on SDGs 2, 10 and 15, the priority knowledge gaps identified concern a wide range of other SDGs and address issues necessary to be solved to better reach several ABTs as well as the 2 ◦C target.

Many characteristics of the problems and challenges found in Madagascar are comparable to those in other biodiversity-rich but economically disadvantaged countries [33]. We hope that our idea of focusing the search for key knowledge gaps on a regional scale (i.e., the dry forests of western Madagascar) and using expert involvement via questionnaires can be used as a blue-print, inspiring and aligning most urgent research projects, streamlining research funds and resources to local needs.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2071-1050/11/20/5695/s1: Questionnaire S1, Table S2: Background information of participating experts, Table S3: Complete ranking of all knowledge gaps, Table S4: Knowledge gaps rankings compared between (a) female and male participants, (b) Malagasy participants and those of other nationality, and (c) di fferent age groups, Table S5: Full list of participants' suggested additional knowledge gaps, Figure S1. Di fferences in mean ranking scores by (a) female and male participants, (b) participants of Malagasy origin and those of other nationalities, and (c) participants of young (<25, 25–35), medium (35–45) and older (45–65, >65) age.

**Author Contributions:** Conceptualization: A.S.K.F. and L.S., data curation: A.S.K.F.; Formal analysis: A.S.K.F.; funding acquisition: L.S.; investigation: L.S., A.S.K.F.; methodology: A.S.K.F. and L.S.; project administration: L.S.; resources: L.S.; supervision: L.S.; writing—original draft preparation: A.S.K.F., L.S.; writing—review and editing: A.S.K.F. and L.S.

**Funding:** This research was conducted within the project "Towards a future sustainable world where climate, biodiversity and human well-being are safeguarded" (sustainCBW)", funded by the Leibniz Association (grant number SAS-2017-PIK-LFV) and the Zoological Research Museum Alexander Koenig.

**Acknowledgments:** We thank the sustainCBW project partners Kirsten Thonicke and Diana Sietz (Potsdam Institute for Climate Impact Research), Katrin Vohland and Jens Jetzkowitz (Museum für Naturkunde Berlin), Ana Paula Aguiar and Odirilwe Selomane (Stockholm Resilience Centre), Carolin Sperk (Think Tank for Sustainability, Berlin) for valuable comments in the initial phase of this project and Wolfgang Wägele (Zoological Research Museum Alexander Koenig, Bonn) for logistic support. Particular thanks go to Peter Kappeler, Christian Roos and Matthias Markolf (German Primate Center, Göttingen) who helped pilot trialing the questionnaire. Matthias Markolf also helped us spreading the questionnaires through his networks. We would also like to thank all the participating experts in this study for their time and e ffort to fill in our questionnaire, and our three anonymous reviewers whose comments and suggestions significantly improved an earlier version of this manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.


**3. Fuzzy lozic controller for point to point position control**

#### **3.1 Introduction**

Point-to-point position control is one of the motion control systems that concern much the precision and speed in its performance. Nevertheless, to develop such a high precision and speed controller is quite complicated because of the nonlinearity function in the system such as friction and saturation. Both conditions cannot be compensated or modeled simply by using the linear control theory. Thus, an alternative controller should be developed to overcome this nonlinearity system.

In the motion control system, two main sources are identified to be the parameters variations that cause the nonlinearity condition. There are frictions and inertia variations. The variations of the inertia occur because of numbers of different payload. The different payload at the same time would also cause the different Coulomb friction variations. Both variations are the main parts that need to be solved to improve the performance of the system.

P = Positive, Z = Zero, N = Negative, PB = Positive Big, PS = Positive Small, NB = Negative

For fuzzy inference, Mamdani's Min-Max method is used in both position and anti-swing control. As for defuzzification, centre of area or COA method is used to calculate the crisp value where the final outputs for both controllers are in Voltage. The results of the fuzzy controllers were obtained experimentally and the comparison between classical PID

Fuzzy Controllers show more satisfied result as compared to PID controller where the percentage overshoot and Settling Time were greatly improved. With lower settling time obtained by using the FLC, the performance of the rotary crane system is more stable than

Point-to-point position control is one of the motion control systems that concern much the precision and speed in its performance. Nevertheless, to develop such a high precision and speed controller is quite complicated because of the nonlinearity function in the system such as friction and saturation. Both conditions cannot be compensated or modeled simply by using the linear control theory. Thus, an alternative controller should be developed to

In the motion control system, two main sources are identified to be the parameters variations that cause the nonlinearity condition. There are frictions and inertia variations. The variations of the inertia occur because of numbers of different payload. The different payload at the same time would also cause the different Coulomb friction variations. Both variations are the main parts that need to be solved to improve the performance of the

**3. Fuzzy lozic controller for point to point position control**

Where

Big, NS = Negative Small

Table 4. Positioning perfomances

Table 5. Anti-swing performances

overcome this nonlinearity system.

with the PID controller.

**3.1 Introduction**

system.

controller and FLC is compared as in following table.

PID controller is one of the most used techniques in motion control system due to its simplicity and performances. However, PID controller could only be used effectively in linear system and does not work well with the nonlinearity system. Even if the model of the system is to be developed with PID controller, it would be complicated and this may affect the performance speed of the hardware.

Again, fuzzy approach is the most suitable technique in developing the control algorithm that relates with the nonlinearity function. With its capability in simplifying the model of the system, it can realize the high speed high precision of the system. M. M. Rashid et.al [5] in his article proposed a design of PID controller with added fuzzy logic controller (FLC) of fuzzy-tuned PID controller. With the addition of the FLC, the PID controller can adapt, learn or change its parameters based on the conditions and desired performance.

In this design, fuzzy logic is used to determine the PID controller gains, Kp, Ki, Kd as the function of error and error rate as illustrated in the following block diagram

Fig. 15. Structure of the Fuzzy-tuned PID controller

In developing the fuzzy-tuned PID controller, two design stages are used as follows:

Since there are three gains to be produced, there would be 3 fuzzy tuners to be designed. Each of them has two inputs (error and error rate) and one output (gain). Different membership functions and rules are constructed in each fuzzy tuner.

Fig. 16. Membership function of a) the error and b) error rate for Kp fuzzy tuner

Fuzzy Logic Controller for Mechatronics and Automation 127

To defuzzyfy the output Kd, COA is also used to produce the crisp value. Different membership functions are used for obtaining the gain Ki as shown in the following figures.

(a) (b)

Fig. 20. Membership function of a) the error and b) error rate for Ki fuzzy tuner

Finally, the gain Ki is defuzzified by using the COA as well to obtain the crisp value of integral gain Ki. The fuzzy-tuned PID controller is tested with rotary positioning system for

As listed in the table above, F-PID controller shows better performance with the improvement of the settling time and accuracy. Less error in F-PID controller indicates the high robustness of the controller and thus proving the capability of the fuzzy approach in

nominal object and increased inertia as visualized in the following figures.

Table 7. Rules constructed for Kd fuzzy tuner

Fig. 21. Membership function for output gain Ki

Table 8. Rules constructed for gain Ki.

this system.

Fig. 17. Membership function of output, Kp

Table 6. Rules base for Kp

In defuzzification, the output of crisp value is then obtained by using the Centre of Area (COA) method for gain Kp.

The following figures show the membership function of error, error rate, output and rule base for deriving the gain Kd

Fig. 18. Membership function of a) the error and b) error rate for Kd fuzzy tuner

Fig. 19. Membership function for output gain Kd

In defuzzification, the output of crisp value is then obtained by using the Centre of Area

The following figures show the membership function of error, error rate, output and rule

(a) (b)

Fig. 19. Membership function for output gain Kd

Fig. 18. Membership function of a) the error and b) error rate for Kd fuzzy tuner

Fig. 17. Membership function of output, Kp

Table 6. Rules base for Kp

(COA) method for gain Kp.

base for deriving the gain Kd

Table 7. Rules constructed for Kd fuzzy tuner

To defuzzyfy the output Kd, COA is also used to produce the crisp value. Different membership functions are used for obtaining the gain Ki as shown in the following figures.

Fig. 20. Membership function of a) the error and b) error rate for Ki fuzzy tuner

Fig. 21. Membership function for output gain Ki

Table 8. Rules constructed for gain Ki.

Finally, the gain Ki is defuzzified by using the COA as well to obtain the crisp value of integral gain Ki. The fuzzy-tuned PID controller is tested with rotary positioning system for nominal object and increased inertia as visualized in the following figures.

As listed in the table above, F-PID controller shows better performance with the improvement of the settling time and accuracy. Less error in F-PID controller indicates the high robustness of the controller and thus proving the capability of the fuzzy approach in this system.

