Keywords: Prostate, Diffusion Analysis and Visualization, Prostate, Machine Learning/Artificial Intelligence
Motivation: Diffusion-weighted imaging(DWI) is essential for prostate cancer imaging; however, its susceptibility to image distortion presents challenges in radiology interpretation. Automatic quantification of the level of image distortion could provide immediate feedback to scan operators, potentially reducing patient recalls.
Goal(s): This study aimed to develop an ensemble machine-learning framework for the automatic assessment of distortion in prostate DWI.
Approach: The framework integrates distortion factors derived from both segmentation-based and registration-based computational methods, employing ensemble learning to improve classification accuracy.
Results: The proposed ensemble model demonstrated superior performance in accurately classifying distortion ranks, achieving AUCs of 1.0, 0.93, 0.93 in distinguishing the three distortion levels.
Impact: The developed ensemble framework for automatic assessment of image distortion may assist in acquiring high-quality prostate DWI and reducing patient recalls.
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