Positive Contrast Susceptibility MR Imaging Using GPU-based Primal-Dual Algorithm

TL;DR

This paper introduces a GPU-accelerated primal-dual algorithm for positive contrast susceptibility MRI, significantly reducing computation time while maintaining accuracy in imaging metallic devices.

Contribution

The paper presents a novel GPU-based primal-dual algorithm that accelerates positive contrast MRI reconstruction, achieving 4-15 times faster performance than CPU-based methods.

Findings

GPU-based algorithm achieves comparable accuracy to CPU-based methods.

Significant reduction in computational time (4-15x faster).

Effective for imaging metallic interventional devices.

Abstract

The susceptibility-based positive contrast MR technique was applied to estimate arbitrary magnetic susceptibility distributions of the metallic devices using a kernel deconvolution algorithm with a regularized L-1 minimization.Previously, the first-order primal-dual (PD) algorithm could provide a faster reconstruction time to solve the L-1 minimization, compared with other methods. Here, we propose to accelerate the PD algorithm of the positive contrast image using the multi-core multi-thread feature of graphics processor units (GPUs). The some experimental results showed that the GPU-based PD algorithm could achieve comparable accuracy of the metallic interventional devices in positive contrast imaging with less computational time. And the GPU-based PD approach was 4~15 times faster than the previous CPU-based scheme.