Motion Compensation Technique Research Articles

Efficient reconfigurable architecture for moving object detection with motion compensation

The detection and tracking of object in large data surveillance requires a proper motion estimation and compensation techniques which are generally used to detect accurate movement from video stream. In this paper, a novel hardware level architecture involving motion detection, estimation, and compensation is proposed for real-time implementation. The motion vectors are obtained using 16×16 sub-blocks with a novel parallel D flip flop architecture in this work to arrive at an optimised architecture. The sum of absolute difference (SAD) is then calculated by optimized absolute difference and adder blocks designed using kogge-stone adder which helps in improving the speed of the architecture. The controller block is designed by finite state machine model used for synchronization of all the operations. Further, the comparator and compensation blocks are optimized by using basic logical elements and the Kogge-stone adder. Finally, the proposed architecture is implemented on Zynq Z7-10 field-programmable gate array (FPGA) and simulated using System Generator tool for real time traffic signal. The hardware and software parameters are compared with the existing techniques which shows that the proposed architecture is efficient than existing methods of design.

Temporally aware volumetric generative adversarial network-based MR image reconstruction with simultaneous respiratory motion compensation: Initial feasibility in 3D dynamic cine cardiac MRI.

Develop a novel three-dimensional (3D) generative adversarial network (GAN)-based technique for simultaneous image reconstruction and respiratory motion compensation of 4D MRI. Our goal was to enable high-acceleration factors 10.7X-15.8X, while maintaining robust and diagnostic image quality superior to state-of-the-art self-gating (SG) compressed sensing wavelet (CS-WV) reconstruction at lower acceleration factors 3.5X-7.9X. Our GAN was trained based on pixel-wise content loss functions, adversarial loss function, and a novel data-driven temporal aware loss function to maintain anatomical accuracy and temporal coherence. Besides image reconstruction, our network also performs respiratory motion compensation for free-breathing scans. A novel progressive growing-based strategy was adapted to make the training process possible for the proposed GAN-based structure. The proposed method was developed and thoroughly evaluated qualitatively and quantitatively based on 3D cardiac cine data from 42 patients. Our proposed method achieved significantly better scores in general image quality and image artifacts at 10.7X-15.8X acceleration than the SG CS-WV approach at 3.5X-7.9X acceleration (4.53 ± 0.540 vs. 3.13 ± 0.681 for general image quality, 4.12 ± 0.429 vs. 2.97 ± 0.434 for image artifacts, P < .05 for both). No spurious anatomical structures were observed in our images. The proposed method enabled similar cardiac-function quantification as conventional SG CS-WV. The proposed method achieved faster central processing unit-based image reconstruction (6 s/cardiac phase) than the SG CS-WV (312 s/cardiac phase). The proposed method showed promising potential for high-resolution (1 mm3 ) free-breathing 4D MR data acquisition with simultaneous respiratory motion compensation and fast reconstruction time.

A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks.

This paper proposes and implements a lightweight, “real-time” localization system (SORLA) with artificial landmarks (reflectors), which only uses LiDAR data for the laser odometer compensation in the case of high-speed or sharp-turning. Theoretically, due to the feature-matching mechanism of the LiDAR, locations of multiple reflectors and the reflector layout are not limited by geometrical relation. A series of algorithms is implemented to find and track the features of the environment, such as the reflector localization method, the motion compensation technique, and the reflector matching optimization algorithm. The reflector extraction algorithm is used to identify the reflector candidates and estimates the precise center locations of the reflectors from 2D LiDAR data. The motion compensation algorithm predicts the potential velocity, location, and angle of the robot without odometer errors. Finally, the matching optimization algorithm searches the reflector combinations for the best matching score, which ensures that the correct reflector combination could be found during the high-speed movement and fast turning. All those mechanisms guarantee the algorithm’s precision and robustness in the high speed and noisy background. Our experimental results show that the SORLA algorithm has an average localization error of 6.45 mm at a speed of 0.4 m/s, and 9.87 mm at 4.2 m/s, and still works well with the angular velocity of 1.4 rad/s at a sharp turn. The recovery mechanism in the algorithm could handle the failure cases of reflector occlusion, and the long-term stability test of 72 h firmly proves the algorithm’s robustness. This work shows that the strategy used in the SORLA algorithm is feasible for industry-level navigation with high precision and a promising alternative solution for SLAM.

