通过稀疏一致性约束提升自监督MRI重建的精度与稳定性。
Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction
- 在k空间掩码基础上引入稀疏域扰动预测一致性项
- 高加速率下显著减少伪影，降低噪声放大
- 适合需要高质量无参考重建的医学影像研究者
基于物理规律的深度学习（PD-DL）模型在快速MRI重建中表现优异。为在缺乏全采样参考数据的情况下训练此类模型，自监督学习日益重要。然而，高加速率下常出现伪影，影响图像保真度。为此，本文提出一种新型训练策略，通过设计特定扰动增强传统自监督学习中的k空间掩码方法，引入新的稀疏域一致性项，评估模型对添加扰动的准确预测能力，从而实现更可靠、无伪影的重建。在fastMRI膝关节和脑部数据集上的实验表明，该方法有效抑制了混叠伪影，缓解了高加速率下的噪声放大问题，在视觉和定量指标上均优于现有自监督方法。
原文摘要 · Abstract (English)
Physics-driven deep learning (PD-DL) models have proven to be a powerful approach for improved reconstruction of rapid MRI scans. In order to train these models in scenarios where fully-sampled reference data is unavailable, self-supervised learning has gained prominence. However, its application at high acceleration rates frequently introduces artifacts, compromising image fidelity. To mitigate this shortcoming, we propose a novel way to train PD-DL networks via carefully-designed perturbations. In particular, we enhance the k-space masking idea of conventional self-supervised learning with a novel consistency term that assesses the model's ability to accurately predict the added perturbations in a sparse domain, leading to more reliable and artifact-free reconstructions. The results obtained from the fastMRI knee and brain datasets show that the proposed training strategy effectively reduces aliasing artifacts and mitigates noise amplification at high acceleration rates, outperforming state-of-the-art self-supervised methods both visually and quantitatively.
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