Abstract

Motivation: There is a need for reproducibility, repeatability, and accuracy. Previously, OSIPI organized a challenge for benchmarking DCE software. As the use of artificial intelligence grows, we now set out to repeat the challenge, focusing on deep learning techniques. Goal(s): To encourage researchers put their quantitative methods to test and stimulate collaboration and to charter the heterogeneity of DCE analysis software. Approach: To use deep learning techniques to estimate perfusion parameters in DCE-MRI of the uterus. We share repeated in-vivo data to assess the algorithm’s precision, and simulated DCE-data to test the accuracy. Results: Top three winners may present their method at ISMRM 2025. Impact: As quantitative perfusion MRI receives more importance and attention, the need for reproducibility, repeatability, and accuracy is inevitable. A public challenge within the MRI community is a great way to highlight the quantification of DCE-MRI.