用深度学习加速心脏核磁共振成像，提升多对比度图像质量。
Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement Network
- 结合vSHARP与辅助精修网络，实现多对比度动态图像重建。
- 在不同采样方案和加速度下，重建图像质量优于传统方法。
- 适合需要快速高精度心脏影像的临床研究与诊断场景。
心脏MRI（CMRI）是深入理解心脏结构与功能的重要影像技术。多对比度CMRI（MCCMRI）通过获取不同对比权重的序列，显著增强对心肌组织特性的捕捉能力，但受限于扫描时间长及运动伪影问题。为此，研究者提出基于深度学习的2D动态多对比度、多采样方案、多加速比MRI重建方法。该方法融合先进的vSHARP模型（利用半二次变量分裂与ADMM优化）与变分网络构成的辅助精修网络（ARN），将欠采样k-space数据输入ARN生成去噪初始预测，再结合原始数据由vSHARP生成高质量2D序列重建结果。实验表明，该方法在多种加速条件下均优于传统重建技术及现有vSHARP基线模型。
原文摘要 · Abstract (English)
Cardiac MRI (CMRI) is a cornerstone imaging modality that provides in-depth insights into cardiac structure and function. Multi-contrast CMRI (MCCMRI), which acquires sequences with varying contrast weightings, significantly enhances diagnostic capabilities by capturing a wide range of cardiac tissue characteristics. However, MCCMRI is often constrained by lengthy acquisition times and susceptibility to motion artifacts. To mitigate these challenges, accelerated imaging techniques that use k-space undersampling via different sampling schemes at acceleration factors have been developed to shorten scan durations. In this context, we propose a deep learning-based reconstruction method for 2D dynamic multi-contrast, multi-scheme, and multi-acceleration MRI. Our approach integrates the state-of-the-art vSHARP model, which utilizes half-quadratic variable splitting and ADMM optimization, with a Variational Network serving as an Auxiliary Refinement Network (ARN) to better adapt to the diverse nature of MCCMRI data. Specifically, the subsampled k-space data is fed into the ARN, which produces an initial prediction for the denoising step used by vSHARP. This, along with the subsampled k-space, is then used by vSHARP to generate high-quality 2D sequence predictions. Our method outperforms traditional reconstruction techniques and other vSHARP-based models.
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