用物理模型指导的自编码器，提升脑胶质瘤灌注成像分析的准确性和鲁棒性。
Physics-Informed autoencoder for DSC-MRI Perfusion post-processing: application to glioma grading
- 融合解析模型的自监督学习方法，无需依赖第三方算法生成标签。
- 在高噪声环境下仍保持稳定性能，计算耗时低于传统方法。
- 适用于临床场景，尤其适合处理含伪影的医学影像数据。
DSC-MRI灌注成像是诊断和预后评估脑肿瘤与中风的重要医学影像技术，其分析依赖数学去卷积，但临床环境中的噪声或运动伪影会破坏该过程，导致灌注参数估计错误。尽管深度学习方法表现良好，但其训练通常依赖第三方去卷积算法生成参考输出，受限于这些算法的缺陷。为此，本文提出一种物理信息自编码器，利用解析模型解码灌注参数，并引导编码网络学习。该自编码器采用自监督方式训练，无需任何第三方软件，其性能在胶质瘤患者数据库上进行了评估。结果表明，本方法在胶质瘤分级上表现可靠，与已有知名去卷积算法一致，同时计算时间更低；且在高噪声条件下仍具竞争力，这对医疗环境至关重要。
原文摘要 · Abstract (English)
DSC-MRI perfusion is a medical imaging technique for diagnosing and prognosing brain tumors and strokes. Its analysis relies on mathematical deconvolution, but noise or motion artifacts in a clinical environment can disrupt this process, leading to incorrect estimate of perfusion parameters. Although deep learning approaches have shown promising results, their calibration typically rely on third-party deconvolution algorithms to generate reference outputs and are bound to reproduce their limitations. To adress this problem, we propose a physics-informed autoencoder that leverages an analytical model to decode the perfusion parameters and guide the learning of the encoding network. This autoencoder is trained in a self-supervised fashion without any third-party software and its performance is evaluated on a database with glioma patients. Our method shows reliable results for glioma grading in accordance with other well-known deconvolution algorithms despite a lower computation time. It also achieved competitive performance even in the presence of high noise which is critical in a medical environment.
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