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Abstract #0341

Deep Learning Optimization of Acquisition-Parameter and Slice-Ordering Schedule for Fast Quantitative Multi-Slice CEST

Ouri Cohen1, Robert J. Young2, and Ricardo Otazo1,2
1Medical Physics, Memorial Sloan Kettering, New York, NY, United States, 2Radiology, Memorial Sloan Kettering, New York, NY, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, CEST & MT

Motivation: To obtain optimized schedules for a multi-slice CEST sequence

Goal(s): To develop a deep learning schedule optimization approach for joint continuous and discrete scan parameters optimization.

Approach: A joint deep learning framework was developed and used to generate optimized 10-slice acquisition schedules. The optimized schedules were compared in simulations to a random 10-slice schedule and an optimized 1-slice schedule. The optimized 10-slice and 1-slice schedules were also compared in vivo in a healthy subject.

Results: The proposed approach yielded acquisition schedules that provided tissue maps with lower error than a random schedule or an optimized 1-slice schedule.

Impact: This work proposes a novel deep learning framework that enables simultaneous optimization of continuous and discrete scan parameters for a rapid quantitative multi-slice CEST sequence. The proposed method provides acquisition schedules that enable quantitative CEST imaging with lower error.

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