Multishot Magnetic Resonance Imaging (MRI) is a promising imaging modality that can produce a high-resolution image with relatively less data acquisition time. The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final MR image that may cause misdiagnosis. Numerous efforts have been made to address this issue; however, all of these proposals are limited in terms of how much motion they can correct and the required computational time. In this paper, we propose a novel generative networks based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI. The proposed framework first employs CG-SENSE reconstruction to produce the motion-corrupted image and then a generative adversarial network (GAN) is used to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative visual analysis) establishes that the proposed method significantly robust and outperforms state-of-the-art motion correction techniques and also reduces severalfold of computational times.
MRI is highly sensitive to subject motion during the -space data acquisition, which can reduce image quality by inducing the motion artifacts. The artifacts by rigid motion are widely observed in multishot MR images during the clinical examination @cite_34 , therefore, the application of motion correction techniques is essentially performed during or after the reconstruction process to obtain an artifact-free image. Retrospective motion correction (RMC) techniques are applied to the rigid motion correction @cite_1 @cite_3 . They perform the -space data acquisition without considering the potential motion and object motion is estimated from acquired -space data @cite_14 . Many researchers proposed different RMC based method for rigid motion correction. For instance, @cite_24 studied the inconsistencies of -space caused by subject motion using parallel imaging (PI) technique. The inconsistent data is discarded and replaced with consistent data generated by the parallel imaging technique to compensate the motion artifacts. This method produces an image with fewer motion artifacts albeit with a lower signal to noise ratio (SNR).
@cite_14 @cite_1 @cite_3 @cite_24 @cite_34
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Subject motion during magnetic resonance imaging (MRI) has been problematic since its introduction as a clinical imaging modality. While sensitivity to particle motion or blood flow can be used to provide useful image contrast, bulk motion presents a considerable problem in the majority of clinical applications. It is one of the most frequent sources of artifacts. Over 30 years of research have produced numerous methods to mitigate or correct for motion artifacts, but no single method can be applied in all imaging situations. Instead, a “toolbox” of methods exists, where each tool is suitable for some tasks, but not for others. This article reviews the origins of motion artifacts and presents current mitigation and correction methods. In some imaging situations, the currently available motion correction tools are highly effective; in other cases, appropriate tools still need to be developed. It seems likely that this multifaceted approach will be what eventually solves the motion sensitivity problem in MRI, rather than a single solution that is effective in all situations. This review places a strong emphasis on explaining the physics behind the occurrence of such artifacts, with the aim of aiding artifact detection and mitigation in particular clinical situations. J. Magn. Reson. Imaging 2015;42:887–901. Motion of an object degrades MR images, as the acquisition is time-dependent, and thus k-space is inconsistently sampled. This causes ghosts. Current motion correction methods make restrictive assumptions on the type of motions, for example, that it is a translation or rotation, and use special properties of k-space for these transformations. Such methods, however, cannot be generalized easily to nonrigid types of motions, and even rotations in multiple shots can be a problem. Here, a method is presented that can handle general nonrigid motion models. A general matrix equation gives the corrupted image from the ideal object. Thus, inversion of this system allows us to get the ideal image from the corrupted one. This inversion is possible by efficient methods mixing Fourier transforms with the conjugate gradient method. A faster but empirical inversion is discussed as well as methods to determine the motion. Simulated three-dimensional affine data and two-dimensional pulsation data and in vivo nonrigid data are used for demonstration. All examples are multishot images where the object moves between shots. The results indicate that it is now possible to correct for nonrigid types of motion that are representative of many types of patient motion, although computation times remain an issue. A new method for correction of MRI motion artifacts induced by corrupted k-space data, acquired by multiple receiver coils such as phased arrays, is presented. In our approach, a projections onto convex sets (POCS)-based method for reconstruction of sensitivity encoded MRI data (POCSENSE) is employed to identify corrupted k-space samples. After the erroneous data are discarded from the dataset, the artifact-free images are restored from the remaining data using coil sensitivity profiles. The error detection and data restoration are based on informational redundancy of phased-array data and may be applied to full and reduced datasets. An important advantage of the new POCS-based method is that, in addition to multicoil data redundancy, it can use a priori known properties about the imaged object for improved MR image artifact correction. The use of such information was shown to improve significantly k-space error detection and image artifact correction. The method was validated on data corrupted by simulated and real motion such as head motion and pulsatile flow. Magn Reson Med 63:1104–1110, 2010. V C 2010 A method has been developed using techniques from partially parallel imaging (PPI) to detect localized inconsistencies in k-space that are caused by certain types of motion. The inconsistent data are discarded and consistent data regenerated from the remaining data using PPI techniques. The price is a small decrease in signal-to-noise ratio (SNR) and additional postprocessing. An iterative scheme is presented which does not require separately acquired coil sensitivity information for the PPI reconstructions. This method has been found to reduce artifact levels in phantom and in vivo test studies. Magn Reson Med 47:677–686, 2002. © 2002 Wiley-Liss, Inc. Abstract Purpose To assess the prevalence, severity, and cost estimates associated with motion artifacts identified on clinical MR examinations, with a focus on the neuroaxis. Methods A retrospective review of 1 randomly selected full calendar week of MR examinations (April 2014) was conducted for the detection of significant motion artifacts in examinations performed at a single institution on 3 different MR scanners. A base-case cost estimate was computed from recently available institutional data, and correlated with sequence time and severity of motion artifacts. Results A total of 192 completed clinical examinations were reviewed. Significant motion artifacts were identified on sequences in 7.5 of outpatient and 29.4 of inpatient and or emergency department MR examinations. The prevalence of repeat sequences was 19.8 of total MRI examinations. The base-case cost estimate yielded a potential cost to the hospital of @math 115,000 per scanner per year may affect hospitals, owing to motion artifacts (univariate sensitivity analysis suggested a lower bound of @math 139,000). Conclusions Motion artifacts represent a frequent cause of MR image degradation, particularly for inpatient and emergency department patients, resulting in substantial costs to the radiology department. Greater attention and resources should be directed toward providing practical solutions to this dilemma.