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Abstract #2104

Accelerating Multi-parametric MRI Sequences Using Learned Acquisition and Reconstruction Optimization (LARO): A Retrospective Study

Jinwei Zhang1, Hyeong-Geol Shin1,2, Blake Dewey3, Samuel W. Remedios1, Aaron Carass1, Xu Li2,3, Peter van Zijl2,3, Shiv Saidha3, Peter Calabresi3, and Jerry L. Prince1
1Johns Hopkins University, Baltimore, MD, United States, 2Kennedy Krieger Research Institute, Baltimore, MD, United States, 3Johns Hopkins University School of Medicine, Baltimore, MD, United States

Synopsis

Keywords: Quantitative Imaging, Quantitative Imaging

Motivation: Multi-parametric pulse sequences provide a comprehensive profile of MS lesions, but significantly prolonged scan time limits widespread use.

Goal(s): Use simulations to demonstrate potential of accelerating multi-parametric sequences, while maintaining consistency of parameter maps and ROI segmentations.

Approach: Learned Acquisition and Reconstruction Optimization (LARO) was applied retrospectively to a series of multi-contrast and quantitative MRI sequences with simulated fully sampled k-space data (gold standard) for acceleration. Quantitative multi-parametric values for relaxometry and susceptometry were compared before and after.

Results: LARO accelerated sequences for R1, R2, R2*, QSM, and susceptibility source separation (acceleration factor=8) achieved high lesion-wise consistency (R-squared>0.94) compared to fully sampled maps.

Impact: Our study demonstrates LARO’s potential to accelerate time-consuming multi-parametric and multi-contrast MRI sequences, paving the way for comprehensive lesion profiling within practical scan times. LARO acceleration ensured consistent parametric maps (except for R2) and ROI segmentations.

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