Enhanced standardization of clinical T2-weighted prostate images: e-CAMP with T2 prior

Motivation: Standardized T2-weighted images are valuable for prostate cancer healthcare. Although e-CAMP, a standardization algorithm for clinical T2-weighted images, has been proven feasible in brains, challenges remain for prostates. Goal(s): Developing an enhanced algorithm of e-CAMP to remove aliasing and ringing artifacts and avoid underestimation. Approach: Based on the insight that T2-weighted images and T2 maps are correlated, a T2 prior image is created from T2-weighted images to guide the reconstruction of T2 in e-CAMP. Results: While T2 prior approximates well, e-CAMP is closer to the ground truth, indicating unique contributions from both parts. Preliminary results on fastMRI prostate dataset are also encouraging. Impact: Sensitivity of machine learning to scanner- and/or protocol- variability can be reduced by estimating T2 maps from T2w. Here we present the feasibility of this approach for prostate imaging, where machine learning has shown great promise.