Motion-induced Artifacts Research Articles

High-quality initial image-guided 4D CBCT reconstruction.

Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts because the 4D CBCT reconstruction process is an extreme sparse-view CT procedure wherein only under-sampled projections are used for the reconstruction of each phase. To obtain a set of 4D CBCT images achieving both high spatial and temporal resolution, we propose an algorithm by providing a high-quality initial image at the beginning of the iterative reconstruction process for each phase to guide the final reconstructed result toward its optimal solution. The proposed method consists of three steps to generate the initial image. First, a prior image is obtained by an iterative reconstruction method using the measured projections of the entire set of 4D CBCT images. The prior image clearly shows the appearance of structures in static regions, although it contains blurring artifacts in motion regions. Second, the robust principal component analysis (RPCA) model is adopted to extract the motion components corresponding to each phase-resolved image. Third, a set of initial images are produced by the proposed linear estimation model that combines the prior image and the RPCA-decomposed motion components. The final 4D CBCT images are derived from the simultaneous algebraic reconstruction technique (SART) equipped with the initial images. Qualitative and quantitative evaluations were performed by using two extended cardiac-torso (XCAT) phantoms and two sets of patient data. Several state-of-the-art 4D CBCT algorithms were performed for comparison to validate the performance of the proposed method. The image quality of phase-resolved images is greatly improved by the proposed method in both phantom and patient studies. The results show an outstanding spatial resolution, in which streaking artifacts are suppressed to a large extent, while detailed structures such as tumors and blood vessels are well restored. Meanwhile, the proposed method depicts a high temporal resolution with a distinct respiratory motion change at different phases. For simulation phantom, quantitative evaluations of the simulation data indicate that an average of 36.72% decrease at EI phase and 42% decrease at EE phase in terms of root-mean-square error (RMSE) are achieved by our method when comparing with PICCS algorithm in Phantom 1 and Phantom 2. In addition, the proposed method has the lowest entropy and the highest normalized mutual information compared with the existing methods in simulation experiments, such as PRI, RPCA-4DCT, SMART, and PICCS. And for real patient cases, the proposed method also achieves the lowest entropy value compared with the competitive method. The proposed algorithm can generate an optimal initial image to improve iterative reconstruction performance. The final sequence of phase-resolved volumes guided by the initial image achieves high spatiotemporal resolution by eliminating motion-induced artifacts. This study presents a practical 4D CBCT reconstruction method with leading image quality.

Reliable Breathing Tracking with Wearable Mask Device.

Breathing tracking is critical for the assessment of lung functions, exercise physiologies, and energy expenditure. Conventional methods require using a face mask or mouthpiece that is connected to a stationary equipment through a tube, restricting the location, movement, or even the posture. To obtain accurate breathing physiology parameters that represent the true state of the patient during different scenarios, a wearable technology that has less intervention to patient's activities in free-living conditions is highly preferred. Here, we propose a miniaturized, reliable, and wide-dynamic ranged flow sensing technology that is immune to orientation, movement, and noise. As far as we know, this is the first work of introducing a fully integrated mask device focusing on breath tracking in free-living conditions. There are two key challenges for achieving this goal: miniaturized flow sensing and motion-induced artifacts elimination. To address these challenges, we come up with two technical innovations: 1) in hardware wise, we have designed an integrated flow sensing technique based on differential pressure Pneumotach approach and motion sensing; 2) in software wise, we have developed comprehensive algorithms based baseline tracking and orientation and motion compensation. The effectiveness of the proposed technology has been proven by the experiments. Experimental results from simulator and real breath conditions show high correlation (R2 = 0.9994 and 0.9964 respectively) and mean error within 2.5% for Minute Volume (VE), when compared to values computed from reference methods. These results show that the proposed method is accurate and reliable to track the key breath parameters in free-living conditions.

