发表机构
Center for Image Sciences, University Medical Center Utrecht; Department of Radiotherapy, University Medical Center Utrecht; Cerebriu; Department of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet; Department of Diagnostic Radiology, Copenhagen University Hospital Herlev and Gentofte（乌得勒支大学医学中心影像科学中心; 乌得勒支大学医学中心放射治疗科; Cerebriu公司; 哥本哈根大学医院 Rigshospitalet 诊断放射科; 哥本哈根大学医院海尔鲁夫与根措夫特医院诊断放射科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究不同DWI预处理策略对前列腺MRI中ADC估计及PI-RADS分类的影响，通过对268个病例应用多种处理方法，比较不同算法下的ADC图，训练分类器预测PI-RADS分数，发现预处理可提升ADC图质量及分类预测能力。
AI 中文摘要
扩散加权成像（DWI）是双参数前列腺MRI的一部分，但存在会降低下游定量和诊断性能的伪影。虽然DWI预处理在脑成像中是标准操作，但在前列腺成像中的应用仍然有限且缺乏标准化流程。本研究调查了不同DWI预处理策略对表观扩散系数（ADC）估计和自动前列腺成像报告与数据系统（PI-RADS）分类的影响。通过依次应用去噪、吉布斯环校正和用于敏感性失真校正的微分同胚配准，从fastMRI前列腺队列中获取了268个病例。使用线性最小二乘法（LLS）和迭代加权LLS（IWLLS）比较ADC图。训练了一个3类DenseNet分类器，以从多通道MRI输入预测PI-RADS分数。ADC分析显示预处理流程之间存在统计学上的显著差异，LLS和IWLLS产生的数值等效图。大多数数据集之间ADC值的线性关系得以保留（PCC约为0.99），而失真校正将DWI重新对齐到T2w解剖结构并相应地改变了ADC值（PCC约为0.90）。分类显示在完全处理的数据集中，高危PI-RADS类别的AUROC和敏感性最佳。假阴性分析表明，该数据集对高危类别的过度自信错误预测最少，这是临床分类所需的特性。DWI预处理，特别是失真校正，提高了ADC图质量和深度学习模型对PI-RADS分类的预测能力，支持在前列腺MRI中需要优化预处理流程。
英文摘要
Diffusion-weighted imaging (DWI) is acquired as part of bi-parametric prostate MRI, but suffers from artifacts that degrade downstream quantitative and diagnostic performance. While DWI preprocessing is standard in brain imaging, its adoption in prostate imaging remains limited and lacks standardized pipelines. This study investigated the effect of different DWI preprocessing strategies on apparent diffusion coefficient (ADC) estimation and automatic Prostate Imaging Reporting and Data System (PI-RADS) classification. 268 cases were derived from the fastMRI prostate cohort by sequentially applying denoising, Gibbs-ringing correction, and diffeomorphic registration for susceptibility distortion correction. ADC maps were compared using linear least squares (LLS) and iteratively-weighted LLS (IWLLS). A 3-class DenseNet classifier was trained to predict PI-RADS scores from multi-channel MRI inputs. ADC analysis revealed statistically significant differences across preprocessing pipelines, with LLS and IWLLS producing numerically equivalent maps. Linear relationships between ADC values were preserved across most datasets (PCC ~0.99), while distortion correction realigned DWI to T2w anatomy and altered ADC values accordingly (PCC ~0.90). Classification showed the best AUROC and sensitivity for high-risk PI-RADS classes in the fully processed dataset. False-negative analysis revealed this dataset produced the least overconfident incorrect predictions on high-risk classes, which is a desirable property for clinical triage. DWI preprocessing, particularly distortion correction, enhances both ADC map quality and the predictive power of deep learning models for PI-RADS classification, supporting the need for optimized preprocessing pipelines in prostate MRI.
Comments19 pages, 10 figures, ISMRM Diffusion workshop 2025, ESMRMB 2025