Interventional respiratory motion compensation by simultaneous fluoroscopic and nuclear imaging: a phantom study

Purpose. A compact and mobile hybrid c-arm scanner, capable of simultaneously acquiring nuclear and fluoroscopic projections and SPECT/CBCT, was developed to aid fluoroscopy-guided interventional procedures involving the administration of radionuclides (e.g. hepatic radioembolization). However, as in conventional SPECT/CT, the acquired nuclear images may be deteriorated by patient respiratory motion. We propose to perform compensation for respiratory motion by extracting the motion signal from fluoroscopic projections so that the nuclear counts can be gated into motion bins. The purpose of this study is to quantify the performance of this motion compensation technique with phantom experiments. Methods. Anthropomorphic phantom configurations that are representative of distributions obtained during the pre-treatment procedure of hepatic radioembolization were placed on a stage that translated with three different motion patterns. Fluoroscopic projections and nuclear counts were simultaneously acquired under planar and SPECT/CBCT imaging. The planar projections were visually assessed. The SPECT reconstructions were visually assessed and quantitatively assessed by calculating the activity recovery of the spherical inserts in the phantom. Results. The planar nuclear projections of the translating anthropomorphic phantom were blurry when no motion compensation was applied. With motion compensation, the nuclear projections became representative of the stationary phantom nuclear projection. Similar behavior was observed for the visual quality of SPECT reconstructions. The mean error of the activity recovery in the uncompensated SPECT reconstructions was 15.8% ± 0.9% for stable motion, 11.9% ± 0.9% for small variations, and 11.0% ± 0.9% for large variations. When applying motion compensation, the mean error decreased to 1.8% ± 1.6% for stable motion, 2.2% ± 1.5% for small variations, and 5.2% ± 2.5% for large variations. Conclusion. A compact and mobile hybrid c-arm scanner, capable of simultaneously acquiring nuclear and fluoroscopic projections, can perform compensation for respiratory motion. Such motion compensation results in sharper planar nuclear projections and increases the quantitative accuracy of the SPECT reconstructions.

Cardiac radioablation for atrial fibrillation: Target motion characterization and treatment delivery considerations.

The safe delivery of cardiac radioablation (CR) for atrial fibrillation (AF) is challenged by multi-direction target motion, cardiac rate variability, target proximity to critical structures, and the importance of complete target dose coverage for therapeutic benefit. Careful selection of appropriate treatment procedures is therefore essential. This work characterizes AF cardiac radioablation target motion and target proximity to surrounding structures in both healthy and AF participants to guide optimal treatment technique and technology choice. Ten healthy participants and five participants with AF underwent MRI acquisition. Multi-slice, cardiac-gated, breath-hold cines were acquired and interpolated to create three-dimensional images for 18-30 cardiac phases. Treatment targets at the left and right pulmonary vein ostia (CTVLeft and CTVRight respectively) and adjacent cardiac structures were contoured and their displacements throughout the cardiac cycle were assessed. Target proximity to surrounding structures were measured. Free-breathing real-time two-dimensional cine images were also acquired at 4Hz frequency for between 1- and 2-min duration. The motion of easily identifiable points within the target, diaphragm and sternum was measured to assess respiratory motion. Target motion due to cardiac contraction was most prominent in the medial-lateral direction and of 4-5mm magnitude. CTVRight displacements were smaller in participants with AF than healthy participants in normal sinus rhythm. Nearby cardiac structures often moved with different magnitudes and motion trajectories. CTVLeft and/or CTVRight were in direct contact with the esophagus in 73% of participants. Target motion due to respiration was most prominent in the superior-inferior direction and of 13-14mm magnitude in both healthy and AF participants. AF CR target motion and relative displacement was characterized. The combination of target motion magnitude and relative displacement to critical structures highlights the importance of personalizing motion compensation techniques for effective AF CR treatments.

