Abstract #3761

SyntheticLGE.jl: An Open-Source Toolbox for Retrospective T1 Fitting and Synthetic LGE Image Generation

Calder D. Sheagren^1,2, Brandon T. T. Tran^1,2, Jaykumar H. Patel^1,2, Angus Z. Lau^1,2, and Graham A. Wright^1,2

¹Medical Biophysics, University of Toronto, Toronto, ON, Canada, ²Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada

Synopsis

Keywords: Synthetic MR, Cardiovascular, Late Gadolinium Enhancement, Synthetic MR

Motivation: Synthetic late gadolinium enhancement (SynLGE) has been proposed as a technique to quantify cardiac fibrosis from post-contrast T1 mapping. Current SynLGE techniques use site-specific code, limiting clinical adoption and standardization.

Goal(s): Develop a software toolbox for SynLGE image generation using retrospective T1* mapping.

Approach: An open-source software SyntheticLGE.jl was implemented in Julia and is publicly available on GitHub with two sample MOLLI datasets for software evaluation.

Results: SynLGE image generation is feasible for both SSFP and gradient-echo MOLLI imaging.

Impact: We hope that SyntheticLGE.jl can enable standardized and reproducible synthetic LGE image generation in simple and challenging clinical scenarios.

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