Wireless, battery-free subdermally implantable photometry systems for chronic recording of neural dynamics

Recording cell-specific neuronal activity while monitoring behaviors of freely moving subjects can provide some of the most significant insights into brain function. Current means for monitoring calcium dynamics in genetically targeted populations of neurons rely on delivery of light and recording of fluorescent signals through optical fibers that can reduce subject mobility, induce motion artifacts, and limit experimental paradigms to isolated subjects in open, two-dimensional (2D) spaces. Wireless alternatives eliminate constraints associated with optical fibers, but their use of head stages with batteries adds bulk and weight that can affect behaviors, with limited operational lifetimes. The systems introduced here avoid drawbacks of both types of technologies, by combining highly miniaturized electronics and energy harvesters with injectable photometric modules in a class of fully wireless, battery-free photometer that is fully implantable subdermally to allow for the interrogation of neural dynamics in freely behaving subjects, without limitations set by fiber optic tethers or operational lifetimes constrained by traditional power supplies. The unique capabilities of these systems, their compatibility with magnetic resonant imaging and computed tomography and the ability to manufacture them with techniques in widespread use for consumer electronics, suggest a potential for broad adoption in neuroscience research.

Dynamic glucose-enhanced (DGE) MRI in the human brain at 7 T with reduced motion-induced artifacts based on quantitative R1ρ mapping.

Dynamic glucose-enhanced (DGE)-MRI based on chemical exchange-sensitive MRI, that is, glucoCEST and gluco-chemical exchange-sensitive spin-lock(glucoCESL), is intrinsically prone to motion-induced artifacts because the final DGE contrast relies on the difference of images, which were acquired with a time gap of several mins. In this study, identification of different types of motion-induced artifacts led to the development of a 3D acquisition protocol for DGE examinations in the human brain at 7 T with improved robustness in the presence of subject motion. DGE-MRI was realized by the chemical exchange-sensitive spin-lock approach based either on relaxation rate in the rotating frame(R1ρ )-weighted or quantitative R1ρ imaging. A 3D image readout was implemented at 7 T, enabling retrospective volumetric coregistration of the image series and quantification of subject motion. An examination of a healthy volunteer without administration of glucose allowed for the identification of isolated motion-induced artifacts. Even after coregistration, significant motion-induced artifacts remained in the DGE contrast based on R1ρ -weighted images. This is due to the spatially varying sensitivity of the coil and was found to be compensated by a quantitative R1ρ approach. The coregistered quantitative approach allowed the observation of a clear increase of the DGE contrast in a patient with glioblastoma, which did not correlate with subject motion. The presented 3D acquisition protocol enables DGE-MRI examinations in the human brain with improved robustness against motion-induced artifacts. Correction of motion-induced artifacts is of high importance for DGE-MRI in clinical studies where an unambiguous assignment of contrast changes due to an actual change in local glucose concentration is a prerequisite.

A Lower Limb Phantom for Simulation and Assessment of Electromyography Technology.

Electromyography signal processing approaches have traditionally been validated through computer simulations. Electromyography electrodes and systems are often not validated or have been validated on human subjects where there is no clear ground truth signal for comparison. We sought to develop a physical limb phantom for validation of electromyography hardware and signal processing approaches. We embedded pairs of wires within a conductive gelatin surrounding an artificial bone such that the antennae could broadcast identified ground truth signals. The ground truth signals can be simple sinusoids or more complex representations of muscle activity. With the phantom and surface electromyography electrodes, we were able to show varying levels of crosstalk between nearby recording electrodes as we altered the amplitude of the antennae signals. We were also able to induce motion artifacts in our recordings by lightly dropping the phantom on a surface while antennae broadcast signals. High-density electromyography recordings of the trials showed that traditional filtering techniques fail to fully eliminate relatively small motion artifacts. The results suggest that the electrical limb phantom could be a valuable tool for testing potential effects of muscle crosstalk and motion artifacts on different electromyography systems and signal processing approaches.

Multi-shot diffusion-weighted MRI reconstruction with magnitude-based spatial-angular locally low-rank regularization (SPA-LLR).

