用生成模型从仅有幅值的MRI图像恢复相位，用于加速成像重建训练。
Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models
- 基于条件得分扩散模型，从幅值图像生成匹配的相位图。
- 用合成数据训练的重建模型在定量指标和图像质量上优于其他方法。
- 适合缺乏原始k空间数据但有大量幅值图像的医疗机构使用。
加速磁共振成像依赖深度学习重建模型的训练，需大量多样化的原始k空间数据。临床实践中，因存储与隐私问题，通常仅保存幅值图像，导致多数研究受限于小规模数据集或少数公开k空间数据集。随着匿名幅值图像数据库增多，亟需技术利用这些数据训练通用深度学习重建模型。本文提出一种基于条件得分扩散模型（SBDM）的生成方法：给定一幅幅值图像，生成与其匹配的图像域相位图。我们评估其在下游任务中的表现——将合成相位图与对应幅值图像组合生成大规模k空间数据集，并用于训练加速MRI重建模型。对比采用平滑相位、生成对抗网络生成相位以及真实k空间数据的训练结果，发现基于SBDM合成数据训练的模型在定量指标和定性重建保真度上均更优，未出现错误或幻觉特征，有助于保障诊断准确性。
原文摘要 · Abstract (English)
Accelerated magnetic resonance imaging (MRI) enabled by the training of deep learning (DL)-based image recon. models requires large and diverse raw k-space datasets. In most clinical MRI applications, due to storage and patient privacy concerns, raw k-space data is discarded and magnitude-only images are the only component saved. Consequently, a large portion of the DL-based MRI recon. literature has either relied on small training datasets or has used one of the few available open-source k-space datasets. At the same time, the growing number of anonymized magnitude-only image registries/databases motivates the development of techniques that can use them as training datasets for generalizable DL-based recon. models. Here we propose to address this challenge by employing a generative approach based on conditional score-based diffusion models (SBDMs): given a magnitude-only MR image, it synthesizes a phase map (in the image domain) that realistically corresponds to the magnitude-only image. We evaluate its generative capabilities in a downstream DL-based recon. task whereby a large k-space dataset is generated by combining the SBDM-synthesized phase-maps and the corresponding magnitude-only images, and this k-space dataset is then used to train a DL model for accelerated MRI recon. We compare the performance of the resulting DL model versus those trained according to (a) a naive approach that uses smooth phase, (b) a k-space training dataset generated using synthesized phase maps derived from a generative adversarial network, and (c) the ground truth k-space data. Our results suggest that the DL model trained from SBDM-synthesized k-space data outperforms the other approaches in terms of quantitative metrics as well as qualitatively observed recon. fidelity, i.e., whether the reconstructed images include erroneous or hallucinated features that could adversely impact diagnostic accuracy.
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