  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.