To resolve the motion-induced phase variations in multi-shot multi-direction diffusion-weighted imaging (DWI) by applying regularization to magnitude images. A nonlinear model was developed to estimate phase and magnitude images separately. A locally low-rank regularization (LLR) term was applied to the magnitude images from all diffusion-encoding directions to exploit the spatial and angular correlation. In vivo experiments with different resolutions and b-values were performed to validate the proposed method. The proposed method significantly reduces the noise level compared to the conventional reconstruction method and achieves submillimeter (0.8mm and 0.9mm isotropic resolutions) DWI with a b-value of 1,000 and 1-mm isotropic DWI with a b-value of 2,000 without modification of the sequence. A joint reconstruction method with spatial-angular LLR regularization on magnitude images substantially improves multi-direction DWI reconstruction, simultaneously removes motion-induced phase artifacts, and denoises images.

Periodically rotated overlapping parallel lines with enhanced reconstruction acquisition to improve motion-induced artifacts in bladder cancer imaging: Initial findings.

Motion-induced artifacts have been a major drawback in bladder cancer imaging. This study is to evaluate the clinical utility of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) acquisition in improving motion-induced artifacts in T2-weighted (T2W) magnetic resonance imaging (MRI) of bladder cancer at 3T.Sixteen patient MRI exams were included. Using a Likert scale, 2 radiologists independently scored T2W data without and with PROPELLER in terms of artifact severity and tumor visualization. Statistical analysis was done to assess the image quality improvement by PROPELLER and inter-observer variability.Without PROPELLER, the median scores of artifact severity and tumor visualization were 1.5 and 1.5 for reviewer 1, and 2.0 and 2.0 for reviewer 2. With PROPELLER, the scores increased to 3 and 3.5 for reviewer 1, and 3.5 and 3.5 for reviewer 2. Despite the inter-observer variability (κ scores < 0.2), both reviewers found significant improvement in artifacts and visualization (all P < .001).PROPELLER acquisition significantly improved the image quality of T2W-MRI. These initial findings indicate that this technique should be utilized in clinical MRI of the bladder.

Probability-based 3D k-space sorting for motion robust 4D-MRI.

Current 4D-MRI techniques are prone to breathing-variation-induced motion artifacts. This study developed a novel method for motion-robust multi-cycle 4D-MRI using probability-based multi-cycle sorting to overcome this deficiency. The main cycles were first extracted from the breathing signal. 3D k-space data were then sorted using a result-driven method for each main cycle. The new method was tested on a 4D-extended cardiac-torso (XCAT) phantom with a patient and an artificially generated breathing curve. For comparison, the k-space data were sorted using conventional phase sorting to generate single-cycle 4D-MRI images. Signal-to-noise ratio (SNR) of tumor and liver, tumor volume consistency, and average intensity projection (AIP) accuracy were compared between the two methods. The original phantom images were used as references for the evaluation. The new method showed improved tumor-to-liver SNR and tumor volume consistency as compared to 3D k-space phase sorting in both the simulated artificial and real patient breathing signals. For the artificial breathing cycles, the average tumor-to-liver SNR and standard deviation (SD) of tumor volume were 2.53 and 3.80% for cycle 1, 2.24 and 6.16% for cycle 2 of probability-based sorting as compared to 1.47 and 21.83% obtained using the phase sorting method; for the patient breathing curve, values of 1.99 and 2.71%, 1.97 and 3.29%, 1.88 and 4.16% were observed for cycle 1, cycle 2 and cycle 3 of probability-based sorting, versus 1.44 and 7.20% for phase sorting method. Furthermore, the AIP accuracy was improved in the probability-based sorting approach when compared to phase sorting, with the average intensity difference per voxel reduced from 0.39 to 0.15 for the artificial curve, and from 0.46 to 0.21 for the patient curve. We demonstrated the feasibility of probability-based 3D k-space sorting for motion-robust multi-cycle 4D-MRI reconstruction with breathing variation induced motion artifact reduction compared with conventional 2D image sorting and 3D phase sorting methods. This new technique can potentially improve the accuracy of radiation treatment guidance for mobile targets.

