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Abstract #1680

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

Horace Z. Zhang1, Hemant D. Tagare1,2, and Gigi Galiana1,2
1Department of Biomedical Engineering, Yale University, New Haven, CT, United States, 2Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States

Synopsis

Keywords: Quantitative Imaging, Quantitative Imaging, Standardization, T2, Prostate, Signal Modeling

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.

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