Abstract #0394

Single breath-hold full abdominal T1 mapping using a CNN based short inversion-recovery sampling technique

Eze Ahanonu¹, Ute Goerke², Kevin Johnson³, Brian Toner⁴, Diego Martin⁵, Vibhas Deshpande⁶, Ali Bilgin^1,3,7, and Maria Altbach^3,7

¹Department of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ, United States, ²Siemens Healthineers, Tucson, AZ, United States, ³Department of Medical Imaging, The University of Arizona, Tucson, AZ, United States, ⁴Applied Math Program, The University of Arizona, Tucson, AZ, United States, ⁵Department of Radiology, Houston Methodist Hospital, Houston, TX, United States, ⁶Siemens Healthineers, Austin, TX, United States, ⁷Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Quantitative ImagingComprehensive liver evaluation with T1 mapping requires full abdominal coverage with sufficiently high spatial resolution for detection of pathology. Existing methods for abdominal T1 mapping are only able to achieve partial coverage, primarily limited by the breath hold and the time required to sample the T1 recovery curve (T1RC) for accurate T1 estimation. We present a radial Look-Locker T1 mapping framework which utilizes short T1RC sampling combined with deep learning based T1 estimation to achieve full abdominal coverage within a single 20s breath hold period.

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