A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy

In the analysis of functional Near-Infrared Spectroscopy (fNIRS) signals from real-world scenarios, artifact rejection is essential. However, currently there exists no gold-standard. Although a plenitude of methodological approaches implicitly assume the presence of latent processes in the signals, elaborate Blind-Source-Separation methods have rarely been applied. A reason are challenging characteristics such as Non-instantaneous and non-constant coupling, correlated noise and statistical dependencies between signal components. We present a novel suitable BSS framework that tackles these issues by incorporating A) Independent Component Analysis methods that exploit both higher order statistics and sample dependency, B) multimodality, i.e., fNIRS with accelerometer signals, and C) Canonical-Correlation Analysis with temporal embedding. This enables analysis of signal components and rejection of motion-induced physiological hemodynamic artifacts that would otherwise be hard to identify. We implement a method for Blind Source Separation and Accelerometer based Artifact Rejection and Detection (BLISSA2RD). It allows the analysis of a novel n-back based cognitive workload paradigm in freely moving subjects, that is also presented in this manuscript. We evaluate on the corresponding data set and simulated ground truth data, making use of metrics based on 1st and 2nd order statistics and SNR and compare with three established methods: PCA, Spline and Wavelet-based artifact removal. Across 17 subjects, the method is shown to reduce movement induced artifacts by up to two orders of magnitude, improves the SNR of continuous hemodynamic signals in single channels by up to 10dB, and significantly outperforms conventional methods in the extraction of simulated Hemodynamic Response Functions from strongly contaminated data. The framework and methods presented can serve as an introduction to a new type of multivariate methods for the analysis of fNIRS signals and as a blueprint for artifact rejection in complex environments beyond the applied paradigm.

Diffusion MRI using two-dimensional single-shot radial imaging (2D ss-rDWI) with variable flip angle and random view ordering

PurposeThe main objective of this study is to develop a 2D single-shot radial-DWI (2D ss-rDWI) technique to reduce motion artifacts and geometric distortion in DW images. MethodA diffusion-preparation module is developed and applied prior to the data acquisition. Because the diffusion-prepared longitudinal magnetization is measured over multiple RF excitations in each shot, 2D ss-rDWI is subject to low signal-to-noise ratio (SNR). We used variable-flip angle (VFA), random view ordering (RVO), and sliding spokes, and compared the performances to constant flip angle (CFA), smooth view ordering (SVO), and identical spoke averaging, respectively. For each technique, we performed numerical simulation and MRI experiments on a fluid phantom as well as in-vivo human brain studies with a 3 T MRI system. ResultsUsing VFA, optimal SNR was acquired for 2D ss-rDWI. Using SVO, the high signal is clustered at specific quadrant in 2D k-space: the first quadrant using high initial flip angle or the last quadrant using the low flip angle. This clustered signal in k-space led to geometric distortion in image space. 2D ss-rDWI using RVO spreads the high signaled spokes over all angular directions and removes the view-order-related distortion. The in-vivo images using 2D ss-rDWI with VFA and RVO show no geometric distortion at the skull base brain, but greatly reduced SNR compared with those using 2D ss-DWEPI. Conclusion2D ss-rDWI is optimized by using VFA with RVO. The resultant DWI using 2D ss-rDWI is insensitive to motion-induced artifacts and geometric distortion. Even with low SNR, it may be useful for DWI of organs limited by severe susceptibility-induced geometric distortion.

Enhanced Quantification of Retinal Perfusion by Improved Discrimination of Blood Flow From Bulk Motion Signal in OCTA.

PurposeQuantification of optical coherence tomography angiography (OCTA) is confounded by the prevalence of bulk motion. We have previously developed a regression-based bulk motion subtraction (rb-BMS) algorithm that estimates bulk motion velocity and corrects for its effect on flow signal. Here, we aim to investigate its ability to improve the reliability of capillary density (CD) quantification.MethodsTwo spectral-domain systems (70-kHz Avanti/AngioVue and 68-kHz Cirrus/AngioPlex) acquired 6 × 6-mm OCTA scans. The rb-BMS algorithm was applied on each OCTA volume. Regression analysis of angiographic versus reflectance signal of avascular A-lines in B-frames was used to set an optimized reflectance-adjusted threshold for discriminating vascular versus nonvascular voxels. The CD was calculated from en face maximum projections of the superficial vascular complex in macular scans and the nerve fiber layer plexus in disc scans, excluding large vessels. The retinal signal strength (RSS) was calculated by averaging the logarithmic-scale OCT reflectance signal, and its correlation with CD was investigated.ResultsEight healthy eyes were scanned with each instrument on 2 separate days. The rb-BMS algorithm improved within-visit repeatability and between-visit reproducibility of CD compared with a global-threshold measurement algorithm. Using the rb-BMS algorithm, the CD results were less affected by RSS and the population variation was reduced. Motion-induced line artifacts were also reduced.ConclusionsThe rb-BMS algorithm improved the reliability of perfusion quantification in OCTA on both Food and Drug Administration–cleared spectral-domain OCTA systems.Translational RelevanceThe rb-BMS method helped reduce the inter-scan variability by generating accurate vessel maps, improving the reliability of retinal perfusion quantification.

