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Abstract #3843

Partial volume estimation from MRF acquisition using a deep learning approach

Tianyi Ding1, Yang Gao2, Zhuang Xiong1, Feng Liu1, Martijn Cloos3, and Hongfu Sun1,4
1School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia, 2School of Computer Science and Engineering, Central South University, Changsha, China, 3Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia, 4School of Engineering, The University of Newcastle, Newcastle, Australia

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: Partial volume effects in MRI limit the accuracy of quantitative parameters like T1 and T2, especially when a voxel contains mixed tissue types. This research seeks to improve partial volume estimation process.

Goal(s): The study aims to enhance partial volume estimation by introducing a self-supervised neural network, MRF-PVMixer, designed to process MRF signals directly for more accurate mapping.

Approach: Applied deep learning framework, MRF-PVMixer extends the MRF-Mixer model with another U-Net decoder to estimate partial volume fractions. This method was validated on synthetic datasets created with radial-undersampling.

Results: MRF-PVMixer showed promising results in PV estimation as seen in both visual and quantitative results.

Impact: This study's self-supervised deep learning approach for partial volume estimation directly from MRF signals could improve diagnostic accuracy and streamline quantitative MRI processes. It opens avenues for real-time, artifact-resilient tissue characterization, potentially transforming clinical workflows and supporting patient-specific imaging studies.

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Keywords