用扩散模型实现无需知道采样模式的MRI快速重建
Sampling-Pattern-Agnostic MRI Reconstruction through Adaptive Consistency Enforcement with Diffusion Model
- 通过自适应一致性约束，让扩散模型不依赖采样模式进行重建
- 在心脏MRI数据集上跨对比度、加速因子表现优于基线方法
- 适合需要通用化重建方案的临床MRI研究者
磁共振成像（MRI）是一种强大的无创诊断工具，但其临床应用受限于漫长的扫描时间。尽管现有基于深度学习的方法在加速MRI方面展现出潜力，但通常依赖已知的采样模式，且对新采样模式泛化能力有限。本文提出一种基于扩散模型的采样模式无关的MRI重建方法，通过自适应一致性约束实现高保真图像重建，适用于不同欠采样采集方式，跨对比度和加速因子均表现良好。我们在MICCAI 2024心脏MRI重建挑战赛（CMRxRecon）数据集的所有对比度上进行了训练与验证，任务为随机采样心脏MRI重建。评估结果表明，所提方法显著优于基线方法。
原文摘要 · Abstract (English)
Magnetic Resonance Imaging (MRI) is a powerful, non-invasive diagnostic tool; however, its clinical applicability is constrained by prolonged acquisition times. Whilst present deep learning-based approaches have demonstrated potential in expediting MRI processes, these methods usually rely on known sampling patterns and exhibit limited generalisability to novel patterns. In the paper, we propose a sampling-pattern-agnostic MRI reconstruction method via a diffusion model through adaptive consistency enforcement. Our approach effectively reconstructs high-fidelity images with varied under-sampled acquisitions, generalising across contrasts and acceleration factors regardless of sampling trajectories. We train and validate across all contrasts in the MICCAI 2024 Cardiac MRI Reconstruction Challenge (CMRxRecon) dataset for the ``Random sampling CMR reconstruction'' task. Evaluation results indicate that our proposed method significantly outperforms baseline methods.
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