多对比MRI任务导向加速的解剖感知深度展开。

Yuzhu He;Chunfeng Lian;Ruyi Xiao;Fangmao Ju;Chao Zou;Zongben Xu;Jianhua Ma
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引用次数: 0

摘要

多层对比磁共振成像(MC-MRI)在临床实践中起着至关重要的作用。然而,它的性能受到扫描时间长和图像采集与下游临床诊断/治疗之间的隔离的阻碍。尽管对加速MC-MRI的研究已被激活,但很少有现有的研究优先考虑针对个体患者特征和临床需求量身定制的个性化成像。也就是说,目前的方法通常旨在提高整体图像质量,而忽略了临床医生特别感兴趣的特定病理或解剖区域。为了应对这一挑战,我们提出了一种基于解剖感知展开的深度网络,称为A2MC-MRI,为满足下游临床需求的快速MC-MRI提供了有希望的可解释性和学习能力。该网络由面向任务的MC-MRI重构模型的迭代算法展开。具体来说,为了增强特定感兴趣目标(toi)的并发MC-MRI,该模型将可学习的组稀疏性与解剖感知的去噪先验相结合。在解剖感知去噪先验中,涉及到一个分割网络,为toi增强去噪提供关键的位置信息。最后,将这种展开网络与k空间采样模式联合学习,用于面向任务的MC-MR重构。对两个公共基准以及内部数据集的综合评估表明,我们的A2MCMRI在高加速率下的MC-MRI重建中具有最先进的性能,并显著提高了TOI成像质量。代码可在https://github.com/ladderlab-xjtu/A2MC-MRI上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Anatomy-Aware Deep Unrolling for Task-Oriented Acceleration of Multi-Contrast MRI
Multi-contrast magnetic resonance imaging (MC-MRI) plays a crucial role in clinical practice. However, its performance is hindered by long scanning times and the isolation between image acquisition and downstream clinical diagnoses/treatments. Despite the activated research on accelerated MC-MRI, few existing studies prioritize personalized imaging tailored to individual patient characteristics and clinical needs. That is, the current approach often aims to enhance overall image quality, disregarding the specific pathologies or anatomical regions that are of particular interest to clinicians. To tackle this challenge, we propose an anatomy-aware unrolling-based deep network, dubbed as $\text {A}^{{2}}$ MC-MRI, offering promising interpretability and learning capacity for fast MC-MRI catering to downstream clinical needs. The network is unfolded from the iterative algorithm designed for a task-oriented MC-MRI reconstruction model. Specifically, to enhance concurrent MC-MRI of specific targets of interest (TOIs), the model integrates a learnable group sparsity with an anatomy-aware denoising prior. Within the anatomy-aware denoising prior, a segmentation network is involved to provide critical location information for TOI-enhanced denoising. Finally, such an unrolled network is jointly learned with k-space sampling patterns for task-oriented MC-MR reconstruction. Comprehensive evaluations on two public benchmarks as well as an in-house dataset demonstrate that our ${A}^{{2}}$ MC-MRI led to state-of-the-art performance in MC-MRI reconstruction under high acceleration rates, featuring notable enhancements in TOI imaging quality. The code will be available at https://github.com/ladderlab-xjtu/A2MC-MRI
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