Fuzzy Logic Controller for Mechatronics and Automation 129

Mobile robots are generally those robots which can move from place to place across the ground.  The world does not have to be modified to bring all needed items within reach of the robot. The robots can move where needed. Fewer robots can be used. Robots with mobility can perform more natural tasks in which the environment is not designed specially for them. These robots can work in a human centred space and cooperate with men by sharing

A mobile robot needs locomotion mechanisms that enable it to move unbounded throughout its environment. There is a large variety of possible ways to move which makes the selection of a robot's approach to locomotion an important aspect of mobile robot design. Most of these locomotion mechanisms have been inspired by their biological counterparts which are adapted to different environments and purposes.[9],[10] Many

In mobile robotics the terms omnidirectional, holonomic and non holonomic are often used,

The terms holonomic and omnidirectional are sometimes used redundantly, often to the confusion of both. Omnidirectional is a poorly defined term which simply means the ability to move in any direction. Because of the planar nature of mobile robots, the operational space they occupy contains only three dimensions which are most commonly thought of as the x, y global position of a point on the robot and the global orientation, θ, of the robot. Whether a robot is omnidirectional is not generally agreed upon whether this is a twodimensional direction, x, y or a three-dimensional direction, x, y, θ. In this context a non

 The robot configuration is described by more than three coordinates. Three values are needed to describe the location and orientation of the robot, while others are needed to

The robot has two DOF, or three DOF with singularities. (One DOF is kinematically

 The robot configuration is described by three coordinates. The internal geometry does not appear in the kinematic equations of the abstract mobile robot, so it can be ignored.

The robot can instantly develop a wrench in an arbitrary combination of directions

 Non holonomic robots are most prevalent because of their simple design and ease of control. By their nature, non holonomic mobile robots have fewer degrees of freedom than holonomic mobile robots. These few actuated degrees of freedom in non holonomic mobile robots are often either independently controllable or mechanically decoupled, further simplifying the low-level control of the robot. Since they have fewer

In this context a holonomic mobile robot has the following properties:

**4. Mobile autonomous robot system**

biologically inspired robots walk, crawl, slither, and hop.

holonomic mobile robot has the following properties:

The robot has three DOF without singularities.

a discussion of their use will be helpful.[9]

describe the internal geometry.

possible but is it a robot then?)

x, y, θ.

**4.1 Introduction**

a workspace together [9].

**4.2 Mechanism**

Fig. 22. System responses of a) nominal object and b) increased inertia

Table 9. Comparison of the performances of PID and F-PID controllers.


*2.1. Search Strategy and Eligibility Criteria*

We searched for equity-informative economic evaluations of vaccines in electronic databases, including PubMed, Embase, Econlit, and Cost-Effectiveness Analysis (CEA) Registry by Tufts Medical Center from database inception to 15 December 2022. The search terms used included a combination of vaccine, economic evaluation, and equity terms, which were modified to match the search techniques of each database. No language restriction was applied. We also screened reference lists of eligible articles to identify further potentially eligible articles. A full search strategy is presented in Table S2 in the Supplementary Materials.

We included articles that met the following eligibility criteria: full-text articles of economic evaluations estimating costs, outcomes, and health equity impact of vaccines across equity-relevant subgroups in any context. After duplicates were removed, identified articles were independently screened and selected by two reviewers (C.P. and J.-Y.C.) using the eligibility criteria. Article selection was performed using EndNote 20.3. Disagreements were resolved with consensus by discussing with the third reviewer (N.C.)


**4. Synaptic CMS**

184 Neuromuscular Disorders

channel kinetics, but that in the subunit dictates slow-channel kinetics. Thus, the LCPs of four AChR subunits contribute in an asymmetric manner to optimize the activation of AChRs through allosteric links to the channel and to the agonist binding sites (Shen *et al.*, 2005).

The mutation V285I introduces a bulky amino acid into the M3 transmembrane domain and causes FCCMS (Fig. 3). Kinetic studies demonstrate that the mutation slows the channel opening rate and speeds the channel closing rate , resulting in a 15.1-fold reduction in the channel gating equilibrium constant (= /). On the other hand, the mutation minimally affects affinity for ACh. The probability of channel openings decreased when we introduced Leu, a bulky amino acid, at position V285, but rather increased when we introduced smaller amino acids such as Thr and Ala. We observed similar effects when we introduced similar substitutions into the , , and subunits. Thus, introduction of bulky amino acids narrows the channel pore, while introduction of smaller amino acids widens the channel pore. Our analysis thus revealed that the M3 domain backs up the channel-lining pore lined by the M2 transmembrane domains and has stereochemical effects on channel gating kinetics (Wang *et*

FCCMS can be effectively treated with anticholinesterases and 3,4-diaminopyridine. The pharmacologic effects of these drugs were discussed in the section of endplate AChR

Fig. 3. Fast channel CMS. (A) Schematic diagram of AChR subunits with FCCMS mutations. (B) Single channel currents from wild-type and fast channel (V285I) AChRs expressed on HEK293 cells. (C) Miniature endplate current (MEPC) recorded from endplates of a control and a patient harboring V285I. The patient's MEPC decays faster than that of the normal

*al.*, 1999).

control.

deficiency (Section 3.1.2).

Defects in three components of the synaptic basal lamina, AChE, 2 laminin and neural agrin, are associated with CMS. The CMS caused by mutations in agrin was discussed above under the postsynaptic CMS (Section 3.1.3) because the site of action of agrin is the LRP4/MuSK complex at the endplate.

### **4.1 Endplate AChE deficiency due to defects in collagen Q**

Three tetramers of catalytic AChE subunits are linked by a triple helical collagen Q (ColQ) to constitute an asymmetric ColQ-tailed AChE (Krejci *et al.*, 1997). ColQ carries three domains (i) an N-terminal proline-rich attachment domain (PRAD) that organizes the catalytic AChE subunits into a tetramer, (ii) a collagenic domain that forms a triple helix, and (iii) a Cterminal domain enriched in charged residues and cysteines. ColQ-tailed AChE is organized in the secretory pathway, excreted, and anchored into the synaptic basal lamina using two domains of ColQ (Fig. 4). First, the collagen domain harbors two heparan sulfate proteoglycan (HSPG) binding domains (Deprez *et al.*, 2003) that bind to HSPG, such as perlecan (Peng *et al.*, 1999). Second, the C-terminal domain binds to MuSK (Cartaud *et al.*, 2004).

Endplate AChE deficiency is caused by congenital defects of ColQ (Donger *et al.*, 1998; Ohno *et al.*, 1998; Ohno *et al.*, 2000). Congenital defects of ColQ cause endplate AChE deficiency. No mutations have been detected in a gene encoding the catalytic subunit of AChE in CMS

Congenital Myasthenic Syndromes – Molecular Bases

**4.2 CMS due to a defect in 2 laminin**

sulfate 8 to 16 mg per day also shows benefit (Liewluck *et al.*, in press).

nephrotic syndrome that requires renal transplantation (Zenker *et al.*, 2004).

pyridostigmine but were improved by ephedrine.

Maselli and coworkers reported a 20-year-old woman with Pierson syndrome caused by two heteroallelic frameshifting mutations (1478delG and 4804delC) in *LAMB2* who also had a severe CMS (Maselli *et al.*, 2009). The nephrotic syndrome was corrected by a renal transplant at age 15 months. The patient had respiratory distress in infancy, delayed motor milestones, a decremental EMG response, limited ocular ductions, bilateral ptosis, severe proximal limb weakness, scoliosis, and required assisted ventilation at night and sometimes during the day. AChE activity was spared at the NMJ. Electron microscopy of the NMJ showed small axon terminal size and encasement of nerve endings by the Schwann cell, widening of the primary synaptic clefts with invasion of the synaptic space by processes of Schwann cells, moderate simplification of postsynaptic membranes, and decreased number of synaptic vesicles. Both morphological and microelectrode studies were similar to those observed in *Lamb2*-mice (Noakes *et al.*, 1995). Notably, symptoms were worsened by

of Congenital Defects of Proteins at the Neuromuscular Junction 187

Anticholinesterase medications have no effect on neuromuscular transmission and can cause excessive muscarinic side effects. Quinidine (Fukudome *et al.*, 1997; Harper & Engel, 1997) and fluoxetine (Harper *et al.*, 2003), which shorten the open duration of the AChR channel and benefit the slow-channel syndrome, can increase muscle weakness. A respirator dependent infant with severe endplate AChE deficiency was improved by intermittent blockade of AChR by atracurium, an agent that protects AChR from overexposure to ACh (Breningstall *et al.*, 1996). Ephedrine sulfate at a dose of 150 to 200 mg per day in adults is effective for myasthenic symptoms (Bestue-Cardiel *et al.*, 2005; Mihaylova *et al.*, 2008). Although high concentrations of ephedrine are able to block AChR openings (Milone & Engel, 1996), molecular bases of ephedrine effects in clinical practice remain elusive. As an alternative to ephedrine, albuterol

