pattern, depending on the position of the patient in the ‘phase-frequency space’. zero line and star artifacts are due to system noise or any cause of rf pollution within the room (faraday cage). if this pattern persists, check for sources of system noise such as bad electronics or alternating current line noise, loose connections to surface coils, or any source of rf pollution. if a star pattern is encountered, the manufacturer needs to readjust the system software so that the image is moved off the zero point. zipper artifacts although less common, zippers are bands through the image centre due to an imperfect faraday cage, with rf pollution in, but originating from outside, the cage. residual free induction decay stimulated echo also causes zippers. bounce point artifact fig. 10. surface coil artifact: high signal at anterior thoracic wall adjacent to surface coil. absence of signal from tissues of a particular t1 value is a consequence of magnitude sensitive reconstruction in inversion recovery imaging. when the chosen t1 equals 69% of the t1 value of a particular tissue, a bounce point artifact occurs. use phase-sensitive reconstruction inversion recovery techniques. surface coil artifacts close to the surface coil the signals are very strong resulting in a very intense image signal (fig. 10). further from the coil the signal strength drops rapidly due to the attenuation with a loss of image brightness and significant shading to the uniformity. surface coil sensitivity intensifies problems related to rf attenuation and rf mismatching. slice-to-slice interference fig. 11. slice-to-slice interference (t1 axial study of lumbar vertebrae). non-uniform rf energy received by adjacent slices during a multi-slice acquisition is due to cross-excitation of adjacent slices with contrast loss in reconstructed images (fig. 11). to overcome these interference artifacts, the acquisition of two independent sets of gapped multi-slice images need to be included, and subsequently reordered during display of the full image set. artifact correction gating gating, also known as triggering, is a technique that acquires mri data at a low motion state. an example of this could be acquiring an mri slice only when the lung capacity is low (i.e. between large breaths). gating is a very simple solution that can have a very large result. gating is best suited for mitigating breathing and cardiac artifacts. this is because these types of motion are repetitive, so we can leverage triggering acquisitions in a ‘low motion state’. gating is used for cine imaging, mra, free-breathing chest scans, csf flow imaging, and more. in order to gate correctly, the system needs to have knowledge of the patient’s cardiac motion and breathing pattern. this is commonly done by using a pulse oximeter or ekg sensor to read a cardiac signal and/or a bellows to read the breathing signal. a big disadvantage to gating is ‘dead time’, defined as time wasted due to waiting for a high motion state to pass. for example, we do not want to acquire an mri image while someone is in the process of inhaling, since this would be a high motion state. so, we have many time periods where we are waiting for a high motion state to pass. this is even more prominent when we consider respiratory and cardiac gating together. the windows of time where the respiratory and cardiac motions are low are very infrequent, leading to high dead times. however, the advantage is that images acquired with both cardiac and respiratory gating have a significant improvement in image quality. pilot tone the pilot tone method involves turning on a constant rf frequency to detect patient motion. more specifically, the mri machine will detect the pilot tone signal when acquiring an image. the strength of the pilot tone signal at every tr will be proportional to the breathing/motion patterns of the patient. that is, the patient’s movements will cause the received constant rf tone to be amplitude modulated. a very large advantage to the pilot tone is that it requires no contact with the patient. extracting a breathing signal using a pilot tone is simple in theory: one must place a constant frequency signal near the mri bore, acquire an image, and take an fft along the readout direction to extract the pilot tone. technical considerations include choosing the rf frequency. the pilot tone must be detectable by the mri machine, however must be carefully chosen not to interfere with the mri image. the pilot tone shows up as a zipper (for a cartesian acquisition). the location of that line is determined by the frequency of the rf tone. for this reason, pilot tone acquisitions usually have slightly large fovs, to make room for the pilot tone. once an image has been acquired, the pilot tone signal can be extracted by taking the fft along the readout direction and plotting the amplitude of the resulting signal. the pilot tone will show up as a line (of varying amplitude) when taking an fft along the readout direction. the pilot tone method can also be used prospectively to acquire cardiac images. the pilot tone method is great for detecting respiratory motion artifacts. this is because there is a very large and distinct modulation due to human breathing patterns. heart signals are much more subtle and difficult to detect using a pilot tone. retrospective techniques using the pilot tone are able to increase the level of detail and reduce blurring in free-breathing radial images. tamer targeted motion estimation and reduction (tamer) is a retrospective motion correction method developed by melissa haskell, stephen cauley, and lawrence wald. the method was first introduced in their paper targeted motion estimation and reduction (tamer): consistency based