Data-driven, projection-based respiratory motion compensation of PET data for cardiac PET/CT and PET/MR imaging

BackgroundRespiratory patient motion causes blurring of the PET images that may impact accurate quantification of perfusion and infarction extents in PET myocardial viability studies. In this study, we investigate the feasibility of correcting for respiratory motion directly in the PET-listmode data prior to image reconstruction using a data-driven, projection-based, respiratory motion compensation (DPR-MoCo) technique. MethodsThe DPR-MoCo method was validated using simulations of a XCAT phantom (Biograph mMR PET/MR) as well as experimental phantom acquisitions (Biograph mCT PET/CT). Seven patient studies following a dual-tracer (18F-FDG/13N-NH3) imaging-protocol using a PET/MR-system were also evaluated. The performance of the DPR-MoCo method was compared against reconstructions of the acquired data (No-MoCo), a reference gate method (gated) and an image-based MoCo method using the standard reconstruction-transform-average (RTA-MoCo) approach. The target-to-background ratio (TBRLV) in the myocardium and the noise in the liver (CoVliver) were evaluated for all acquisitions. For all patients, the clinical effect of the DPR-MoCo was assessed based on the end-systolic (ESV), the end-diastolic volumes (EDV) and the left ventricular ejection fraction (EF) which were compared to functional values obtained from the cardiac MR. ResultsThe DPR-MoCo and the No-MoCo images presented with similar noise-properties (CoV) (P = .12), while the RTA-MoCo and reference-gate images showed increased noise levels (P = .05). TBRLV values increased for the motion limited reconstructions when compared to the No-MoCo reconstructions (P > .05). DPR-MoCo results showed higher correlation with the functional values obtained from the cardiac MR than the No-MoCo results, though non-significant (P > .05). ConclusionThe projection-based DPR-MoCo method helps to improve PET image quality of the myocardium without the need for external devices for motion tracking.

Evaluation of the Usefulness of Motion Compensation Technique Using SMART Average Technique

Evaluation of the Usefulness of Motion Compensation Technique Using SMART Average Technique

Comparison of two elastic motion correction approaches for whole-body PET/CT: motion deblurring vs gate-to-gate motion correction

BackgroundRespiratory motion in PET/CT leads to well-known image degrading effects commonly compensated using elastic motion correction approaches. Gate-to-gate motion correction techniques are promising tools for improving clinical PET data but suffer from relatively long reconstruction times. In this study, the performance of a fast elastic motion compensation approach based on motion deblurring (DEB-MC) was evaluated on patient and phantom data and compared to an EM-based fully 3D gate-to-gate motion correction method (G2G-MC) which was considered the gold standard.MethodsTwenty-eight patients were included in this study with suspected or confirmed malignancies in the thorax or abdomen. All patients underwent whole-body [18F]FDG PET/CT examinations applying hardware-based respiratory gating. In addition, a dynamic anthropomorphic thorax phantom was studied with PET/CT simulating tumour motion under controlled but realistic conditions. PET signal recovery values were calculated from phantom scans by comparing lesion activities after motion correction to static ground truth data. Differences in standardized uptake values (SUV) and metabolic volume (MV) between both reconstruction methods as well as between motion-corrected (MC) and non motion-corrected (NOMC) results were statistically analyzed using a Wilcoxon signed-rank test.ResultsPhantom data analysis showed high lesion recovery values of 91% (2 cm motion) and 98% (1 cm) for G2G-MC and 83% (2 cm) and 90% (1 cm) for DEB-MC. The statistical analysis of patient data found significant differences between NOMC and MC reconstructions for SUV max, SUV mean, MV, and contrast-to-noise ratio (CNR) for both reconstruction algorithms. Furthermore, both methods showed similar increases of 11–12% in SUV max and SUV mean after MC. The statistical analysis of the MC/NOMC ratio found no significant differences between the methods.ConclusionBoth motion correction techniques deliver comparable improvements of SUV max, SUV mean, and CNR after MC on clinical and phantom data. The fast elastic motion compensation technique DEB-MC may thereby be a valuable alternative to state-of-the art motion correction techniques.

