4倍加速下直接从采样数据重建脑部MRI，无需预设线圈敏感度图。
Parallel qMRI Reconstruction from 4x Accelerated Acquisitions
- 端到端深度学习联合估计线圈敏感度与图像重建。
- 在10名受试者8个回波数据上实现视觉更平滑的重构。
- 适合需要快速成像且无预标定条件的临床场景。
磁共振成像（MRI）扫描耗时长，限制了患者通量并增加运动伪影风险。加速并行MRI通过欠采样k空间数据缩短采集时间，但需鲁棒重建方法恢复高质量图像。传统方法如SENSE需同时使用欠采样k空间数据和预先计算的线圈敏感度图（CSM）。我们提出一种端到端深度学习框架，仅从4倍加速下的欠采样k空间测量值中联合估计线圈敏感度图并重建图像。该两模块架构包含线圈敏感度图估计模块和基于U-Net的MRI重建模块。我们在10名受试者的多线圈脑部MRI数据（每例8个回波）上评估方法，以2倍SENSE重构结果作为真值。所提方法生成的重构图像视觉更平滑，尽管峰值信噪比（PSNR）和结构相似性（SSIM）略低，但仍达到可比视觉质量。我们识别出不同加速因子间空间错位等关键挑战，并提出未来改进方向。
原文摘要 · Abstract (English)
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.
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