Motion-adaptive spatio-temporal regularization for accelerated dynamic MRI

Abstract

Accelerated magnetic resonance imaging techniques reduce signal acquisition time by undersampling k-space. A fundamental problem in accelerated magnetic resonance imaging is the recovery of quality images from undersampled k-space data. Current state-of-the-art recovery algorithms exploit the spatial and temporal structures in underlying images to improve the reconstruction quality. In recent years, compressed sensing theory has helped formulate mathematical principles and conditions that ensure recovery of (structured) sparse signals from undersampled, incoherent measurements. In this article, a new recovery algorithm, motion-adaptive spatio-temporal regularization, is presented that uses spatial and temporal structured sparsity of MR images in the compressed sensing framework to recover dynamic MR images from highly undersampled k-space data. In contrast to existing algorithms, our proposed algorithm models temporal sparsity using motion-adaptive linear transformations between neighboring images. The efficiency of motion-adaptive spatio-temporal regularization is demonstrated with experiments on cardiac magnetic resonance imaging for a range of reduction factors. Results are also compared with k-t FOCUSS with motion estimation and compensation-another recently proposed recovery algorithm for dynamic magnetic resonance imaging. .

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

“…Since at the very beginning of the algorithm we do not have any data from which motion information can be estimated, we have resorted to the common approach (14,15,17) of making use of a regular CS reconstruction prior step; then an iterative procedure consisting in a ME/MC step followed by a MC-CS reconstruction step, is adopted. In our implementation a predefined number of iterations has been set; it is very simple to use some alternative convergence criterion instead.…”

“…The reference frame may not be available and the final reconstruction result depends heavily on its quality (17). In MASTeR (17), motion is estimated sequentially between each pair of consecutive frames.…”

“…The reference frame may not be available and the final reconstruction result depends heavily on its quality (17). In MASTeR (17), motion is estimated sequentially between each pair of consecutive frames. In both approaches only two frames of the sequence are available for the ME algorithm at each execution, therefore the ME algorithm cannot benefit from the additional information present in the rest of the frames making it more sensitive to artifacts in the sequence.…”

Accelerated Dynamic Cardiac MRI Using Motion-Guided Compressed Sensing with Regional Sparsity

“…The dynamics-simplified images now present contrast changes with little or no motion, and eases the job of the sparsifying transform, as illustrated in Figure 1.4. The motion-compensation has been successfully combined with sparsity transform such as temporal difference (20)(21)(22)) and x-f transform (16,23). One drawback of these motion-compensated CS methods is that the quality of the reconstruction relies heavily on the accuracy of the motion estimation.…”

“…Another approach is to compensate the image dataset for motion and then apply a CS sparsity transform to the motioncompensated data, such as in k-t FOCUSS with motion estimation and compensation (16) We sought to develop and evaluate a CS method for first-pass contrast-enhanced cardiac perfusion MRI that combines the advantages of data-driven spatiotemporal basis functions and regional motion tracking. Specifically, we propose a method that divides images into regions, 22 tracks the regions over time, and applies SVD to the tracked regions. Using this approach, our method can both account for regional non-periodic variations in motion and can exploit regional spatiotemporal sparsity.…”