用超强梯度+自监督模型，提升前列腺癌微观结构成像精度
Ultra-Strong Gradient Diffusion MRI with Self-Supervised Learning for Prostate Cancer Characterization
- 结合超强梯度与自监督深度学习优化生物物理模型拟合
- 相比传统方法，信噪比提升47%，参数波动降低52%
- 适合追求高精度无创前列腺癌诊断的研究者和临床团队
扩散MRI（dMRI）可无创评估前列腺微结构，但传统指标如多参数MRI中的表观扩散系数反映的是多种组织特征的混合，并非明确的组织学特征。将dMRI与基于隔室的生物物理VERDICT（Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumours）框架结合，可提供更丰富的微结构信息。然而，临床梯度系统（40–80 mT/m）在强扩散加权下常因延长回波时间导致信噪比下降。使用超强梯度（如300 mT/m）可改善信噪比和对比噪声比。本研究探讨了在超强梯度数据下，融合物理先验的自监督VERDICT（ssVERDICT）拟合是否优于现有方法及临床梯度系统。我们采用密集多层感知机与卷积U-Net架构改进ssVERDICT拟合方法，对比非线性最小二乘（NLLS）VERDICT、原始ssVERDICT及扩散峰度成像，在临床至超强梯度系统中进行评估。对于相同超强梯度数据，密集ssVERDICT优于NLLS VERDICT，使中位对比噪声比提升47%，患者间变异系数降低52%，合并$ f_{ic} $ 变异减少50%。总体而言，密集ssVERDICT在对比噪声比、参数稳定性及肿瘤-正常组织对比度方面均最优。结果表明，先进梯度系统与基于深度学习的建模相结合，能显著提升无创前列腺癌表征能力。
原文摘要 · Abstract (English)
Diffusion MRI (dMRI) enables non-invasive assessment of prostate microstructure but conventional dMRI metrics such as the Apparent Diffusion Coefficient in multiparametric MRI and reflect a mixture of underlying tissues features rather than distinct histologic characteristics. Integrating dMRI with the compartment-based biophysical VERDICT (Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumours) framework offers richer microstructural insights, though clinical gradient systems (40-80 mT/m) often suffer from poor signal-to-noise ratio at stronger diffusion weightings due to prolonged echo times. Ultra-strong gradients (e.g., 300 mT/m) can mitigate these limitations by improving SNR and contrast-to-noise ratios. This study investigates whether physics-informed self-supervised VERDICT (ssVERDICT) fitting when combined with ultra-strong gradient data, enhances prostate microstructural characterization relative to current fitting approaches and clinical gradient systems. We developed enhanced ssVERDICT fitting approaches using dense multilayer perceptron and convolutional U-Net architectures, comparing them against non-linear least-squares (NLLS) VERDICT fitting, original ssVERDICT implementation, and Diffusion Kurtosis Imaging across clinical- to ultra-strong gradient systems. For the same ultra-strong gradient data, Dense ssVERDICT outperformed NLLS VERDICT, boosting median CNR by 47%, cutting inter-patient Coefficient of Variation by 52%, and reducing pooled $f_{ic}$ variation by 50%. Overall, Dense ssVERDICT delivered the highest CNR, the most stable parameter estimates, and the clearest tumour-normal contrast compared with conventional fitting methods and clinical gradient systems. These findings underscore that meaningful gains in non-invasive prostate cancer characterization arise from the combination of advanced gradient systems and deep learning-based modelling.
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