Three-dimensional diffusion imaging with spiral encoded navigators from stimulated echoes (3D-DISPENSE).

To introduce a new method for motion-insensitive 3D multishot diffusion imaging using 3D spiral-encoded navigators from stimulated echoes (3D-DISPENSE). The 3D-DISPENSE sequence contains a 3D stack-of-spiral navigator generated between the diffusion preparation and the turbo spin-echo image acquisition from the twin pathway of a stimulated echo. Unlike normal navigator methods, 3D-DISPENSE separates the navigator acquisition from the imaging readout without compromising the image SNR. Phase information from the navigators was included in an iterative image reconstruction algorithm to correct for intershot phase incoherence caused by motion. In a phantom experiment, 3D-DISPENSE correctly estimated deliberately introduced phase errors. In a moving phantom, motion-induced artifacts in the DWI were greatly mitigated by 3D-DISPENSE. The ADC after 3D-DISPENSE correction was identical to the reference. In a brain diffusion tensor experiment, phase-incoherence artifacts from breathing, cardiac, and subject motion were removed almost perfectly in all view angles, resulting in distortion-free DWI and color-coded fractional anisotropy maps with 1.5-mm isotropic resolution and nearly full brain coverage. Three-dimensional DISPENSE corrects motion-induced phase-incoherence artifacts in 3D multishot diffusion imaging and produces high-quality 3D DWI and DTI.

Abdominal DCE-MRI reconstruction with deformable motion correction for liver perfusion quantification.

Abdominal dynamic contrast-enhanced (DCE) MRI suffers from motion-induced artifacts that can blur images and distort contrast-agent uptake curves. For liver perfusion analysis, image reconstruction with rigid-body motion correction (RMC) can restore distorted portal-venous input functions (PVIF) to higher peak amplitudes. However, RMC cannot correct for liver deformation during breathing. We present a reconstruction algorithm with deformable motion correction (DMC) that enables correction of breathing-induced deformation in the whole abdomen. Raw data from a golden-angle stack-of-stars gradient-echo sequence were collected for 54 DCE-MRI examinations of 31 patients. For each examination, a respiratory motion signal was extracted from the data and used to reconstruct 21 breathing states from inhale to exhale. The states were aligned with deformable image registration to the end-exhale state. Resulting deformation fields were used to correct back-projection images before reconstruction with view sharing. Images with DMC were compared to uncorrected images and images with RMC. DMC significantly increased the PVIF peak amplitude compared to uncorrected images (P << 0.01, mean increase: 8%) but not compared to RMC. The increased PVIF peak amplitude significantly decreased estimated portal-venous perfusion in the liver (P << 0.01, mean decrease: 8 ml/(100 ml·min)). DMC also removed artifacts in perfusion maps at the liver edge and reduced blurring of liver tumors for some patients. DCE-MRI reconstruction with DMC can restore motion-distorted uptake curves in the abdomen and remove motion artifacts from reconstructed images and parameter maps but does not significantly improve perfusion quantification in the liver compared to RMC.

Characterizing the Effects of MR Image Quality Metrics on Intrinsic Connectivity Brain Networks: A Multivariate Approach.