Automatic motion compensation for structured illumination endomicroscopy using a flexible fiber bundle.

.Significance: Confocal laser scanning enables optical sectioning in clinical fiber bundle endomicroscopes, but lower-cost, simplified endomicroscopes use widefield incoherent illumination instead. Optical sectioning can be introduced in these simple systems using structured illumination microscopy (SIM), a multiframe digital subtraction process. However, SIM results in artifacts when the probe is in motion, making the technique difficult to use in vivo and preventing the use of mosaicking to synthesize a larger effective field of view (FOV).Aim: We report and validate an automatic motion compensation technique to overcome motion artifacts and allow generation of mosaics in SIM endomicroscopy.Approach: Motion compensation is achieved using image registration and real-time pattern orientation correction via a digital micromirror device. We quantify the similarity of moving probe reconstructions to those acquired with a stationary probe using the relative mean of the absolute differences (MAD). We further demonstrate mosaicking with a moving probe in mechanical and freehand operation.Results: Reconstructed SIM images show an improvement in the MAD from 0.85 to 0.13 for lens paper and from 0.27 to 0.12 for bovine tissue. Mosaics also show vastly reduced artifacts.Conclusion: The reduction in motion artifacts in individual SIM reconstructions leads to mosaics that more faithfully represent the morphology of tissue, giving clinicians a larger effective FOV than the probe itself can provide.

A translational motion compensation technique for inverse synthetic aperture radar images using multi‐objective particle swarm optimization algorithm

AbstractIn this study, a novel multi‐objective (MO) motion compensation (MC) technique is proposed to clear blur effects in the inverse synthetic aperture radar (ISAR) images resulted by the translational motion of the target. Thanks to the MO particle swarm optimization (PSO) algorithm, the velocity and acceleration of the target which minimize the entropy and maximize the contrast of the ISAR image are optimally determined. In order to find an optimal solution from trade‐off solutions between entropy and contrast, Pareto front technique is exploited. To demonstrate the performance of the algorithm, the proposed method is implemented for the four ISAR scenarios reported elsewhere, and compared with the single‐objective meta‐heuristic optimization algorithms (artificial bee colony, genetic algorithm, and PSO with island model) implemented in the literature. Furthermore, the accuracy of the proposed technique is numerically pointed out by comparing the entropy and contrast values of each scenarios with the actual values. The results obviously indicate that the proposed MO MC technique is very successful and efficient compared to single‐objective algorithms and performance of the technique is higher than the other methods as the velocity and the acceleration increases.

Inverse identification of constitutive parameters from heat source fields: A local approach applied to hyperelasticity

AbstractIn this paper, a new inverse identification method of constitutive parameters is developed from full kinematic and thermal field measurements. It consists in reconstructing the heat source field from two different approaches by using the heat diffusion equation. The first one requires the temperature field measurement and the value of the thermophysical parameters. The second one is based on the kinematic field measurement and the choice of a thermo‐hyperelastic model that contains the parameters to be identified. The identification is carried out at the local scale, ie, at any point of the heat source field, without using the boundary conditions. In the present work, the method is applied to the challenging case of hyperelasticity from a heterogeneous test. Due to large deformations undergone by the rubber specimen tested, a motion compensation technique is developed to plot the kinematic and the thermal fields at the same points before reconstructing the heterogeneous heat source field. In the present case, the constitutive parameter of the Neo‐Hookean model has been identified, and its distribution has been characterized with respect to the strain state at the surface of a cross‐shaped specimen.