Laminins are cruciform heterotrimeric glycoproteins composed of , , and chains and are assembled from products of five , four , and three genes. The laminin molecules are named according to their chain composition. For example, laminin-321 contains 3, 2, and 1 chains (Aumailley *et al.*, 2005). Three laminins are present at the synaptic basal lamina, laminin-221, laminin-421, and laminin-521. Each contains the 2 subunit. Laminin-421 is restricted to the primary synaptic cleft and promotes the precise alignment of pre- and postsynaptic specializations. Laminin-521 lines the primary and secondary clefts, promotes presynaptic differentiation, and prevents Schwann cells from entering the synaptic cleft. The synaptic laminins provide a stop signal for axons at developing endplates and organize presynaptic differentiation (Sanes, 1997). Mice deficient for *Lamb2* that encodes 2 laminin show reduced terminal branching of presynaptic motor axons, with a decreased number of active zones, no clustering of the synaptic vesicles above the active zones, and extension of Schwann cell processes into the primary synaptic cleft, and decreased spontaneous and evoked quantal release (Noakes *et al.*, 1995; Patton *et al.*, 1998). In addition to its presence at the endplate, 2 laminin is also highly expressed in renal glomeruli and the eye. *LAMB2* mutations in humans cause Pierson syndrome characterized by ocular malformation including small non-reactive pupils, loss of accommodation, and abnormalities of the lens, cornea and retina and by fatal

or in any other disease. There are three classes of ColQ mutations. First, mutations in the proline-rich attachment domain (PRAD) hinder binding of ColQ to AChE. Sedimentation analysis of AChE species of the patient muscle and transfected cells shows complete lack of ColQ-tailed AChE. Second, mutations in the collagen domain, most of which are truncation mutations, hinder formation of triple helix of ColQ. Sedimentation analysis of muscle and transfected cells demonstrate a truncated single-stranded ColQ associated with a homotetramer of AChE. Third, the mutations in the C-terminal domain have no deleterious effect on formation of the asymmetric ColQ-tailed AChE, but they compromise anchoring of ColQ-tailed AChE to the synaptic basal lamina as elegantly shown in vitro overlay binding of mutant and wild-type human recombinant ColQ-tailed AChE to the frog endplate (Kimbell *et al.*, 2004).

Fig. 4. ColQ anchors to the synaptic basal lamina by binding to perlecan and MuSK.

EMG studies show a decremental response as in other CMS. In addition, most patients have a repetitive CMAP response on a single nerve stimulus. The repetitive CMAP decrements faster than the primary CMAP. It can be overlooked unless a well rested muscle is tested by single nerve stimuli. The prolonged dwell time of unhydrolyzed ACh in the synaptic space prolongs the endplate potential; when this exceeds the absolute refractory period of the muscle fiber action potential, it elicits a repetitive CMAP. As mentioned above, a repetitive CMAP also occurs in slow channel syndrome.

Some aspects of the pathophysiology of endplate AChE deficiency resemble those of the SCCMS. As in the SCCMS, neuromuscular transmission is compromised by three distinct mechanisms. First, staircase summation of endplate potentials causes a depolarization block, which inactivates a proportion the voltage-gated skeletal sodium channel, NaV1.4. (Maselli & Soliven, 1991). Second, prolonged exposure of AChR to ACh during physiologic activity desensitizes a fraction of the available AChRs (Milone *et al.*, 1997). Third, repeated openings of AChR cause calcium overloading to the endplate, which culminates in an endplate myopathy (Groshong *et al.*, 2007). Unlike in the SCCMS, the nerve terminals are abnormally small and often encased by Schwann cells. This decreases the quantal content and hence the amplitude of the endplate potential (Engel *et al.*, 1977).

Anticholinesterase medications have no effect on neuromuscular transmission and can cause excessive muscarinic side effects. Quinidine (Fukudome *et al.*, 1997; Harper & Engel, 1997) and fluoxetine (Harper *et al.*, 2003), which shorten the open duration of the AChR channel and benefit the slow-channel syndrome, can increase muscle weakness. A respirator dependent infant with severe endplate AChE deficiency was improved by intermittent blockade of AChR by atracurium, an agent that protects AChR from overexposure to ACh (Breningstall *et al.*, 1996). Ephedrine sulfate at a dose of 150 to 200 mg per day in adults is effective for myasthenic symptoms (Bestue-Cardiel *et al.*, 2005; Mihaylova *et al.*, 2008). Although high concentrations of ephedrine are able to block AChR openings (Milone & Engel, 1996), molecular bases of ephedrine effects in clinical practice remain elusive. As an alternative to ephedrine, albuterol sulfate 8 to 16 mg per day also shows benefit (Liewluck *et al.*, in press).


**5. Summary**

The main risk factors for cardiovascular atherosclerotic disease are high total and LDL cholesterol, elevated blood pressure, smoking, psychological stress, uncontrolled or poorly controlled diabetes, and obesity. Risk factors are treated based on the goals set by the guidelines.

Regular physical activity is important in secondary prevention.

In general, a reduction of sedentary time and more involvement in activities is recommended for all adults for primary and secondary prevention. Cardiac rehabilitation is encouraged for all patients with established CvAD.

A diet poor in saturated fats and rich in vegetables and fruits lowers the risk of cardiovascular disease.

Maintaining a normal weight may influence favorably other risks such as elevated blood pressure and elevated total and LDL cholesterol, reducing, thus, the CvAD risk.

Mental healthcare, especially management of depression, improves the quality of life and reduces the risk for CvAD.

Stopping smoking reduces the CvAD risk in all persons. Assistance drug therapy and follow-up support should be used to encourage patients to stop smoking if difficulties are observed.

The levels of LDL should be lowered by the use of statins, ezetimibe, and/or PCSK9 inhibitors in all patients with CvAD aiming to achieve the goal of values less than 55 mg/dL (<1.4 mmol/L), or if not achieved, by a reduction of more than 50% compared to baseline.

Lifestyle interventions and antihypertensive drugs, such as beta-blockers, ACE inhibitors, or ARBs, are recommended to lower BP levels based on recent guidelines aiming to achieve levels of 120/80 mmHg or lower.

Antiplatelet therapy with aspirin is indicated in all patients with CvAD. In patients with CvAD and diabetes, treatment of diabetes with a target of 6.5–7.5% HbA1c is recommended.

**Table 7** summarizes the use of cardioprotective medications in CvAD.

### **Table 7.**

*Recommendations for cardioprotective medical therapy in patients with CvAD (ESC guidelines 2021).*


**Acknowledgements**

I sincerely thank Dr. Rong-Huay Juang (National Taiwan University of Science and Technology) for stimulating discussion and critical reading of the manuscript. *Phytochelatin Synthase in Heavy Metal Detoxification and Xenobiotic Metabolism DOI: http://dx.doi.org/10.5772/intechopen.99077*


**Azita Goshtasebi**

Department of Family Health, Mother and Child Health Research Center, Iranian Institute for Health Sciences Research, ACECR Tehran, Iran

VIII Preface

dysfunctions.

book to be valuable and practical in education, research and treatment of sexual

Department of Family Health, Mother and Child Health Research Center,

Iranian Institute for Health Sciences Research, ACECR

**Azita Goshtasebi**

Tehran, Iran

**Part 1**

**Epidemiology of Sexual Dysfunctions**


**Comparing the Dynamic Analysis of Energy Efficiency in China with Other Countries**

Chenchen Yang, Feng Yang, Liang Liang and Xiping Xu

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/48589


Surgical Planning and Additive Manufacturing of an Anatomical Model: A Case Study of a Spine Surgery

*Levent Aydin, Ozgur Cakir, Riza Dilek and Mucahit Ege*

#### **Abstract**

3D scanning technologies have promising solutions for medical needs such as anatomical models, biocompatible implants, and orthotic/prosthetic models. Although virtual presurgical planning offers more precise results, it may not be applied in every hospital because of the high costs. The aim of this study is to assess the accuracy of the suggested low-cost and effective surgical planning method by means of additive manufacturing to increase success rate of each surgery. In this study, a full spine model of a scoliosis patient was acquired and reconstructed in MIMICS software using different filters and parameters. Therefore, a comparison in terms of geometrical errors among each model was performed based on a reference model. Subsequently, patient-specific full spine model was manufactured using a three-dimensional printing method (fused deposition modeling) and utilized before the surgery. 3D surgical model reconstruction parameters such as wrap tool, binomial blur, and curvature flow filters produced high geometrical errors, while mean filter produced the lowest geometrical error. Furthermore, similarity results of the curvature flow and discrete Gaussian filters were close to mean filter. Smooth tool and mean filter produced almost the same volume of the reference model. Consequently, an ideal protocol for surgical planning of a spine surgery is defined with measurable accuracy. Thus, success rate of a spine surgery may be increased especially for the severe cases owing to the more accurate preoperative review: operability.