Motion-induced artifact detection has become a fixture in the assessment of functional magnetic resonance imaging (fMRI) quality control. However, the effects of other MR image quality (IQ) metrics on intrinsic connectivity brain networks are largely unexplored. Accordingly, we report herein the initial assessment of the effects of a comprehensive list of IQ metrics on resting state networks using a multivariate analysis of covariance (MANCOVA) approach based on high-order spatial independent component analysis (ICA). Three categories of MR IQ metrics were considered: (1) metrics for artifacts including the AFNI outlier ratio and quality index, framewise displacement, and ghost to signal ratio, (2) metrics for the temporal quality of MRI data including the temporal framewise change in global BOLD signals (DVARS), global correlation of time-series, and temporal signal to noise ratio, (3) metrics for the structural quality of MRI data including the entropy focus criterion, foreground-background energy ratio, full-width half maximum smoothness, and static signal to noise ratio. After FDR-correction for multiple comparisons, results showed significant effects of the static and temporal signal to noise ratios on the spatial map intensities of the basal ganglia, default-mode and cerebellar networks. AFNI outlier ratio, framewise displacement and DVARS exhibited significant effects on the BOLD power spectra of sensorimotor networks. The global correlation of time-series displayed wide-spread modulation of the spectral power in most networks. Further investigations of the effect of IQ metrics on the characteristics of intrinsic connectivity brain networks allow more accurate interpretation of the fMRI results.

Development of a Dedicated Rebinner with Rigid Motion Correction for the mMR PET/MR Scanner, and Validation in a Large Cohort of 11C-PIB Scans.

Head motion occurring during brain PET studies leads to image blurring and to bias in measured local quantities. The objective of this work was to implement a correction method for PET data acquired with the mMR synchronous PET/MR scanner. Methods: A list-mode-based motion-correction approach has been designed. The developed rebinner chronologically reads the recorded events from the Siemens list-mode file, applies the estimated geometric transformations, and frames the detected counts into sinograms. The rigid-body motion parameters were estimated from an initial dynamic reconstruction of the PET data. We then optimized the correction for 11C-Pittsburgh compound B (11C-PIB) scans using simulated and actual data with well-controlled motion. Results: An efficient list-mode-based motion correction approach has been implemented, fully optimized, and validated using simulated and actual PET data. The average spatial resolution loss induced by inaccuracies in motion parameter estimates and by the rebinning process was estimated to correspond to a 1-mm increase in full width at half maximum with motion parameters estimated directly from the PET data with a temporal frequency of 20 s. The results show that the rebinner can be safely applied to the 11C-PIB scans, allowing almost complete removal of motion-induced artifacts. The application of the correction method to a large cohort of 11C-PIB scans led to the following observations: first, that more than 21% of the scans were affected by motion greater than 10 mm (39% for subjects with Mini-Mental State Examination scores below 20), and second, that the correction led to quantitative changes in Alzheimer-specific cortical regions of up to 30%. Conclusion: The rebinner allows accurate motion correction at a cost of minimal resolution reduction. Application of the correction to a large cohort of 11C-PIB scans confirmed the necessity of systematically correcting for motion to obtain quantitative results.

Range Imaging for Motion Compensation in C-Arm Cone-Beam CT of Knees under Weight-Bearing Conditions

C-arm cone-beam computed tomography (CBCT) has been used recently to acquire images of the human knee joint under weight-bearing conditions to assess knee joint health under load. However, involuntary patient motion during image acquisition leads to severe motion artifacts in the subsequent reconstructions. The state-of-the-art uses fiducial markers placed on the patient’s knee to compensate for the induced motion artifacts. The placement of markers is time consuming, tedious, and requires user experience, to guarantee reliable motion estimates. To overcome these drawbacks, we recently investigated whether range imaging would allow to track, estimate, and compensate for patient motion using a range camera. We argue that the dense surface information observed by the camera could reveal more information than only a few surface points of the marker-based method. However, the integration of range-imaging with CBCT involves flexibility, such as where to position the camera and what algorithm to align the data with. In this work, three dimensional rigid body motion is estimated for synthetic data acquired with two different range camera trajectories: a static position on the ground and a dynamic position on the C-arm. Motion estimation is evaluated using two different types of point cloud registration algorithms: a pair wise Iterative Closest Point algorithm as well as a probabilistic group wise method. We compare the reconstruction results and the estimated motion signals with the ground truth and the current reference standard, a marker-based approach. To this end, we qualitatively and quantitatively assess image quality. The latter is evaluated using the Structural Similarity (SSIM). We achieved results comparable to the marker-based approach, which highlights the potential of both point set registration methods, for accurately recovering patient motion. The SSIM improved from 0.94 to 0.99 and 0.97 using the static and the dynamic camera trajectory, respectively. Accurate recovery of patient motion resulted in remarkable reduction in motion artifacts in the CBCT reconstructions, which is promising for future work with real data.

