Variable Resolution Sampling and Deep Learning Image Recovery for Accelerated Multi-Spectral MRI Near Metal Implants

TL;DR

This paper introduces a variable resolution sampling combined with deep learning reconstruction to improve multi-spectral MRI near metal implants, significantly reducing scan times while maintaining high image quality.

Contribution

It proposes a novel spectral undersampling scheme and deep learning method that enhance MRI efficiency and image quality near metal implants, outperforming conventional techniques.

Findings

Deep learning reconstructions achieved higher SSIM and PSNR than conventional methods.

Edge sharpness in deep learning images matched fully sampled references.

Scan time was reduced by approximately 40% with maintained image quality.

Abstract

Purpose: This study presents a variable resolution (VR) sampling and deep learning reconstruction approach for multi-spectral MRI near metal implants, aiming to reduce scan times while maintaining image quality. Background: The rising use of metal implants has increased MRI scans affected by metal artifacts. Multi-spectral imaging (MSI) reduces these artifacts but sacrifices acquisition efficiency. Methods: This retrospective study on 1.5T MSI knee and hip data from patients with metal hardware used a novel spectral undersampling scheme to improve acquisition efficiency by ~40%. U-Net-based deep learning models were trained for reconstruction. Image quality was evaluated using SSIM, PSNR, and RESI metrics. Results: Deep learning reconstructions of undersampled VR data (DL-VR) showed significantly higher SSIM and PSNR values (p<0.001) compared to conventional reconstruction (CR-VR), with improved edge sharpness. Edge sharpness in DL-reconstructed images matched fully sampled references (p=0.5). Conclusion: This approach can potentially enhance MRI examinations near metal implants by reducing scan times or enabling higher resolution. Further prospective studies are needed to assess clinical value.