**Keywords:** additive manufacturing, image guided surgery, orthopedic surgery, surgical planning, accuracy assessment, scoliosis

#### **1. Introduction**

Scoliosis is a three-dimensional (3D) deformity in the natural shape of spine that requires surgery in serious cases [1]. A lateral deviation of the spine greater than 10 degrees is accepted as scoliosis and an abnormal sideways curvature is observed from the frontal plane of a patient, while a healthy spine should look like straight [2, 3]. Although hereditary factors play a role in the etiology, most of the cases (80% of the patients) with unknown cause are called idiopathic scoliosis and classified into three main groups based on the age, namely, (i) infantile (up to 3 years

old), (ii) juvenile (4–9 years old), and (iii) adolescent (10 years old to teen years) [4]. The severity of scoliosis is also determined by Cobb method that provides obtain information about the curvature of the spine in terms of degrees. According to these measurements, a scoliotic curve of the spine is defined as (i) mild scoliosis (10–25°, requires monitoring), (ii) significant scoliosis (25–40°, treated with bracing), and (iii) severe scoliosis (over 40°, requires surgery) [5]. Recently, a number of the scoliosis patients are growing up to 2–3% worldwide due to the unawareness of people [6]. It is important to predict risk of progression, diagnose at early stage for preventing degenerative effects, and contribute to the patient's quality of life. Unfortunately, in some cases, rapid progress on spine deformity or late diagnosis is resulted with surgical operations that are necessary to stabilize the spine by means of rod placement on each affected vertebra [6]. All operations require precise processes in a limited workspace because of the spinal nerves and blood vessels. Besides, an injury of a vessel around the surgical site may initiate clotting that may result with pulmonary embolism or even death [7]. Therefore, novel technologies and methods are of great importance to assist or plan the surgery in advance.

Medical imaging technologies such as multidetector computed tomography (MDCT) and magnetic resonance imaging (MRI) are rapidly evolving to visualize more complex tissues of the human body [8]. Furthermore, high-resolution images provide radiologists more accurate diagnosis [9]. Although 3D visualization of the scanned tissues simplifies the surgical procedures, patients may undergo a suboptimal outcome after the treatment [10]. On the other hand, two-dimensional (2D) computer screens prevent the direct interaction with the target 3D model [11]. While virtual presurgical planning offers more precise results and overcomes mentioned drawbacks above, it is not applicable for every hospital because of its high costs [10]. Additive manufacturing, layer-by-layer fabrication of a physical object, is increasingly being used especially in the fields of medicine. Nowadays, these technologies are utilized with medical imaging to produce patient-specific (i) medical devices, orthosis, and prosthesis [12], (ii) anatomical to assist-surgical models [13], (iii) body parts [14], (iv) dental and maxillofacial implants [15], (v) blood vessels [16], and (vi) organs [17]. Typical 2D digital imaging and communications in medicine (DICOM) files from the CT or MRI images are transformed into a 3D standard tessellation language (STL) file to perform 3D printing of the target model. A wide range of polymers including glass, ceramic, metal, or biological materials may be used to create complex models in a cost-effective way compared to conventional manufacturing methods [18]. Although the latest innovative studies are focused on a functional tissue or even an organ printing, clinical bedside applications of these technologies are difficult and limited [11]. On the other hand, surgical planning or patient-specific implant production via 3D printing is available for any case and widely used to manage a complex surgery or achieve the exact placement of an implant [18]. Consequently, success rate of each surgery is significantly increased as well as it becomes minimally invasive and requires shorter recovery owing to the preoperative review [19, 20].

Surgical planning by means of additive manufacturing consists of five basic steps, namely, (i) data acquisition, (ii) image segmentation, (iii) 3D model reconstruction, (iv) 3D printing, and (v) preoperative review [21]. In data acquisition step, a medical imaging system such as CT is used to obtain 2D DICOM images in general. The accuracy of surgical planning is first dependent on the contrast-tonoise ratio (CNR) of scanned raw images [22]. High CNR values provide detailed segmentation before the 3D reconstruction of a model. These images are then processed via a commercial (Materialize Mimics) or an open-source (InVesalius) medical image processing software using the default segmentation functions or manual

**67**

*Surgical Planning and Additive Manufacturing of an Anatomical Model: A Case Study…*

selection of the region of interest on each image during the segmentation step. Thus, the target anatomical model's contour is masked on each image section. In 3D model reconstruction step, the mask images are positioned sequentially to form a solid model in 3D workspace of the software. Tissue-specific filters or data sets may be applied to minimize noise using predefined thresholds before and after the 3D reconstruction process. The solid model is exported as a STL file and prepared to be 3D printed. In 3D printing step, the STL file is 3D printed using one of the additive manufacturing methods such as fused deposition modeling (FDM), selective laser sintering (SLS), or stereolithography (SLA). In a preoperative review step, the printed model is evaluated and used before and during the surgery (requires sterilization) as a reference model that guide the operation as well as it can be used for custom implant or assistive surgical apparatus creation such as drill guides [23]. Since surgical planning is important and plays a crucial role during the surgery, reference 3D model requires to be created with minimal geometrical errors. The accuracy of 3D reconstructed models can be analyzed by calculating the Hausdorff distance (HD) and dice similarity coefficient (DSC) values for each model [24, 25]. While DSC measures the volume overlap between two models that ranges from 0 (no overlap) to 1 (exact overlap), HD calculates the Euclidean distance between each binary volume [26, 27]. Furthermore, several methods (geometric distance, roughness, structure, saliency, and strain-energy-based calculations) may be implemented to assess visual quality and accuracy of the 3D models [28]. In this study, a spine model of a scoliosis patient is first acquired and then 3D reconstructed in MIMICS software (manual and automatic segmentation) using different filters and parameters to compare each geometrical error. The results of the DSC and HD values of each reconstructed model reveal the ideal protocol for surgical planning of

a spine surgery and determine the accuracy of the created model.

A 6-year-old patient (male) who has a congenital scoliosis history participated to this study for obtaining raw image data. Medical scanning process was performed at the Radiology Department, Faculty of Medicine Kocaeli University, using a CT scanner (Aquillion 64, Toshiba Medical Systems, Tokyo, Japan). Ethical permission was received by the Ethics and Research Committee of Kocaeli University (reference number, KU GOKAEK 2019/204). A full spinal CT scanning was performed at 2 mm of slice thickness, 140 mm field of view (FoV) and 135 kV (40 mA, 1 s). Raw DICOM images (449 slices) were obtained from the picture archiving and communication system (PACS) server of the Radiology Department (**Figure 1(a)**). Thus, data acquisition process, the first step of the surgical planning, was completed. MIMICS software (v19) was utilized in image segmentation step, and a reference model was automatically reconstructed via MIMICS without any filters

(**Figure 1(b)**). DICOM files (449 images) were imported, and segmentation process was performed using the thresholding tool at predefined threshold set of the bone (226, Hounsfield Unit, HU as lower threshold, and 3071 HU as higher threshold). These parameters were ideal to obtain exact contour of the target model in a maximum allowable noisy form according to the raw DICOM images for this case. Segmentation step was completed after each image section was masked and high-

3D calculation function was utilized at high quality to reconstruct the 3D raw surface, and a quite noisy model was created with desired contour based on the masked images in 3D model reconstruction step (**Figure 1(d)**). Each image

**2. Material and methods**

lighted by a different color (**Figure 1(c)**).

*DOI: http://dx.doi.org/10.5772/intechopen.89950*

*Surgical Planning and Additive Manufacturing of an Anatomical Model: A Case Study… DOI: http://dx.doi.org/10.5772/intechopen.89950*

selection of the region of interest on each image during the segmentation step. Thus, the target anatomical model's contour is masked on each image section. In 3D model reconstruction step, the mask images are positioned sequentially to form a solid model in 3D workspace of the software. Tissue-specific filters or data sets may be applied to minimize noise using predefined thresholds before and after the 3D reconstruction process. The solid model is exported as a STL file and prepared to be 3D printed. In 3D printing step, the STL file is 3D printed using one of the additive manufacturing methods such as fused deposition modeling (FDM), selective laser sintering (SLS), or stereolithography (SLA). In a preoperative review step, the printed model is evaluated and used before and during the surgery (requires sterilization) as a reference model that guide the operation as well as it can be used for custom implant or assistive surgical apparatus creation such as drill guides [23]. Since surgical planning is important and plays a crucial role during the surgery, reference 3D model requires to be created with minimal geometrical errors. The accuracy of 3D reconstructed models can be analyzed by calculating the Hausdorff distance (HD) and dice similarity coefficient (DSC) values for each model [24, 25]. While DSC measures the volume overlap between two models that ranges from 0 (no overlap) to 1 (exact overlap), HD calculates the Euclidean distance between each binary volume [26, 27]. Furthermore, several methods (geometric distance, roughness, structure, saliency, and strain-energy-based calculations) may be implemented to assess visual quality and accuracy of the 3D models [28]. In this study, a spine model of a scoliosis patient is first acquired and then 3D reconstructed in MIMICS software (manual and automatic segmentation) using different filters and parameters to compare each geometrical error. The results of the DSC and HD values of each reconstructed model reveal the ideal protocol for surgical planning of a spine surgery and determine the accuracy of the created model.

