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.

翻译：扩散磁共振成像（dMRI）能够无创评估前列腺微结构，但传统dMRI指标（如多参数MRI中的表观扩散系数）反映的是潜在组织特征的混合信息，而非明确的组织学特性。将dMRI与基于腔室的生物物理VERDICT（肿瘤血管、细胞外及受限扩散细胞计量学）框架相结合，可提供更丰富的微结构信息，然而临床梯度系统（40-80 mT/m）在较强扩散加权下常因回波时间延长而面临信噪比低的问题。超强梯度系统（如300 mT/m）通过提升信噪比与对比噪声比，能够缓解这些局限性。本研究探讨了结合物理信息的自监督VERDICT（ssVERDICT）拟合方法在与超强梯度数据联合使用时，相较于现有拟合方法与临床梯度系统，能否提升前列腺微结构表征性能。我们开发了基于密集多层感知机与卷积U-Net架构的增强型ssVERDICT拟合方法，并将其与非线性最小二乘VERDICT拟合、原始ssVERDICT实现及扩散峰度成像在临床至超强梯度系统范围内进行比较。对于相同的超强梯度数据，密集ssVERDICT优于非线性最小二乘VERDICT，将中值对比噪声比提升47%，将患者间变异系数降低52%，并将汇总的$f_{ic}$变异减少50%。总体而言，相较于传统拟合方法与临床梯度系统，密集ssVERDICT实现了最高的对比噪声比、最稳定的参数估计以及最清晰的肿瘤-正常组织对比。这些发现表明，先进梯度系统与基于深度学习的建模方法相结合，能为无创前列腺癌表征带来显著提升。