Usage of characteristic points to reduce the time of performance of SAD metric calculation during video flow compression by motion compensation method

Today, it is difficult to imagine a video system that does not use compression of the input video stream for transmission or storage, because the uncompressed size of video files can reach several hundred gigabytes. There are many different video stream compression techniques used in various codecs, however, one of the most commonly used is the motion compensation technique, which allows you to transmit video stream frames in the form of compensated inter frame difference, by finding the motion vectors of individual blocks image. Despite the fact that the above-mentioned technique has been used for the last twenty years, it still has two problem-priority places - assessments of the similarity of image blocks and the algorithm for searching blocks. This article focuses on the problem of estimating the similarity of the image, in order to reduce the time of estimating the similarity of the blocks, which in modern algorithms takes from 40 to 80% of the time of the entire process of encoding the video stream. The article considers the usage of metrics for estimating the similarity of images and their importance for the process of motion compensation during video stream compression, such as SSD (standard deviation) and SAD (sum of absolute differences). The temporal contribution of metrics to the video stream encoding process, in particular to the motion compensation process, is estimated. An algorithm combining classical SAD and parameter estimation based on characteristic points is proposed, which will reduce the metric calculation time for estimating block similarity. The reduction of SAD calculation time due to reduction of comparison points is estimated for three proposed comparison templates - HSAD (half SAD), TSAD (third SAD), DSAD (diagonal SAD). For the selected templates, the results of processing the test video sequence were compared with the results obtained during the processing of the video sequence by the classic SAD. The main attention was paid to the assessment of the following parameters: the relative difference SAD, the increase in the number of candidate blocks, the overlap of candidate blocks.

3D whole-heart isotropic sub-millimeter resolution coronary magnetic resonance angiography with non-rigid motion-compensated PROST

BackgroundTo enable free-breathing whole-heart sub-millimeter resolution coronary magnetic resonance angiography (CMRA) in a clinically feasible scan time by combining low-rank patch-based undersampled reconstruction (3D-PROST) with a highly accelerated non-rigid motion correction framework.MethodsNon-rigid motion corrected CMRA combined with 2D image-based navigators has been previously proposed to enable 100% respiratory scan efficiency in modestly undersampled acquisitions. Achieving sub-millimeter isotropic resolution with such techniques still requires prohibitively long acquisition times. We propose to combine 3D-PROST reconstruction with a highly accelerated non-rigid motion correction framework to achieve sub-millimeter resolution CMRA in less than 10 min. Ten healthy subjects and eight patients with suspected coronary artery disease underwent 4–5-fold accelerated free-breathing whole-heart CMRA with 0.9 mm3 isotropic resolution. Vessel sharpness, vessel length and image quality obtained with the proposed non-rigid (NR) PROST approach were compared against translational correction only (TC-PROST) and a previously proposed NR motion-compensated technique (non-rigid SENSE) in healthy subjects. For the patient study, image quality scoring and visual comparison with coronary computed tomography angiography (CCTA) were performed.ResultsAverage scan times [min:s] were 6:01 ± 0:59 (healthy subjects) and 8:29 ± 1:41 (patients). In healthy subjects, vessel sharpness of the left anterior descending (LAD) and right (RCA) coronary arteries were improved with the proposed non-rigid PROST (LAD: 51.2 ± 8.8%, RCA: 61.2 ± 9.1%) in comparison to TC-PROST (LAD: 43.8 ± 5.1%, P = 0.051, RCA: 54.3 ± 8.3%, P = 0.218) and non-rigid SENSE (LAD: 46.1 ± 5.8%, P = 0.223, RCA: 56.7 ± 9.6%, P = 0.50), although differences were not statistically significant. The average visual image quality score was significantly higher for NR-PROST (LAD: 3.2 ± 0.6, RCA: 3.3 ± 0.7) compared with TC-PROST (LAD: 2.1 ± 0.6, P = 0.018, RCA: 2.0 ± 0.7, P = 0.014) and non-rigid SENSE (LAD: 2.3 ± 0.5, P = 0.008, RCA: 2.5 ± 0.7, P = 0.016). In patients, the proposed approach showed good delineation of the coronaries, in agreement with CCTA, with image quality scores and vessel sharpness similar to that of healthy subjects.ConclusionsWe demonstrate the feasibility of combining high undersampling factors with non-rigid motion-compensated reconstruction to obtain high-quality sub-millimeter isotropic CMRA images in ~ 8 min. Validation in a larger cohort of patients with coronary artery disease is now warranted.