#### **2. Material and methods**

*Medical Robotics - New Achievements*

old), (ii) juvenile (4–9 years old), and (iii) adolescent (10 years old to teen years) [4]. The severity of scoliosis is also determined by Cobb method that provides obtain information about the curvature of the spine in terms of degrees. According to these measurements, a scoliotic curve of the spine is defined as (i) mild scoliosis (10–25°, requires monitoring), (ii) significant scoliosis (25–40°, treated with bracing), and (iii) severe scoliosis (over 40°, requires surgery) [5]. Recently, a number of the scoliosis patients are growing up to 2–3% worldwide due to the unawareness of people [6]. It is important to predict risk of progression, diagnose at early stage for preventing degenerative effects, and contribute to the patient's quality of life. Unfortunately, in some cases, rapid progress on spine deformity or late diagnosis is resulted with surgical operations that are necessary to stabilize the spine by means of rod placement on each affected vertebra [6]. All operations require precise processes in a limited workspace because of the spinal nerves and blood vessels. Besides, an injury of a vessel around the surgical site may initiate clotting that may result with pulmonary embolism or even death [7]. Therefore, novel technologies and methods are of great importance to assist or plan the surgery in advance. Medical imaging technologies such as multidetector computed tomography (MDCT) and magnetic resonance imaging (MRI) are rapidly evolving to visualize more complex tissues of the human body [8]. Furthermore, high-resolution images provide radiologists more accurate diagnosis [9]. Although 3D visualization of the scanned tissues simplifies the surgical procedures, patients may undergo a suboptimal outcome after the treatment [10]. On the other hand, two-dimensional (2D) computer screens prevent the direct interaction with the target 3D model [11]. While virtual presurgical planning offers more precise results and overcomes mentioned drawbacks above, it is not applicable for every hospital because of its high costs [10]. Additive manufacturing, layer-by-layer fabrication of a physical object, is increasingly being used especially in the fields of medicine. Nowadays, these technologies are utilized with medical imaging to produce patient-specific (i) medical devices, orthosis, and prosthesis [12], (ii) anatomical to assist-surgical models [13], (iii) body parts [14], (iv) dental and maxillofacial implants [15], (v) blood vessels [16], and (vi) organs [17]. Typical 2D digital imaging and communications in medicine (DICOM) files from the CT or MRI images are transformed into a 3D standard tessellation language (STL) file to perform 3D printing of the target model. A wide range of polymers including glass, ceramic, metal, or biological materials may be used to create complex models in a cost-effective way compared to conventional manufacturing methods [18]. Although the latest innovative studies are focused on a functional tissue or even an organ printing, clinical bedside applications of these technologies are difficult and limited [11]. On the other hand, surgical planning or patient-specific implant production via 3D printing is available for any case and widely used to manage a complex surgery or achieve the exact placement of an implant [18]. Consequently, success rate of each surgery is significantly increased as well as it becomes minimally invasive and requires shorter

**66**

recovery owing to the preoperative review [19, 20].

Surgical planning by means of additive manufacturing consists of five basic steps, namely, (i) data acquisition, (ii) image segmentation, (iii) 3D model reconstruction, (iv) 3D printing, and (v) preoperative review [21]. In data acquisition step, a medical imaging system such as CT is used to obtain 2D DICOM images in general. The accuracy of surgical planning is first dependent on the contrast-tonoise ratio (CNR) of scanned raw images [22]. High CNR values provide detailed segmentation before the 3D reconstruction of a model. These images are then processed via a commercial (Materialize Mimics) or an open-source (InVesalius) medical image processing software using the default segmentation functions or manual

A 6-year-old patient (male) who has a congenital scoliosis history participated to this study for obtaining raw image data. Medical scanning process was performed at the Radiology Department, Faculty of Medicine Kocaeli University, using a CT scanner (Aquillion 64, Toshiba Medical Systems, Tokyo, Japan). Ethical permission was received by the Ethics and Research Committee of Kocaeli University (reference number, KU GOKAEK 2019/204). A full spinal CT scanning was performed at 2 mm of slice thickness, 140 mm field of view (FoV) and 135 kV (40 mA, 1 s). Raw DICOM images (449 slices) were obtained from the picture archiving and communication system (PACS) server of the Radiology Department (**Figure 1(a)**). Thus, data acquisition process, the first step of the surgical planning, was completed.

MIMICS software (v19) was utilized in image segmentation step, and a reference model was automatically reconstructed via MIMICS without any filters (**Figure 1(b)**). DICOM files (449 images) were imported, and segmentation process was performed using the thresholding tool at predefined threshold set of the bone (226, Hounsfield Unit, HU as lower threshold, and 3071 HU as higher threshold). These parameters were ideal to obtain exact contour of the target model in a maximum allowable noisy form according to the raw DICOM images for this case. Segmentation step was completed after each image section was masked and highlighted by a different color (**Figure 1(c)**).

3D calculation function was utilized at high quality to reconstruct the 3D raw surface, and a quite noisy model was created with desired contour based on the masked images in 3D model reconstruction step (**Figure 1(d)**). Each image

#### **Figure 1.**

*3D Surgical model segmentation of the reference model (a) An image of the 3D raw model, based on nonfiltered DICOM images in Sectra workspace, a 3D DICOM viewer of the default system software that does not allow 3D model exporting or processing, (b) Importing non-filtered DICOM images in MIMICS, (c) Segmentation of each image and obtaining the exact contour of target model in a maximum allowable noisy form in MIMICS, (d) 3D reconstruction of the raw DICOM images in a noisy form via MIMICS, (e) Manual noise cleaning in 3-Matic, (f) Manual surface reconstruction and fixing the geometrical errors of 3D raw model in 3-Matic, (g) Fixing the sharp cornered bonded surfaces in Meshmixer.*

was positioned sequentially to form a 3D raw model of the spine. Since there was noise in the workspace and model's surface, the 3D raw model was directly exported to 3-matic software (v11, Materialize) for manual surface reconstruction and noise elimination (**Figure 1(e)**). The surface geometry of the model was manually revised, and the noise was manually cleared via 3-matic software according to the raw image geometries in MIMICS. Polygon area mark tool under the Mark—Area Mark menu was used to select each noisy surface and subsequently deleted. The gaps of the deleted surfaces were marked as bad contours on the 3D raw model. The bad contours were then selected and fixed, respectively, using the Fill Hole Freeform tool under the Fix menu. The Fill Hole Freeform process was performed at medium triangulation quality and created in tangent form. After manual surface reconstruction, the Fix Wizard tool under the Fix menu was utilized to fix geometrical normals, stitching, noise shells, holes, triangles, overlaps, and shells on the model. The errors were automatically fixed by clicking Follow Advice, Apply, and Update buttons, respectively, for each option (**Figure 1(f )**). Finally, the patient ID was added using the Quick Label tool under the Finish menu. Thus, 3D-reconstructed reference surgical model was obtained and exported as STL file by clicking File, Export, and STL buttons, respectively. The reference model was exported at binary format and one scaling factor. Some sharp cornered bonded surfaces that occurred by Fix Wizard tool during the fixing process were also revised in Meshmixer (v3.4.35, Autodesk) manually to obtain exact geometry of target anatomical model. Robust Smooth tool under the Sculp – Brushes menu was used (brush parameters, 10 strength, 10 size, 0 depth, and 0 laziness) to fix sharp surfaces (**Figure 1(g)**). The 3D model reconstruction step was completed after the reference model has taken its final shape (48 hours of work). 3D reconstruction of the reference surgical model is illustrated step by step in **Figure 1**.

Test models were separated into three groups, namely, (i) preprocessed, (ii) post-processed, and (iii) fully processed models. Preprocessed models were obtained using the filtered DICOM images before the segmentation step without any post-process in MIMICS. Post-processed models were created from the raw DICOM images and only processed via 3-matic after the model was

**69**

*Surgical Planning and Additive Manufacturing of an Anatomical Model: A Case Study…*

reconstructed (**Figure 2(a)**). Fully processed models were formed with a combination of both processes. Five different image filters of MIMICS, (i) binomial blur (**Figure 2(b)**), (ii) curvature flow (**Figure 2(c)**), (iii) discrete Gaussian (**Figure 2(d)**), (iv) mean (**Figure 2(e)**), and (v) median (**Figure 2(f )**), were applied on the raw DICOM images, respectively, and then the segmentation step was performed separately for each sample on preprocessed models. Two different 3D tools of 3-matic, (i) Smooth (**Figure 2(g)**) and (ii) wrap (**Figure 2(h)**), were utilized separately for each sample in post-processing case. Preprocessing, post-processing, and full-processing parameters of the 3D reconstructed surgical

*Reference model and each generated 3D test model with a local mesh view (a) Reference model, (b) Binomial blur filter in MIMICS, (c) Curvature flow filter in MIMICS, (d) Discrete gaussian filter in MIMICS, (e) Mean filter in MIMICS, (f) Median filter in MIMICS, (g) Smooth function in 3-Matic and (h) Wrap*

Before the calculation of HD and DSC values by CloudCompare software, each spine model was processed via 3-matic software that was necessary to determine the spine regions equally on each sample model and obtain more correct results in accuracy assessment. Therefore, the spine sections of each model were extracted manually and exported using the default options. All test models were imported into the CloudCompare software (v2.11, Open Source) and then compared with the reference model in terms of the HD and DSC results [29]. The best results of the preprocessing and the post-processing cases were combined and applied together to form the fully processed model. Finally, the fully processed model was compared to the reference model to reveal the ideal solution for 3D surgical model reconstruction. Each generated test model is

HD of the test models was calculated by means of importing and analyzing the 3D models, the reference model and one of the test models at the same time, in CloudCompare workspace. Both models were then aligned using Registration Match bounding function—under the Tools menu. This process is required to align the box centers (volume frames) of models before performing any similarity function in order to obtain accurate results. Fine registration function under the Tools— Registration menu was applied on each model to highlight the difference of point clouds without any scale adjustment. The fine registration function also applies a rotation to compared model (generates a rotation matrix with a theoretical overlap value) to provide an ideal Overlap in volumes of the both reference and compared test model. Finally, HD value (mean distance) was computed via Distances—Cloud/ Mesh Dist under the Tools menu. DSC values were calculated similarly via importing

*DOI: http://dx.doi.org/10.5772/intechopen.89950*

models are given in **Table 1**.

