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Abstract #2199

Technical Development and In Silico Implementation of SyntheticMR in Adaptive Radiation Therapy on the 1.5T MR-Linac

Lucas McCullum1,2, Samuel Mulder1,2, Natalie West1,2, Robert Aghoghovbia3, Alaa Mohamed Shawky Ali1, Hayden Scott1,2, Travis Salzillo4, Yao Ding4, Alex Dresner5, Ergys Subashi4, Dan Ma6, R. Jason Stafford7, Ken-Pin Hwang7, and Clifton D. Fuller1
1Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 2UT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, United States, 3Morehouse School of Medicine, Atlanta, GA, United States, 4Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 5Philips Healthcare MR Oncology, Cleveland, OH, United States, 6Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 7Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States

Synopsis

Keywords: MR-Guided Radiotherapy, MR-Guided Radiotherapy, Synthetic MR, Quantitative MRI

Motivation: SyntheticMR has not been studied on the 1.5T MR-Linac despite its potential value for treatment adaptation based on quantitative imaging.

Goal(s): The goal of this study was to investigate the technical feasibility of translating SyntheticMR to the 1.5T MR-Linac for the radiation therapy workflow.

Approach: SyntheticMR was scanned on phantoms and a volunteer and analyzed for geometric distortion and quantitative T1, T2, and PD vial measurements against reference values.

Results: There was no significant geometric distortion. Mean bias was below 3% for T1, 13% for T2, and 3% for PD vials and the Lin's Concordance Correlation Coefficient was above 0.96 across all vials.

Impact: SyntheticMR can enhance the MR-Linac workflow in the following ways: 1) multi-contrast anatomic/quantitative information in a single scan, 2) superior quantitative accuracy and spatial resolution compared to existing techniques, and 3) clinically acceptable repeatability, reproducibility, and spatial accuracy.

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Keywords