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.
@cite_12 proposed a joint reconstruction and motion correction technique to iteratively search for motion trajectory. Gradient-based optimization approach has been opted to efficiently explore the search space. The same authors extended their work in @cite_16 by disintegrating the image into small windows that contain local rigid motion and used their own forward model to construct an objective function that optimizes the unknown motion parameters. Similarly, @cite_20 proposed the use of a forward model to correct motion artifacts. However, this technique utilises the full reconstruction inverse to integrate the information of multi-coils for estimation and correction of motion. In another study @cite_9 , authors extended their framework to correct three-dimensional motion (i.e., in-plane and through-plane motion). Through the plane, the motion is corrected by sampling the slices in overlapped manner.
@cite_9 @cite_16 @cite_20 @cite_12
2626006036 1754483689 2297651635 2160857949
Purpose To introduce a methodology for the reconstruction of multi-shot, multi-slice magnetic resonance imaging able to cope with both within-plane and through-plane rigid motion and to describe its application in structural brain imaging. Theory and Methods The method alternates between motion estimation and reconstruction using a common objective function for both. Estimates of three-dimensional motion states for each shot and slice are gradually refined by improving on the fit of current reconstructions to the partial k-space information from multiple coils. Overlapped slices and super-resolution allow recovery of through-plane motion and outlier rejection discards artifacted shots. The method is applied to T2 and T1 brain scans acquired in different views. Results The procedure has greatly diminished artifacts in a database of 1883 neonatal image volumes, as assessed by image quality metrics and visual inspection. Examples showing the ability to correct for motion and robustness against damaged shots are provided. Combination of motion corrected reconstructions for different views has shown further artifact suppression and resolution recovery. Conclusion The proposed method addresses the problem of rigid motion in multi-shot multi-slice anatomical brain scans. Tests on a large collection of potentially corrupted datasets have shown a remarkable image quality improvement. Magn Reson Med, 2017. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Purpose Physiological nonrigid motion is inevitable when imaging, e.g., abdominal viscera, and can lead to serious deterioration of the image quality. Prospective techniques for motion correction can handle only special types of nonrigid motion, as they only allow global correction. Retrospective methods developed so far need guidance from navigator sequences or external sensors. We propose a fully retrospective nonrigid motion correction scheme that only needs raw data as an input. Methods Our method is based on a forward model that describes the effects of nonrigid motion by partitioning the image into patches with locally rigid motion. Using this forward model, we construct an objective function that we can optimize with respect to both unknown motion parameters per patch and the underlying sharp image. Results We evaluate our method on both synthetic and real data in 2D and 3D. In vivo data was acquired using standard imaging sequences. The correction algorithm significantly improves the image quality. Our compute unified device architecture (CUDA)-enabled graphic processing unit implementation ensures feasible computation times. Conclusion The presented technique is the first computationally feasible retrospective method that uses the raw data of standard imaging sequences, and allows to correct for nonrigid motion without guidance from external motion sensors. Magn Reson Med 73:1457–1468, 2015. © 2014 Wiley Periodicals, Inc. This paper introduces a framework for the reconstruction of magnetic resonance images in the presence of rigid motion. The rationale behind our proposal is to make use of the partial @math -space information provided by multiple receiver coils in order to estimate the position of the imaged object throughout the shots that contribute to the image. The estimated motion is incorporated into the reconstruction model in an iterative manner to obtain a motion-free image. The method is parameter-free, does not assume any prior model for the image to be reconstructed, avoids blurred images due to resampling, does not make use of external sensors, and does not require modifications in the acquisition sequence. Validation is performed using synthetically corrupted data to study the limits for full motion-recovered reconstruction in terms of the amount of motion, encoding trajectories, number of shots and availability of prior information, and to compare with the state of the art. Quantitative and visual results of its application to a highly challenging volumetric brain imaging cohort of @math neonates are also presented, showing the ability of the proposed reconstruction to generally improve the quality of reconstructed images, as evaluated by both sparsity and gradient entropy based metrics. Purpose Subject motion can severely degrade MR images. A retrospective motion correction algorithm, Gradient-based motion correction, which significantly reduces ghosting and blurring artifacts due to subject motion was proposed. The technique uses the raw data of standard imaging sequences; no sequence modifications or additional equipment such as tracking devices are required. Rigid motion is assumed. Methods The approach iteratively searches for the motion trajectory yielding the sharpest image as measured by the entropy of spatial gradients. The vast space of motion parameters is efficiently explored by gradient-based optimization with a convergence guarantee. Results The method has been evaluated on both synthetic and real data in two and three dimentions using standard imaging techniques. MR images are consistently improved over different kinds of motion trajectories. Using a graphics processing unit implementation, computation times are in the order of a few minutes for a full three-dimentional volume. Conclusion The presented technique can be an alternative or a complement to prospective motion correction methods and is able to improve images with strong motion artifacts from standard imaging sequences without requiring additional data. Magn Reson Med 70:1608–1618, 2013. © 2013 Wiley Periodicals, Inc.