Groupwise Elastic Registration by a New Sparsity-Promoting Metric: Application to the Alignment of Cardiac Magnetic Resonance Perfusion Images

IEEE Trans. Comput. Imaging

Teixeira

Hughes

et al. 2016

155

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 k-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 207 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.

Section: Discussionmentioning

confidence: 99%

Sensitivity Encoding for Aligned Multishot Magnetic Resonance Reconstruction

IEEE Trans. Comput. Imaging

Teixeira

Hughes

et al. 2016

155

“…is minimized (18). The solution is constrained to those sets of transformations with the average deformation of each position equal to the identity transformation (23):…”

Section: Temporal Groupwise Registration For Me/mcmentioning

confidence: 99%

Nonrigid groupwise registration for motion estimation and compensation in compressed sensing reconstruction of breath‐hold cardiac cine MRI

Royuela-del-Val

Magnetic Resonance in Med

Simmross-Wattenberg

et al. 2015

Theory and Methods: A spatio-temporal regularized, groupwise, non-rigid registration method based on a B-splines deformation model and a least squares metric is used to estimate and to compensate the movement of the heart in breath-hold cine acquisitions and to obtain a quasi-static sequence with highly sparse representation in temporally transformed domains.Results: Short axis in vivo datasets are used for validation, both original multi-coil as well as DICOM data. Fully sampled data were retrospectively undersampled with various acceleration factors and reconstructions were compared with the two well-known methods k-t FOCUSS and MASTeR. The proposed method achieves higher signal to error ratio and structure similarity index for medium to high acceleration factors. Conclusions:Reconstruction methods based on groupwise registration show higher quality reconstructions for cardiac cine images than the pairwise counterparts tested.

“…The SSD metric relies on the assumption that the intensity of each pixel is preserved along time. Other metrics can be incorporated for different imaging modalities where such a condition is not met [9]. The deformation model used in the GW registration algorithm is based on a non-rigid 2D+t free form deformations (FFD) with cubic B-splines, which has been widely used in practice and has shown to be flexible enough to describe the motion and deformation of anatomical structures [8].…”

Section: Groupwise Temporal Registration For Me/mcmentioning

confidence: 99%

Multiresolution reconstruction of real-time MRI with motion compensated compressed sensing: Application to 2D free-breathing cardiac MRI

Royuela-del-Val

Usman

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

et al. 2016

Self Cite

Real-time MRI is a novel noninvasive imaging technique that allows the visualization of physiological processes with both good spatial and temporal resolutions. However, the reconstruction of images from highly undersampled data, needed to perform real-time imaging, remains challenging. Recently, the combination of Compressed Sensing theory with motion compensation techniques has shown to achieve better results than previous methods. In this work we describe a real-time MRI algorithm based on the acquisition of the k-space data following a Golden Radial trajectory, Compressed Sensing reconstruction and a groupwise temporal registration algorithm for the estimation and compensation of the motion in the image, all this embedded within a temporal multiresolution scheme. We have applied the proposed method to the reconstruction of free-breathing acquisition of short axis views of the heart, achieving a temporal resolution of 25ms, corresponding to an acceleration factor of 28 with respect to fully sampled Cartesian acquisitions.