**Figure 2.**

*function in 3-Matic.*

illustrated in **Figure 2**.

*Surgical Planning and Additive Manufacturing of an Anatomical Model: A Case Study… DOI: http://dx.doi.org/10.5772/intechopen.89950*

#### **Figure 2.**

*Medical Robotics - New Achievements*

**Figure 1.**

was positioned sequentially to form a 3D raw model of the spine. Since there was noise in the workspace and model's surface, the 3D raw model was directly exported to 3-matic software (v11, Materialize) for manual surface reconstruction and noise elimination (**Figure 1(e)**). The surface geometry of the model was manually revised, and the noise was manually cleared via 3-matic software according to the raw image geometries in MIMICS. Polygon area mark tool under the Mark—Area Mark menu was used to select each noisy surface and subsequently deleted. The gaps of the deleted surfaces were marked as bad contours on the 3D raw model. The bad contours were then selected and fixed, respectively, using the Fill Hole Freeform tool under the Fix menu. The Fill Hole Freeform process was performed at medium triangulation quality and created in tangent form. After manual surface reconstruction, the Fix Wizard tool under the Fix menu was utilized to fix geometrical normals, stitching, noise shells, holes, triangles, overlaps, and shells on the model. The errors were automatically fixed by clicking Follow Advice, Apply, and Update buttons, respectively, for each option (**Figure 1(f )**). Finally, the patient ID was added using the Quick Label tool under the Finish menu. Thus, 3D-reconstructed reference surgical model was obtained and exported as STL file by clicking File, Export, and STL buttons, respectively. The reference model was exported at binary format and one scaling factor. Some sharp cornered bonded surfaces that occurred by Fix Wizard tool during the fixing process were also revised in Meshmixer (v3.4.35, Autodesk) manually to obtain exact geometry of target anatomical model. Robust Smooth tool under the Sculp – Brushes menu was used (brush parameters, 10 strength, 10 size, 0 depth, and 0 laziness) to fix sharp surfaces (**Figure 1(g)**). The 3D model reconstruction step was completed after the reference model has taken its final shape (48 hours of work). 3D reconstruction of the reference surgical model is illustrated step by

*3D Surgical model segmentation of the reference model (a) An image of the 3D raw model, based on nonfiltered DICOM images in Sectra workspace, a 3D DICOM viewer of the default system software that does not*

*(c) Segmentation of each image and obtaining the exact contour of target model in a maximum allowable noisy form in MIMICS, (d) 3D reconstruction of the raw DICOM images in a noisy form via MIMICS, (e) Manual noise cleaning in 3-Matic, (f) Manual surface reconstruction and fixing the geometrical errors of 3D raw*

*allow 3D model exporting or processing, (b) Importing non-filtered DICOM images in MIMICS,*

*model in 3-Matic, (g) Fixing the sharp cornered bonded surfaces in Meshmixer.*

Test models were separated into three groups, namely, (i) preprocessed, (ii) post-processed, and (iii) fully processed models. Preprocessed models were obtained using the filtered DICOM images before the segmentation step without any post-process in MIMICS. Post-processed models were created from the raw DICOM images and only processed via 3-matic after the model was

**68**

step in **Figure 1**.

*Reference model and each generated 3D test model with a local mesh view (a) Reference model, (b) Binomial blur filter in MIMICS, (c) Curvature flow filter in MIMICS, (d) Discrete gaussian filter in MIMICS, (e) Mean filter in MIMICS, (f) Median filter in MIMICS, (g) Smooth function in 3-Matic and (h) Wrap function in 3-Matic.*

reconstructed (**Figure 2(a)**). Fully processed models were formed with a combination of both processes. Five different image filters of MIMICS, (i) binomial blur (**Figure 2(b)**), (ii) curvature flow (**Figure 2(c)**), (iii) discrete Gaussian (**Figure 2(d)**), (iv) mean (**Figure 2(e)**), and (v) median (**Figure 2(f )**), were applied on the raw DICOM images, respectively, and then the segmentation step was performed separately for each sample on preprocessed models. Two different 3D tools of 3-matic, (i) Smooth (**Figure 2(g)**) and (ii) wrap (**Figure 2(h)**), were utilized separately for each sample in post-processing case. Preprocessing, post-processing, and full-processing parameters of the 3D reconstructed surgical models are given in **Table 1**.

Before the calculation of HD and DSC values by CloudCompare software, each spine model was processed via 3-matic software that was necessary to determine the spine regions equally on each sample model and obtain more correct results in accuracy assessment. Therefore, the spine sections of each model were extracted manually and exported using the default options. All test models were imported into the CloudCompare software (v2.11, Open Source) and then compared with the reference model in terms of the HD and DSC results [29]. The best results of the preprocessing and the post-processing cases were combined and applied together to form the fully processed model. Finally, the fully processed model was compared to the reference model to reveal the ideal solution for 3D surgical model reconstruction. Each generated test model is illustrated in **Figure 2**.

HD of the test models was calculated by means of importing and analyzing the 3D models, the reference model and one of the test models at the same time, in CloudCompare workspace. Both models were then aligned using Registration Match bounding function—under the Tools menu. This process is required to align the box centers (volume frames) of models before performing any similarity function in order to obtain accurate results. Fine registration function under the Tools— Registration menu was applied on each model to highlight the difference of point clouds without any scale adjustment. The fine registration function also applies a rotation to compared model (generates a rotation matrix with a theoretical overlap value) to provide an ideal Overlap in volumes of the both reference and compared test model. Finally, HD value (mean distance) was computed via Distances—Cloud/ Mesh Dist under the Tools menu. DSC values were calculated similarly via importing

#### **Table 1.**

*Parameters of each applied filters and tools.*

the reference model and a test model into the 3D workspace of CloudCompare software. The match bounding and fine registration steps were also performed to obtain perfect alignment. After the alignment process, a plugin named as Cork, under the Plugins menu, was utilized to obtain the intersection of both models in terms of volume (cube units). Each volume was also measured by means of the mesh measure volume function under the Edit menu. The DSC value of each model was then calculated to obtain overlapped volume between the two models according to the formula given below (α = total volume of the reference model, β = total volume of the compared model, and α ∩ β = intersection of both models in terms of volume): DSC (α, <sup>β</sup>) = \_

$$\text{DSC } \left( \alpha, \beta \right) = \frac{2 \left< \alpha \cap \beta \right>}{\alpha \star \beta} \tag{1}$$

**71**

**Figure 4.**

*Surgical Planning and Additive Manufacturing of an Anatomical Model: A Case Study…*

**Model volume (cube units)**

1 Binomial blur Pre 80393.9 9.502507 1.083704 0.547654 2 Curvature flow Pre 81445.2 8.31908 1.107846 0.923381

 Mean Pre 82308.1 7.347738 1.041506 0.913479 Median Pre 79867.7 10.09483 1.043368 0.911775 Smooth Post 90480.1 −1.851286 1.185564 0.937718 Wrap Post 113,092 −27.30496 0.461971 0.871986 Mean + Smooth Together 82231.4 7.434077 1.041154 0.916657

*Results of each applied filters and tools (DSC value: 0 = no overlap, 1 = exact overlap, negative max error values indicate a larger volume than the reference model while positive values mean a lower volume according*

*The intersection of reference model and each generated 3D test model with a local view (a) Reference model and Binomial blur pre-filtered test model, (b) Reference model and Curvature flow pre-filtered test model, (c) Reference model and Discrete gaussian pre-filtered test model, (d) Reference model and Mean pre-filtered test model, (e) Reference model and Median pre-filtered test model,( f) Reference model and Smooth post-filtered*

*test model and (g) Reference model and Wrap post-filtered test model.*

*Results of each produced model. (a) Reference model, (b) Mean filter and (c) Smooth tool.*

**Max error (%) HD (mm) DSC (0–1)**

Pre 81787.5 7.933765 1.034391 0.911621

**type**

*DOI: http://dx.doi.org/10.5772/intechopen.89950*

**HD and DSC results of each test model No. Filter-tool name Filter**

3 Discrete

**Table 2.**

**Figure 3.**

Gaussian

Reference model volume (cube units): 88835.5

*to the reference model in terms of cube units).*

#### **3. Results**

Wrap tool, binomial blur, and curvature flow filters produce high geometrical errors, while mean filter produces the lowest geometrical error. Furthermore, HD and DSC results of the curvature flow and discrete Gaussian filters are close to mean filter. Smooth tool and mean filter produce almost the same volume of the reference model. However, binomial blur filter and wrap tool generate unacceptably different volumes. Moreover, the DSC results of the both mentioned functions are not overlapping properly. Each result is illustrated in **Table 2**.

