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Abstract #3134

Automated Volume of Interest Evaluation for Sequence Development

Ying Wu1,2, Hongyan Du3, Fiona Malone1, Shawn Sidharthan1, Ann Ragin4, Robert Edelman1,5

1Radiology, NorthShore University HealthSystem, Evanston, IL, United States; 2Radiology , University of Chicago; 3NorthShore University HealthSystem Research Institute, IL, United States; 4Radiology, Northwestern University; 5Radiology, University of Chicago

This investigation compared the standard manual region of interest approach with a volume-of-interest analysis based on automated brain segmentation. Analysis based on automated VOI successfully detected subtle changes in tissue contrast and was consistently informative for MR sequence optimization. Results based on the standard ROI approach were ambiguous in different brain regions and individuals, and failed to document changes in image quality when scanning parameters were alternated in MR sequence optimization. These findings demonstrate the potential benefit of integrating advanced quantitative image analysis into sequence development routines to improve efficiency and accuracy.