Parallel qMRI Reconstruction from 4x Accelerated Acquisitions

TL;DR

This paper introduces a deep learning framework for accelerated parallel MRI reconstruction that jointly estimates coil sensitivity maps and reconstructs images from undersampled data at 4x acceleration, aiming to reduce scan times.

Contribution

It presents an end-to-end two-module deep learning architecture that improves MRI reconstruction quality from highly undersampled data, addressing limitations of traditional methods like SENSE.

Findings

Produces smoother reconstructions compared to conventional SENSE.

Achieves comparable visual quality despite lower PSNR/SSIM metrics.

Identifies challenges like spatial misalignment and suggests future improvements.

Abstract

Magnetic Resonance Imaging (MRI) acquisitions require extensive scan times, limiting patient throughput and increasing susceptibility to motion artifacts. Accelerated parallel MRI techniques reduce acquisition time by undersampling k-space data, but require robust reconstruction methods to recover high-quality images. Traditional approaches like SENSE require both undersampled k-space data and pre-computed coil sensitivity maps. We propose an end-to-end deep learning framework that jointly estimates coil sensitivity maps and reconstructs images from only undersampled k-space measurements at 4x acceleration. Our two-module architecture consists of a Coil Sensitivity Map (CSM) estimation module and a U-Net-based MRI reconstruction module. We evaluate our method on multi-coil brain MRI data from 10 subjects with 8 echoes each, using 2x SENSE reconstructions as ground truth. Our approach produces visually smoother reconstructions compared to conventional SENSE output, achieving comparable visual quality despite lower PSNR/SSIM metrics. We identify key challenges including spatial misalignment between different acceleration factors and propose future directions for improved reconstruction quality.