用自监督深度学习提升肾肿瘤微结构成像精度
Dual Deep Learning Approach for Non-invasive Renal Tumour Subtyping with VERDICT-MRI
- 基于VERDICT-MRI框架，用自监督神经网络拟合扩散MRI数据
- 4个b值组合仅需14分钟，准确区分癌变与正常组织的细胞内体积分数
- 适合对肾肿瘤无创分型有需求的临床与影像研究者
本研究旨在利用扩散MRI（dMRI）表征肾肿瘤微结构，采用血管-细胞外-受限扩散用于肿瘤细胞计数（VERDICT）-MRI框架，并结合自监督学习。从14名患者共15个经活检证实的肾肿瘤中获取了包含9个b值（b=[0,2500]s/mm²）的全面数据集。通过自监督深度神经网络拟合三室VERDICT模型，由经验泌尿放射科医生勾画感兴趣区域（ROIs）。采用递归特征选择优化未来大样本研究的经济化扫描协议。结果显示，VERDICT模型对肾肿瘤扩散数据的描述优于IVIM或ADC。结合自监督学习，VERDICT成功识别出癌变组织与正常组织间显著差异的细胞内体积分数，以及血管性与非血管性肿瘤间血管体积分数的差异。特征选择得到最优4个b值方案：b = [70,150,1000,2000]，总扫描时间仅14分钟。
原文摘要 · Abstract (English)
This work aims to characterise renal tumour microstructure using diffusion MRI (dMRI); via the Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumours (VERDICT)-MRI framework with self-supervised learning. Comprehensive datasets were acquired from 14 patients with 15 biopsy-confirmed renal tumours, with nine b-values in the range b=[0,2500]s/mm2. A three-compartment VERDICT model for renal tumours was fitted to the dMRI data using a self-supervised deep neural network, and ROIs were drawn by an experienced uroradiologist. An economical acquisition protocol for future studies with larger patient cohorts was optimised using a recursive feature selection approach. The VERDICT model described the diffusion data in renal tumours more accurately than IVIM or ADC. Combined with self-supervised deep learning, VERDICT identified significant differences in the intracellular volume fraction between cancerous and normal tissue, and in the vascular volume fraction between vascular and non-vascular. The feature selector yields a 4 b-value acquisition of b = [70,150,1000,2000], with a duration of 14 minutes.
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