HMLFC: Hierarchical Motion‐Compensated Light Field Compression for Interactive Rendering

AbstractWe present a new motion‐compensated hierarchical compression scheme (HMLFC) for encoding light field images (LFI) that is suitable for interactive rendering. Our method combines two different approaches, motion compensation schemes and hierarchical compression methods, to exploit redundancies in LFI. The motion compensation schemes capture the redundancies in local regions of the LFI efficiently (local coherence) and the hierarchical schemes capture the redundancies present across the entire LFI (global coherence). Our hybrid approach combines the two schemes effectively capturing both local as well as global coherence to improve the overall compression rate. We compute a tree from LFI using a hierarchical scheme and use phase shifted motion compensation techniques at each level of the hierarchy. Our representation provides random access to the pixel values of the light field, which makes it suitable for interactive rendering applications using a small run‐time memory footprint. Our approach is GPU friendly and allows parallel decoding of LF pixel values. We highlight the performance on the two‐plane parameterized light fields and obtain a compression ratio of 30–800× with a PSNR of 40–45 dB. Overall, we observe a ∼2–5× improvement in compression rates using HMLFC over prior light field compression schemes that provide random access capability. In practice, our algorithm can render new views of resolution 512 × 512 on an NVIDIA GTX‐980 at ∼200 fps.

A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating.

Dual cardiac and respiratory gating is a well-known technique for motion compensation in nuclear medicine imaging. In this study, we present a new data fusion framework for dual cardiac and respiratory gating based on multidimensional microelectromechanical (MEMS) motion sensors. Our approach aims at robust estimation of the chest vibrations, that is, high-frequency precordial vibrations and low-frequency respiratory movements for prospective gating in positron emission tomography (PET), computed tomography (CT), and radiotherapy. Our sensing modality in the context of this paper is a single dual sensor unit, including accelerometer and gyroscope sensors to measure chest movements in three different orientations. Since accelerometer- and gyroscope-derived respiration signals represent the inclination of the chest, they are similar in morphology and have the same units. Therefore, we use principal component analysis (PCA) to combine them into a single signal. In contrast to this, the accelerometer- and gyroscope-derived cardiac signals correspond to the translational and rotational motions of the chest, and have different waveform characteristics and units. To combine these signals, we use independent component analysis (ICA) in order to obtain the underlying cardiac motion. From this cardiac motion signal, we obtain the systolic and diastolic phases of cardiac cycles by using an adaptive multi-scale peak detector and a short-time autocorrelation function. Three groups of subjects, including healthy controls (n = 7), healthy volunteers (n = 12), and patients with a history of coronary artery disease (n = 19) were studied to establish a quantitative framework for assessing the performance of the presented work in prospective imaging applications. The results of this investigation showed a fairly strong positive correlation (average r = 0.73 to 0.87) between the MEMS-derived (including corresponding PCA fusion) respiration curves and the reference optical camera and respiration belt sensors. Additionally, the mean time offset of MEMS-driven triggers from camera-driven triggers was 0.23 to 0.3 ± 0.15 to 0.17 s. For each cardiac cycle, the feature of the MEMS signals indicating a systolic time interval was identified, and its relation to the total cardiac cycle length was also reported. The findings of this study suggest that the combination of chest angular velocity and accelerations using ICA and PCA can help to develop a robust dual cardiac and respiratory gating solution using only MEMS sensors. Therefore, the methods presented in this paper should help improve predictions of the cardiac and respiratory quiescent phases, particularly with the clinical patients. This study lays the groundwork for future research into clinical PET/CT imaging based on dual inertial sensors.

