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Abstract #4320

Automatic Quantification of Image Distortion in Prostate Diffusion-Weighted Imaging

Haoran Sun1,2, Lixia Wang1, Hsu-Lei Lee1, Vibhas S. Deshpande3, Fei Han1,3, Debiao Li1, and Yibin Xie1
1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, 3Siemens Healthineers, Austin, TX, United States

Synopsis

Keywords: Prostate, Prostate, DWI distortion

Motivation: DWI is crucial for prostate cancer imaging, but its susceptibility to image distortion poses challenges to reading and leads to a substantial rate of nondiagnostic scans.

Goal(s): This study aimed to develop and compare two algorithms for automatic distortion assessment for prostate DWI.

Approach: Two automatic distortion assessment methods were developed based on image segmentation, and deformable registration. Both were validated and compared using radiology grading as the reference.

Results: Both distortion assessment methods quantified the levels of image distortion in prostate DWI consistent with visual assessments and correlated well with expert ratings. The deformable registration-based approach appeared to outperform its counterpart.

Impact: The developed methods for automatic assessment of image distortion may assist in acquiring high-quality prostate DWI and reducing patient recalls.

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