The recent introduction of synthetic correlated diffusion (CDI$^s$) imaging has demonstrated significant potential in the realm of clinical decision support for prostate cancer (PCa). CDI$^s$ is a new form of magnetic resonance imaging (MRI) designed to characterize tissue characteristics through the joint correlation of diffusion signal attenuation across different Brownian motion sensitivities. Despite the performance improvement, the CDI$^s$ data for PCa has not been previously made publicly available. In our commitment to advance research efforts for PCa, we introduce Cancer-Net PCa-Data, an open-source benchmark dataset of volumetric CDI$^s$ imaging data of PCa patients. Cancer-Net PCa-Data consists of CDI$^s$ volumetric images from a patient cohort of 200 patient cases, along with full annotations (gland masks, tumor masks, and PCa diagnosis for each tumor). We also analyze the demographic and label region diversity of Cancer-Net PCa-Data for potential biases. Cancer-Net PCa-Data is the first-ever public dataset of CDI$^s$ imaging data for PCa, and is a part of the global open-source initiative dedicated to advancement in machine learning and imaging research to aid clinicians in the global fight against cancer.

翻译：近期引入的合成相关扩散（CDI$^s$）成像技术在前列腺癌（PCa）临床决策支持领域展现出巨大潜力。CDI$^s$是一种新型磁共振成像（MRI）技术，通过联合分析不同布朗运动敏感度下的扩散信号衰减来表征组织特性。尽管性能有所提升，但PCa相关的CDI$^s$数据此前尚未公开。为推动PCa研究进展，我们推出Cancer-Net PCa-Data——首个基于PCa患者容积CDI$^s$成像数据的开源基准数据集。该数据集包含200例患者病例的CDI$^s$容积影像，并附带完整标注信息（腺体掩膜、肿瘤掩膜及各肿瘤对应的PCa诊断结果）。我们还分析了Cancer-Net PCa-Data的人口统计学特征与标签区域多样性以识别潜在偏差。作为全球开源计划的重要组成部分，Cancer-Net PCa-Data是首个公开的PCa CDI$^s$影像数据集，致力于推进机器学习和影像研究，助力全球临床医生抗击癌症。