Smooth and wrap tools generate undesired mesh structures caused by the noise during the segmentation step in MIMICS. HD results of each test model are illustrated in **Figure 3**.

*Surgical Planning and Additive Manufacturing of an Anatomical Model: A Case Study… DOI: http://dx.doi.org/10.5772/intechopen.89950*

#### **Table 2.**

*Medical Robotics - New Achievements*

**Pre-processing parameters of applied filters in MIMICS**

**Post-processing parameters of applied tools in 3-matic**

**Full-processing parameters of both applied filter and tool**

the reference model and a test model into the 3D workspace of CloudCompare software. The match bounding and fine registration steps were also performed to obtain perfect alignment. After the alignment process, a plugin named as Cork, under the Plugins menu, was utilized to obtain the intersection of both models in terms of volume (cube units). Each volume was also measured by means of the mesh measure volume function under the Edit menu. The DSC value of each model was then calculated to obtain overlapped volume between the two models according to the formula given below (α = total volume of the reference model, β = total volume of the compared model, and α ∩ β = intersection of both models in terms of volume): DSC (α, <sup>β</sup>) = \_

**Model no. Filter name Parameter Value**

2 Curvature flow Time step 0.5

4 \*Mean Filter radius 3 5 Median Filter radius 3

6 \*Smooth Smooth factor 1 7 Wrap Gap closing 0.05

8 \*Mean with \*Smooth together Filter radius 3

1 Binomial blur Number of

3 Discrete Gaussian Gaussian

Wrap tool, binomial blur, and curvature flow filters produce high geometrical errors, while mean filter produces the lowest geometrical error. Furthermore, HD and DSC results of the curvature flow and discrete Gaussian filters are close to mean filter. Smooth tool and mean filter produce almost the same volume of the reference model. However, binomial blur filter and wrap tool generate unacceptably different volumes. Moreover, the DSC results of the both mentioned functions are

Smooth and wrap tools generate undesired mesh structures caused by the noise during the segmentation step in MIMICS. HD results of each test model are illus-

not overlapping properly. Each result is illustrated in **Table 2**.

2 (α ∩ β)

<sup>α</sup> <sup>+</sup> <sup>β</sup> (1)

iterations

Number of iterations

variance

Max kernel width

Smallest detail 0.5

Smooth factor 0.5

5

5

5

5

**70**

trated in **Figure 3**.

**3. Results**

*(\* = best similarity results).*

*Parameters of each applied filters and tools.*

**Table 1.**

*Results of each applied filters and tools (DSC value: 0 = no overlap, 1 = exact overlap, negative max error values indicate a larger volume than the reference model while positive values mean a lower volume according to the reference model in terms of cube units).*

#### **Figure 3.**

*The intersection of reference model and each generated 3D test model with a local view (a) Reference model and Binomial blur pre-filtered test model, (b) Reference model and Curvature flow pre-filtered test model, (c) Reference model and Discrete gaussian pre-filtered test model, (d) Reference model and Mean pre-filtered test model, (e) Reference model and Median pre-filtered test model,( f) Reference model and Smooth post-filtered test model and (g) Reference model and Wrap post-filtered test model.*

**Figure 4.** *Results of each produced model. (a) Reference model, (b) Mean filter and (c) Smooth tool.*

The results of the last case given in **Table 2** (mean filter with Smooth tool together) show that increased Smooth provides to obtain better HD and DSC results when compared to the fourth test case (mean filter only). However, the max error is proportionally increased with the Smooth tool. Reference model, mean filter, and Smooth tool results are illustrated in **Figure 4**.

#### **4. Discussion**

According to the results, binomial blur filter, median filter, and wrap tool are not suitable for surgical planning of a spine model due to the high error percentage in volume. These functions produce misleading information when used alone and may be used with another filter together at lower parametric values. Although Smooth tool provides better DSC result than the curvature flow filter, mesh surface improvement should not be performed by means of this tool only because of the undesired mesh structures caused by the noise. Therefore, a prefilter-like curvature flow, discrete Gaussian, or mean should to be applied on raw DICOM images to filter noise more accurately during the segmentation step, and Smooth tool should to be used at lower values after 3D model reconstruction process. It should be noted that some geometrical errors may not be fixed via 3-matic software and may require a third party software such as Geomagic Design X or Autodesk Meshmixer. On the other hand, fractured or unbounded bone structures may be lost during the noise filtering process. Therefore, some functions such as largest surface selection in MIMICS should not be used in complex cases, and target model should be cleared manually after the 3D reconstruction process. Mesh distribution of the models require to be uniform to perform a healthy DSC calculation. Both reference and compared test models may be processed via optimize mesh tool in Geomagic Design X to achieve uniform mesh distribution on each model.

While software optimizations may be performed to obtain an ideal protocol, improvements may also be applied on used CT device at the beginning for more correct presurgical planning. Devices with more advanced detector structure, which could provide to operate at lower voxels and have high beam quality, may reduce noise on raw data images. For example, it is known that dual energy computed tomography (DECT) is more suitable for tissue segmentation than single-energy computed tomography (SECT) [30]. Besides, some known sources of noise (beam hardening, partial volume effect, etc.) can be reduced by making some adjustments before imaging such as in monochromatic X-ray, reduced beam hardening effect and thin section thickness, and reduced the partial volume effect, which are very important for spine imaging [31, 32]. Additionally, using higher tube current provides sharper image because of the amount of beam delivery to the target tissue. However, high current may not be applied in all cases because of the increased radiation dose given to the patient.

#### **5. Conclusion**

In this study, an ideal protocol for surgical planning of a spine surgery is defined with measurable accuracy. Thus, success rate of a spine surgery may be increased especially for the severe cases owing to the more accurate preoperative review.

**73**

**Author details**

, Ozgur Cakir2

provided the original work is properly cited.

, Riza Dilek<sup>3</sup>

1 Biomedical Device Technology, Istanbul Gedik University, Istanbul, Turkey

2 Department of Radiology, Faculty of Medicine, Kocaeli University, Kocaeli,

3 Medical Imaging Techniques, Istanbul Gedik University, Istanbul, Turkey

© 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>
and Mucahit Ege1

\*

Levent Aydin1

Turkey

*Surgical Planning and Additive Manufacturing of an Anatomical Model: A Case Study…*

*DOI: http://dx.doi.org/10.5772/intechopen.89950*

#### **Acknowledgements**

We declare that we have no acknowledgments.

*Surgical Planning and Additive Manufacturing of an Anatomical Model: A Case Study… DOI: http://dx.doi.org/10.5772/intechopen.89950*

#### **Author details**

*Medical Robotics - New Achievements*

**4. Discussion**

Smooth tool results are illustrated in **Figure 4**.

X to achieve uniform mesh distribution on each model.

We declare that we have no acknowledgments.

The results of the last case given in **Table 2** (mean filter with Smooth tool together) show that increased Smooth provides to obtain better HD and DSC results when compared to the fourth test case (mean filter only). However, the max error is proportionally increased with the Smooth tool. Reference model, mean filter, and

According to the results, binomial blur filter, median filter, and wrap tool are not suitable for surgical planning of a spine model due to the high error percentage in volume. These functions produce misleading information when used alone and may be used with another filter together at lower parametric values. Although Smooth tool provides better DSC result than the curvature flow filter, mesh surface improvement should not be performed by means of this tool only because of the undesired mesh structures caused by the noise. Therefore, a prefilter-like curvature flow, discrete Gaussian, or mean should to be applied on raw DICOM images to filter noise more accurately during the segmentation step, and Smooth tool should to be used at lower values after 3D model reconstruction process. It should be noted that some geometrical errors may not be fixed via 3-matic software and may require a third party software such as Geomagic Design X or Autodesk Meshmixer. On the other hand, fractured or unbounded bone structures may be lost during the noise filtering process. Therefore, some functions such as largest surface selection in MIMICS should not be used in complex cases, and target model should be cleared manually after the 3D reconstruction process. Mesh distribution of the models require to be uniform to perform a healthy DSC calculation. Both reference and compared test models may be processed via optimize mesh tool in Geomagic Design

While software optimizations may be performed to obtain an ideal protocol, improvements may also be applied on used CT device at the beginning for more correct presurgical planning. Devices with more advanced detector structure, which could provide to operate at lower voxels and have high beam quality, may reduce noise on raw data images. For example, it is known that dual energy computed tomography (DECT) is more suitable for tissue segmentation than single-energy computed tomography (SECT) [30]. Besides, some known sources of noise (beam hardening, partial volume effect, etc.) can be reduced by making some adjustments before imaging such as in monochromatic X-ray, reduced beam hardening effect and thin section thickness, and reduced the partial volume effect, which are very important for spine imaging [31, 32]. Additionally, using higher tube current provides sharper image because of the amount of beam delivery to the target tissue. However, high current may not be applied in all cases because of the increased radiation dose given to the patient.

