[论文解读] Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction

Florian Knöll, Kerstin Hammernik+5 more|arXiv (Cornell University)|Apr 1, 2019

Advanced MRI Techniques and Applications参考文献 75被引用 37

一句话总结

本文综述深度学习与神经网络在并行MRI重建中的应用，涵盖像域与k-space方法，重点关注多线圈数据与学习的正则化/导航欠采样伪影。

ABSTRACT

Following the success of deep learning in a wide range of applications, neural network-based machine learning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI). A number of ideas inspired by deep learning techniques from computer vision and image processing have been successfully applied to non-linear image reconstruction in the spirit of compressed sensing for both low dose computed tomography and accelerated MRI. The additional integration of multi-coil information to recover missing k-space lines in the MRI reconstruction process, is still studied less frequently, even though it is the de-facto standard for currently used accelerated MR acquisitions. This manuscript provides an overview of the recent machine learning approaches that have been proposed specifically for improving parallel imaging. A general background introduction to parallel MRI is given that is structured around the classical view of image space and k-space based methods. Both linear and non-linear methods are covered, followed by a discussion of recent efforts to further improve parallel imaging using machine learning, and specifically using artificial neural networks. Image-domain based techniques that introduce improved regularizers are covered as well as k-space based methods, where the focus is on better interpolation strategies using neural networks. Issues and open problems are discussed as well as recent efforts for producing open datasets and benchmarks for the community.

研究动机与目标

• 解释多线圈并行MRI的基本原理及其重建挑战。
• 综述线性和非线性传统方法（SENSE、GRAPPA、SPIRiT）及其在欠采样下的局限性。
• 介绍并评估用于图像域和k-space并行MRI重建的机器学习框架。
• 突出未解决问题、数据集和基准测试，以加速ML驱动的并行成像。

提出的方法

• 讨论图像域与k-space并行MRI重建的表述。
• 描述非线性正则化和压缩感知，作为基于ML的方法基础。
• 提出受神经网络启发的展开式迭代方案用于图像域重建（变分网络、学习的正则化器）。
• 解释基于扫描特异和数据库驱动的k-space插值方法（RAKI、DeepSPIRiT 等）。
• 总结基于低秩/Hankel以及核方法的学习方法（SAKE、LORAKS、ALOHA 等）。

实验结果

研究问题

• RQ1 What machine learning strategies can improve parallel MRI reconstruction beyond classical regularizers?
• RQ2 How do image-domain and k-space ML approaches compare in handling undersampling and coil data?
• RQ3 Can learned models achieve artifact suppression with comparable or better quality than CG-SENSE/GRAPPA under various sampling schemes?
• RQ4 What are the practical considerations (data requirements, calibration, computation) for ML-based parallel imaging?
• RQ5 What open datasets and benchmarks exist to evaluate ML methods in parallel MRI?

主要发现

• ML-based image-domain reconstructions can outperform traditional CG-SENSE and TGV-based methods in artifact suppression and feature preservation (higher SSIM in reported comparisons).
• K-space learned interpolation methods (e.g., RAKI, DeepSPIRiT) reduce noise amplification compared with GRAPPA/SPIRiT, though scan-specific calibration introduces trade-offs.
• Unrolled neural networks mirror classic iterative reconstruction, enabling end-to-end learning with data fidelity and learned regularizers.
• Database-trained and scan-specific CNNs can interpolate missing k-space lines without full calibration data, enabling flexible handling of diverse acquisitions.
• Learning-based methods can leverage multi-coil data to achieve improved reconstruction quality and potentially faster inference once trained.