PET motion correction in context of integrated PET/MR: Current techniques, limitations, and future projections.

Patient motion is an important consideration in modern PET image reconstruction. Advances in PET technology mean motion has an increasingly important influence on resulting image quality. Motion-induced artifacts can have adverse effects on clinical outcomes, including missed diagnoses and oversized radiotherapy treatment volumes. This review aims to summarize the wide variety of motion correction techniques available in PET and combined PET/CT and PET/MR, with a focus on the latter. A general framework for the motion correction of PET images is presented, consisting of acquisition, modeling, and correction stages. Methods for measuring, modeling, and correcting motion and associated artifacts, both in literature and commercially available, are presented, and their relative merits are contrasted. Identified limitations of current methods include modeling of aperiodic and/or unpredictable motion, attaining adequate temporal resolution for motion correction in dynamic kinetic modeling acquisitions, and maintaining availability of the MR in PET/MR scans for diagnostic acquisitions. Finally, avenues for future investigation are discussed, with a focus on improvements that could improve PET image quality, and that are practical in the clinical environment.

Motion compensation in the region of the coronary arteries based on partial angle reconstructions from short-scan CT data.

In order to mitigate motion-induced artifacts, several motion compensation (MoCo) methods have been developed, which are either able to (a) compensate for severe artifacts, but utilize the data for the reconstruction of several cardiac phases, or (b) improve image quality of a single reconstruction with only moderate motion artifacts. We propose a method combining both benefits: dose efficiency by utilizing only the data needed for a single short-scan reconstruction while still being able to compensate for severe artifacts. We introduce a MoCo method, which we call PAMoCo, to improve the visualization of the coronary arteries of a standard coronary CT angiography exam by reducing motion artifacts. As a first step, we segment a region of interest covering a chosen coronary artery. We subdivide a volume covering the whole heart into several stacks, which are sub-volumes, reconstructed from phase-correlated short-scan data acquired during different heart cycles. Motion-compensated reconstruction is performed for each stack separately, based on partial angle reconstructions, which are derived by dividing the data corresponding to the segmented stack volume into several double-overlapping sectors. We model motion along the coronary artery center line obtained from segmentation and the temporal dimension by a low-degree polynomial and create a dense 3D motion vector field (MVF). The parameters defining the MVF are estimated by optimizing an image artifact measuring cost function and we employ a semi-global optimization routine by re-initializing the optimization multiple times. The algorithm was evaluated on the basis of a phantom measurement and clinical data. For the phantom measurement an artificial vessel equipped with calcified lesions mounted on a moving robot arm was measured, where typical coronary artery motion patterns for 70 bpm and 90 bpm have been applied. For analysis, we calculated the calcified volume V inside an ROI and measured the maximum vessel diameter d based on cross-sectional views to compare the performances of standard reconstructions obtained via filtered backprojection (FBP) and PAMoCo reconstructions between 20% and 80% of the cardiac cycle. Further, the new algorithm was applied to six clinical cases of patients with heart rates between 50 bpm and 74 bpm. Standard FBP, PAMoCo reconstructions were performed and compared to best phase FBP reconstructions and another MoCo algorithm, which is based on motion artifact metrics (MAM), via visual inspection. In case of the phantom measurement we found a strong dependence of V and d on the cardiac phase in case of the FBP reconstructions. When applying PAMoCo, V and d became almost constant due to a better discrimination from calcium to vessel and water background and values close to the ground truth have been derived. In the clinical study we chose reconstructions showing strong motion artifacts and observed a substantially improved delineation of the coronary arteries in PAMoCo reconstructions compared to the standard FBP reconstructions and also the MAM images, confirming the findings of the phantom measurement. Due to the fast reconstruction of PAMoCo images and the introduction of a new motion model, we were able to re-initialize the optimization routine at pre-selected parameter sets and thereby increase the potential of the MAM algorithm. From the phantom measurement we conclude that PAMoCo performed almost equally well in all cardiac phases and suggest applying the PAMoCo algorithm for single source systems in case of patients with high or irregular heart rates.

