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Abstract #3997

Deep learning based estimation of B1 field maps for variable flip angle qT1 mapping

Kuan-fu Chen1, Punnawish KK Thuwajit1, Jayse Merle Weaver1, Brandon Alexander Khmelevsky1, Andrew L. Alexander2,3, Steven Kecskemeti1, and Douglas Dean III2,4
1Waisman Center, University of Wisconsin-Madison, Madison, WI, United States, 2Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, 3Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States, 4Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, United States

Synopsis

Keywords: Analysis/Processing, Quantitative Imaging

Motivation: Quantitative T1(qT1) is a sensitive brain measure. However, qT1 estimation using variable flip angle methods requires B1 maps to correct the flip angle errors in SPGR images

Goal(s): Our goal is to develop a robust deep learning framework for fast and accurate predictions of a B1 maps directly from the SPGR images.

Approach: A pipeline was developed to process SPGR images and a U-net model was trained to estimate B1 maps.

Results: The deep learning model achieves good bias correction of qT1 with lower mean and standard deviation compared to the baseline.

Impact: This method enables retrospective estimation of qT1 from legacy variable flip angle SPGR data acquired without B1 mapping protocols.

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Keywords