Parallel qMRI Reconstruction from 4x Accelerated Acquisitions

2025-11-22

AI Summary

Long MRI acquisition times hinder clinical efficiency and exacerbate motion artifacts. Conventional parallel imaging techniques (e.g., SENSE) rely on pre-acquired coil sensitivity maps, entailing complex calibration procedures and susceptibility to spatial misalignment. This paper proposes an end-to-end deep learning framework that jointly estimates coil sensitivity maps and reconstructs images directly from 4× undersampled multi-channel k-space data—eliminating the need for separate calibration scans. To our knowledge, this is the first method enabling joint sensitivity and image learning from a single undersampled acquisition. The architecture features two co-optimized branches: a sensitivity estimation module and a U-Net-based reconstruction module. Evaluated on brain MRI data from 10 subjects, the method yields reconstructions with visual quality comparable to SENSE; although PSNR and SSIM are marginally lower, it achieves substantially improved robustness and clinical practicality.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• Reducing MRI scan times through 4x accelerated parallel acquisitions
• Reconstructing high-quality images from undersampled k-space measurements
• Eliminating need for pre-computed coil sensitivity maps

Innovation

• Jointly estimates coil sensitivity maps and images
• Uses two-module deep learning architecture
• Reconstructs from only undersampled k-space measurements