Motion compensation in extremity cone-beam CT using a penalized image sharpness criterion

Cone-beam CT (CBCT) for musculoskeletal imaging would benefit from a method to reduce the effects of involuntary patient motion. In particular, the continuing improvement in spatial resolution of CBCT may enable tasks such as quantitative assessment of bone microarchitecture (0.1 mm–0.2 mm detail size), where even subtle, sub-mm motion blur might be detrimental. We propose a purely image based motion compensation method that requires no fiducials, tracking hardware or prior images. A statistical optimization algorithm (CMA-ES) is used to estimate a motion trajectory that optimizes an objective function consisting of an image sharpness criterion augmented by a regularization term that encourages smooth motion trajectories. The objective function is evaluated using a volume of interest (VOI, e.g. a single bone and surrounding area) where the motion can be assumed to be rigid. More complex motions can be addressed by using multiple VOIs. Gradient variance was found to be a suitable sharpness metric for this application. The performance of the compensation algorithm was evaluated in simulated and experimental CBCT data, and in a clinical dataset. Motion-induced artifacts and blurring were significantly reduced across a broad range of motion amplitudes, from 0.5 mm to 10 mm. Structure similarity index (SSIM) against a static volume was used in the simulation studies to quantify the performance of the motion compensation. In studies with translational motion, the SSIM improved from 0.86 before compensation to 0.97 after compensation for 0.5 mm motion, from 0.8 to 0.94 for 2 mm motion and from 0.52 to 0.87 for 10 mm motion (~70% increase). Similar reduction of artifacts was observed in a benchtop experiment with controlled translational motion of an anthropomorphic hand phantom, where SSIM (against a reconstruction of a static phantom) improved from 0.3 to 0.8 for 10 mm motion. Application to a clinical dataset of a lower extremity showed dramatic reduction of streaks and improvement in delineation of tissue boundaries and trabecular structures throughout the whole volume. The proposed method will support new applications of extremity CBCT in areas where patient motion may not be sufficiently managed by immobilization, such as imaging under load and quantitative assessment of subchondral bone architecture.

A Novel Method of Cone Beam CT Projection Binning Based on Image Registration.

Accurate sorting of beam projections is important in 4D cone beam computed tomography (4D CBCT) to improve the quality of the reconstructed 4D CBCT image by removing motion-induced artifacts. We propose image registration-based projection binning (IRPB), a novel marker-less binning method for 4D CBCT projections, which combines intensity-based feature point detection and trajectory tracking using random sample consensus. IRPB extracts breathing motion and phases by analyzing tissue feature point trajectories. We conducted experiments with two phantom and six patient datasets, including both regular and irregular respirations. In experiments, we compared the performance of the proposed IRPB, Amsterdam Shroud method (AS), Fourier transform-based method (FT), and local intensity feature tracking method (LIFT). The results showed that the average absolute phase shift of IRPB was 3.74 projections and 0.48 projections less than that of FT and LIFT, respectively. AS lost the most breathing cycles in the respiration extraction for the five patient datasets, so we could not compare the average absolute phase shift between IRPB and AS. Based on the peak signal-to-noise ratio (PSNR) of the reconstructed 4D CBCT images, IRPB had 5.08, 1.05, and 2.90 dB larger PSNR than AS, FT, and LIFT, respectively. The average Structure SIMilarity Index (SSIM) of the 4D CBCT image reconstructed by IRPB, AS, and LIFT were 0.87, 0.74, 0.84, and 0.70, respectively. These results demonstrated that IRPB has superior performance to the other standard methods.