Retrospective motion compensation for edited MR spectroscopic imaging

Edited magnetic resonance spectroscopic imaging (MRSI) is capable of mapping the distribution of low concentration metabolites such as gamma-aminobutyric acid (GABA) or and glutathione (GSH), but is prone to subtraction artifacts due to head motion or other instabilities. In this study, a retrospective motion compensation algorithm for edited MRSI is proposed. The algorithm identifies movement-affected signals by comparing residual water and lipid peaks between different transients recorded at the same point in k-space, and either phase corrects, replaces or removes affected spectra prior to spatial Fourier transformation. The method was tested on macromolecule-unsuppressed GABA-edited spin-echo MR spectroscopic imaging data acquired from 8 healthy adults scanned at 3T. Relative to non-motion compensated data sets, the motion compensated data had significantly less subtraction artifacts across subjects. The residual choline (Cho) peak in the spectrum (which is well resolved from as a different chemical shift from GABA and is completely absent in a spectrum without subtraction artifact) was used as a metric of motion artifact severity. The normalized Cho area was 5.14 times lower with motion compensation than without motion compensation. A ‘removal-only’ version of the technique is also shown to be promising in removing motion-corrupted artifacts in a GSH-edited MRSI acquisition acquired in 1 healthy subject. This study introduces a motion compensation technique and demonstrates that retrospective compensation in k-space is possible and significantly reduces the amount of subtraction artifacts in the resulting edited spectra.

Update on MR urography (MRU): technique and clinical applications.

Magnetic resonance imaging of the upper tract (pyelocalyces and ureters) or MR Urography (MRU) is technically possible and when performed correctly offers similar visualization of the upper tracts and for detection of non-calculous diseases of the collecting system similar specificity but with lower sensitivity compared to CTU. MRU provides the ability to simultaneously image the kidneys and urinary bladder with improved soft tissue resolution, better tissue characterization and when combined with assessment of the upper tract, a comprehensive examination of the urinary system. MRU requires meticulous attention to technical details and is a longer more demanding examination compared to CTU. Advances in MR imaging techniques including: parallel imaging, free-breathing motion compensation techniques and compressed sensing can dramatically shorten examination times and improve image quality and patient tolerance for the exam. This review article discusses updates in the MRU technique, summarizes clinical indications and opportunities for MRU in clinical practice and reviews advantages and disadvantages of MRU compared to CTU.

03:09 PM Abstract No. 394 Ultra low-dose CT fluoroscopy for percutaneous interventions: a feasibility study in a porcine model

To determine the feasibility of ultra-low-dose CT fluoroscopy (ULD-CTF) for performing percutaneous CT-guided interventions in an in vivo porcine model and to compare radiation dose, spatial accuracy, and metal artifact reduction to conventional CT (CCT) and CT fluoroscopy (CTF). ULD-CTF is performed by using only two of 984 projection images to determine the position of a device, which is then superimposed on a previously acquired CT. A motion-compensation technique is used to adjust for breathing. An in vivo swine model was used (n=4 animals) with 20 total needle placements guided by conventional CT: lung (n=6), liver (n=6), spleen (n=3), and kidney (n=5). ULD-CTF acquisitions were simulated using CCT acquisition and selecting two (of 984) projection images. The data were retrospectively analyzed and the accuracy, radiation dose, and metal artifacts compared to CCT and CTF. The accuracy of the reconstructed needle tip was 1.11±0.56 mm. The estimated dose reduction for a percutaneous procedure including planning acquisitions was 35% for patients (effective dose) and 98% for physicians (scattered dose in air) compared to CT fluoroscopy. Metal artifacts were statistically significantly reduced (paired T-test, p < 0.001), and the motion compensation accuracy was 2.12±1.48 mm. Ultra-low-dose CT fluoroscopy for percutaneous CT-guided interventions has the potential to reduce radiation exposure for intraprocedural scans to patients and staff by a factor of approximately 500 times compared to conventional CT acquisition while maintaining image quality without metal artifacts.