motion mitigation for mri using a reduced model joint optimization, as part of the ieee transactions on medical imaging journal. the method corrects motion-related artifacts by acquiring a joint estimation of the desired motion-free image and the associated motion trajectory by minimizing the data consistency error of a sense forward model that includes rigid-body subject motion. preliminaries the tamer method utilizes the sense forward model (described below) that has been modified to include the effects of motion in a 2d multi-shot imaging sequence. note: the following modified sense model is described in detail in melissa haskell's doctoral dissertation, retrospective motion correction for magnetic resonance imaging. suppose that we have c coils. let x be a n n s h × 1 \times 1} column vector of image voxel values where n is the number of k space samples acquired per shot and let s be the n n s h c × 1 c\times 1} signal data from c coils. let e θ be an n n s h c × n n s h nn_c\times nn_ encoding matrix for a given m × 1 patient motion trajectory vector, θ . e θ is composed of n s h many n c × n n s h sub-matrices e θ i = u i f c t x y , i t z , i r i =u_fct_t_r_ (encoding matrices for each shot i ). for each shot i , we have the sub-matrix e θ i = u i f c t x y , i t z , i r i =u_fct_t_r_ which is the encoding matrix for that particular shot i where: u i is the n c × n n s h c c} under-sampling operator f is the fourier encoding operator t x y , i is the in-place translation operator t z , i is the through-plane translation operator r i is the rotation operator sense motion forward model: s = e x sense model extended to describe a 2d multi-shot imaging sequence: s = e θ x where e θ = ( e θ 1 e θ 2 . . . e θ n s h ) and e θ i = u i f c t x y , i t z , i r i for shot i = 1 , 2 , . . . , n s h xe_=e_\\e_\\.\\.\\.\\e_\ende_=u_fct_t_r_i=1,2,...,n_ the rigid-body motion forward model is nonlinear and the process of solving for estimations of both the motion trajectory and the image volume is computationally challenging and time-consuming. in the effort to speed up and simplify computations, the tamer method separates the vector of image voxel values, x , into a vector of target voxel values, x t , and a vector of fixed voxels, x f . given any choice of target voxels and fixed voxels, we have the following: x = ( x t x f ) s = s t + s f = e θ ( t ) x t + e θ ( f ) x f +s_=e_(t)x_+e_(f)x_ s t = s − e θ ( f ) x f = e θ ( t ) x t =s-e_(f)x_=e_(t)x_ note: the length of x t only makes up about 5% of the total length of x . now the optimization can be reduced to fitting the signal contribution of the target voxels to the correct target voxel values x t and the correct motion, θ . tamer algorithm the tamer algorithm has 3 main stages: initialization, jumpstart of motion parameter search, and the joint optimization reduced model search. initialization: the first stage of the tamer algorithm acquires the initial reconstruction of the full image volume, x 0 , by assuming that all motion parameters are zero. one can solve for x 0 by minimizing the least squared error of the sense forward model without motion i.e. solve the system ( e 0 h e 0 ) x 0 = e 0 h s ^e_)x_=e_^s} where e 0 = u f c =ufc} and e 0 h ^ is the conjugate transpose of e 0 . we have discussed the notion of separating the sense model into s = s t + s f = e θ ( t ) x t + e θ ( f ) x f +s_=e_(t)x_+e_(f)x_ ; however, we haven't yet discussed how the target voxels are chosen. voxels that are strongly coupled together indicate motion. in a motion-free cartesian acquisition, each voxel would only be coupled to itself, so our goal is to essentially un-couple these voxels. as described in the paper targeted motion estimation and reduction (tamer): consistency based motion mitigation for mri using a reduced model joint optimization, as part of the ieee transactions on medical imaging journal, the tamer algorithm converges fastest when choosing target voxels that are highly coupled. the target voxels can be entirely determined by the sequence parameters and coil sensitivities. target voxel selection process: group coils based on artifact properties. the model error is first computed assuming no motion. the model correlation is then computed across all channels. tamer is applied to groups of coils with the largest correlation artifacts to attain the motion and image estimation. the initial target voxels are selected by first choosing a root voxel (generally the center of the image). once the root voxel is chosen, the correlation between the root voxel and all other voxels is determined by attaining the column vector of the correlation matrix e h e e} corresponding to the root voxel. the magnitude of the entries in this column vector represent the strength of interaction between the root voxel and all of the other voxels. the root voxel along with the voxels that have the strongest interaction with the root voxel are then chosen to be the initial target voxels. note: for each iteration of the tamer process, the target voxels are selected by shifting the target voxels from the previous iteration perpendicularly to the phase encode direction by a preset amount. jumpstart of motion parameter search: now the initial guess of the patient's motion is determined by evaluating the data consistency metric over a range of values for each of the m motion parameters and the best value for each parameter is selected to construct the initial guess. joint optimization reduced model search: we now have the initial target voxels, motion estimate, and coil groupings. the following procedure is now executed. let θ ( i ) be the motion trajectory estimate for the i t h search step. let i m a x be the max number of iterations. while i < i m a