In this study, an ideal protocol for surgical planning of a spine surgery is defined with measurable accuracy. Thus, success rate of a spine surgery may be increased especially for the severe cases owing to the more accurate preoperative review.

**72**

**5. Conclusion**

**Acknowledgements**

Levent Aydin1 , Ozgur Cakir2 , Riza Dilek<sup>3</sup> and Mucahit Ege1 \*

1 Biomedical Device Technology, Istanbul Gedik University, Istanbul, Turkey

2 Department of Radiology, Faculty of Medicine, Kocaeli University, Kocaeli, Turkey

3 Medical Imaging Techniques, Istanbul Gedik University, Istanbul, Turkey

\*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.

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[19] Samset E, Schmalstieg D, Vander Sloten J, et al. Augmented Reality in Surgical Procedures. Human Vision and Electronic Imaging XIII ; 2008. p. 68060K

[20] Modabber A, Ayoub N, Bock A, et al. Medial approach for minimallyinvasive harvesting of a deep circumflex iliac artery flap for reconstruction of the jaw using virtual surgical planning and cad/cam technology. British Journal of Oral and Maxillofacial Surgery. 2017;**55**(9):946-951

[21] Guo HC, Wang Y, Dai J, et al. Application of 3d printing in the surgical planning of hypertrophic obstructive cardiomyopathy and physician-patient communication: A preliminary study. Journal of Thoracic Disease. 2018;**10**(2):867

[22] Starosolski ZA, Kan JH, Rosenfeld SD, et al. Application of 3-d printing (rapid prototyping) for creating physical models of pediatric orthopedic disorders. Pediatric Radiology. 2014;**44**(2):216-221

[23] Flügge TV, Nelson K, Schmelzeisen R, et al. Threedimensional plotting and printing of an implant drilling guide: Simplifying guided implant surgery. Journal of Oral and Maxillofacial Surgery. 2013;**71**(8):1340-1346

[24] Egger J, Kapur T, Fedorov A, et al. GBM volumetry using the 3d slicer medical image computing platform. Scientific Reports. 2013;**3**:1364

[25] Egger J, Gall M, Tax A, et al. Interactive reconstructions of cranial 3d implants under mevislab as an alternative to commercial planning software. PLoS One. 2017;**12**(3):e0172694

[26] Deeley MA, Chen A, Datteri R, et al. Comparison of manual and automatic segmentation methods for brain structures in the presence of

space-occupying lesions: A multi-expert study. Physics in Medicine & Biology. 2011;**56**(14):4557

[27] Taha AA, Hanbury A. An efficient algorithm for calculating the exact hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015;**37**(11):2153-2163

[28] Bulbul A, Capin T, Lavoué G, et al. Assessing visual quality of 3-d polygonal models. IEEE Signal Processing Magazine. 2011;**28**(6):80-90

[29] Cloud Compare. 2019. Available from: https://www.cloudcompare.org/ doc/qCC/CloudCompare%20v2.6.1%20 -%20User%20manual.pdf [Accessed: June 20, 2019]

[30] Nora H. Dual energy CT as an alternative for ion radiotheraphy treatment planning [doctoral dissertation]. Germany: Combined Faculties for the Natural Sciences and for Mathematics of the Ruperto-Carola University of Heidelberg; 2014

[31] Torikoshi M, Tsunoo T, Sasaki M. Electron density measurement with dual-energy x-ray ct using synchrotron radiation. Physics in Medicine & Biology. 2003;**48**(5):673-685

[32] Kemerink GJ. The nonlinear partial volume effect and computed tomography densitometry of foam and lung. Medical Physics. 1995;**22**(9):1445-1450

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*Medical Robotics - New Achievements*

[2] Scoliosis—Better Health Channel. 2019. Available from: betterhealth.vic.gov.au/health/ ConditionsAndTreatments/

[3] Miller MD, Thompson SR,

Health Sciences; 2012. p. 1

June 20, 2019]

**References**

[1] Choudhry MN, Ahmad Z, Verma R. Adolescent idiopathic scoliosis. The Open Orthopaedics Journal. 2016;**10**:143 International Journal of Oral and Maxillofacial Surgery. 2017;**46**:46

[12] Aydin L, Kucuk S. A method for more accurate fea results on a medical device developed by 3d technologies. Polymers for Advanced Technologies.

[13] Kurenov SN, Ionita C, Sammons D, et al. Three-dimensional printing to facilitate anatomic study, device development, simulation, and planning in thoracic surgery. The Journal of Thoracic and Cardiovascular Surgery.

[11] Chae MP, Rozen WM, McMenamin PG, et al. Emerging applications of bedside 3d printing in plastic surgery. Frontiers in Surgery.

2018;**29**(8):2281-2286

2015;**149**(4):973-979

[14] Wallace GG, Cornock RC, O'Connell CD, et al. 3d Bioprinting: Printing Parts for Bodies. Vol. 1566. ARC Centre of Excellence for Electromaterials Science; 2014. p. 1.

ISBN 978-0-646-92867-8

[15] Dawood A, Marti BM,

2015;**219**(11):521

2017;**124**:106-115

2016;**86**(9):648-653

Sauret-Jackson V, et al. 3d printing in dentistry. British Dental Journal.

[16] Zhu W, Qu X, Zhu J, et al. Direct 3d bioprinting of prevascularized tissue constructs with complex microarchitecture. Biomaterials.

[17] Murphy SV, Atala A. 3d bioprinting

applications. ANZ Journal of Surgery.

of tissues and organs. Nature Biotechnology. 2014;**32**(8):773

[18] Mulford JS, Babazadeh S, Mackay N. Three-dimensional printing in orthopaedic surgery: Review of current and future

2015;**2**:25

scoliosis?viewAsPdf=true [Accessed:

Hart J. Review of orthopaedics. Elsevier

[4] Treating Scoliosis. 2019. Available from: https://www.treatingscoliosis. com/infographics/scoliosis-by-thenumbers/ [Accessed: June 20, 2019]

[5] Napierkowski DB. Scoliosis: A case study in an adolescent boy. Orthopaedic

Gerdhem P. Genetics and pathogenesis of idiopathic scoliosis. Scoliosis and Spinal Disorders. 2016;**11**(1):45

[7] University of Maryland School of Medicine. 2019. Available from: https:// www.umms.org/ummc/health-services/ orthopedics/services/spine/patientguides/complications-spine-surgery

[8] Rengier F, Mehndiratta A, Von Tengg-Kobligk H, et al. 3d printing based on imaging data: Review of medical applications. International Journal of Computer Assisted Radiology

and Surgery. 2010;**5**(4):335-341

[10] Palau JR. Three-dimensional planning in craniomaxillofacial surgery.

2007;**25**(1):38-44

[9] Kido T, Kurata A, Higashino H, et al. Cardiac imaging using 256-detector row four-dimensional CT: Preliminary clinical report. Radiation Medicine.

Nursing. 2007;**26**(3):147-155

[6] Grauers A, Einarsdottir E,

[Accessed: June 20, 2019]

**77**

Section 5

Robotic Urologic Surgery

Section 5


**6. Conclusions**

In this article, a joint optimization problem of task offloading and resource allocation based on privacy protection for smart city is formulated to minimize the energy consumption of all IoT devices. First, the deep reinforcement learning algorithm based on DDPG framework is proposed to solve the mixed-integer nonlinear programming problem. Then, in order to protect user privacy and improve training performance, the federated learning is introduced into the DDPG framework. To this end, the two-timescale FL-DDPG algorithm is proposed to optimize the above problem. Specifically, the small timescale is to train the DDPG network and the large timescale is to aggregate the parameters of DDPG network. In this way, the privacy of users is not only protected, but also the performance of the algorithm is improved. We provide numerical simulation results in terms of the convergence property, reward, and energy consumption, which shows that our proposed algorithm has better performance.

**Author Contributions:** X.C. performed the literature reading, algorithm coding and experiment. G.L. provided guidance, strcturing and proofreading of the article. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by Shaanxi Key R&D Program (NO.2018ZDCXL-GY-04-03-02).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data available on request from the authors.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**

The following abbreviations are used in this manuscript:

#### **References**