NIH R01 · 2025
In this project we aim to develop deep-learning anthropomorphic model observers (AMO) as a substitute for human observers (HO) in studies aimed to assess image quality, defined in a task- based fashion based on clinical diagnostic performance. An accurate AMO would allow fast and clinically relevant procedure for optimization of imaging system and algorithm designs. The proposed AMO methods aim to achieve good generalization, i.e., an AMO developed for images reconstructed by one algorithm (or scanner setup) should predict HO performance accurately for a different reconstruction algorithm (or different scanner setting). In other words, a desired AMO should be tolerant to domain shifts. It…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.