e-space
Manchester Metropolitan University's Research Repository

Accelerated Dynamic MRI Using Kernel-Based Low Rank Constraint

Arif, Omar and Afzal, Hammad and Abbas, Haider and Amjad, Muhammad Faisal and Wan, Jiafu and Nawaz, Raheel (2019) Accelerated Dynamic MRI Using Kernel-Based Low Rank Constraint. Journal of Medical Systems, 43 (8). ISSN 0148-5598

[img]
Preview

Available under License Creative Commons Attribution.

Download (355kB) | Preview

Abstract

We present a novel reconstruction method for dynamic MR images from highly under-sampled k-space measurements. The reconstruction problem is posed as spectrally regularized matrix recovery problem, where kernel-based low rank constraint is employed to effectively utilize the non-linear correlations between the images in the dynamic sequence. Unlike other kernel-based methods, we use a single-step regularized reconstruction approach to simultaneously learn the kernel basis functions and the weights. The objective function is optimized using variable splitting and alternating direction method of multipliers. The framework can seamlessly handle additional sparsity constraints such as spatio-temporal total variation. The algorithm performance is evaluated on a numerical phantom and in vivo data sets and it shows significant improvement over the comparison methods.

Impact and Reach

Statistics

Downloads
Activity Overview
34Downloads
63Hits

Additional statistics for this dataset are available via IRStats2.

Altmetric

Actions (login required)

Edit Item Edit Item