A hybrid block-based motion estimation algorithm using JAYA for video coding techniques

In any video coding solution, motion estimation (ME) and motion compensation (MC) techniques are widely used and they play an inevitable role in reducing the temporal redundancies between successive frames. Block-based motion estimation (BME) is one of the widely utilized techniques in the recently developed video compression standards by JCT-VC including HEVC due to its efficacy and ease of implementation. In BME, the frames in a video sequence are partitioned into a number of non-overlapping blocks. Then, for each of the blocks, a best-matched block is obtained within a definite search region in the reference frame to minimize certain cost function or fitness value such as the sum of absolute difference (SAD), mean of absolute difference (MAD), or mean square error (MSE). However, the key challenge lies in the evaluation of these cost functions, since they are computationally expensive and involves the most time-taking operations in the BME process. Hence, BME-based approaches can be viewed as an optimization problem and meta-heuristic algorithms can be effectively exploited for the problem under consideration. In this paper, a hybrid BME technique using a recently developed optimization algorithm, namely, JAYA algorithm, is proposed. Besides this proposal, several other modules, namely, the fitness approximation technique, search history preservation, and early & adaptive termination criterion are utilized. The main objective behind the use of the aforementioned modules is to avoid the unnecessary evaluation of the fitness function. Exhaustive simulations are carried out to demonstrate the efficacy of the proposed method over that of the benchmark schemes with respect to different performance measures, namely, the peak signal-to-noise ratio (PSNR), PSNR degradation ratio (DPSNR), search efficiency, structural similarity index measure (SSIM), and computation time. Comparative analysis and quantitative evaluation clearly show that the present technique produces more reliable motion vectors with competitive time complexity as compared to that of the benchmark schemes.

Automated quantification and evaluation of motion artifact on coronary CT angiography images.

This study developed and validated a Motion Artifact Quantification algorithm to automatically quantify the severity of motion artifacts on coronary computed tomography angiography (CCTA) images. The algorithm was then used to develop a Motion IQ Decision method to automatically identify whether a CCTA dataset is of sufficient diagnostic image quality or requires further correction. The developed Motion Artifact Quantification algorithm includes steps to identify the right coronary artery (RCA) regions of interest (ROIs), segment vessel and shading artifacts, and to calculate the motion artifact score (MAS) metric. The segmentation algorithms were verified against ground-truth manual segmentations. The segmentation algorithms were also verified by comparing and analyzing the MAS calculated from ground-truth segmentations and the algorithm-generated segmentations. The Motion IQ Decision algorithm first identifies slices with unsatisfactory image quality using a MAS threshold. The algorithm then uses an artifact-length threshold to determine whether the degraded vessel segment is large enough to cause the dataset to be nondiagnostic. An observer study on 30 clinical CCTA datasets was performed to obtain the ground-truth decisions of whether the datasets were of sufficient image quality. A five-fold cross-validation was used to identify the thresholds and to evaluate the Motion IQ Decision algorithm. The automated segmentation algorithms in the Motion Artifact Quantification algorithm resulted in Dice coefficients of 0.84 for the segmented vessel regions and 0.75 for the segmented shading artifact regions. The MAS calculated using the automated algorithm was within 10% of the values obtained using ground-truth segmentations. The MAS threshold and artifact-length thresholds were determined by the ROC analysis to be 0.6 and 6.25mm by all folds. The Motion IQ Decision algorithm demonstrated 100% sensitivity, 66.7%±27.9% specificity, and a total accuracy of 86.7%±12.5% for identifying datasets in which the RCA required correction. The Motion IQ Decision algorithm demonstrated 91.3% sensitivity, 71.4% specificity, and a total accuracy of 86.7% for identifying CCTA datasets that need correction for any of the three main vessels. The Motion Artifact Quantification algorithm calculated accurate (<10% error) motion artifact scores using the automated segmentation methods. The developed algorithms demonstrated high sensitivity (91.3%) and specificity (71.4%) in identifying datasets of insufficient image quality. The developed algorithms for automatically quantifying motion artifact severity may be useful for comparing acquisition techniques, improving best-phase selection algorithms, and evaluating motion compensation techniques.