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Abstract #1704

Improved Resolution and Precision of whole-brain multi-parametric qMRI using Deep Learning Denoising and Super-Resolution Reconstruction

Hung P Do1, Dawn Berkeley1, Paul Kokeny2, Sean Sethi2, Miller Fawaz2, Kiarash Ghassaban2, Wissam AlGhuraibawi1, Brian Tymkiw1, John Ciliberto2, Karen Holzberger2, Mo Kadbi1, James Backstrom3, and Kevin Berger4
1Canon Medical Systems USA, Inc., Tustin, CA, United States, 2SpinTech-MRI Inc., Bingham Farms, MI, United States, 3Armstrong County Memorial Hospital, Butler, PA, United States, 4Chesapeake Medical Imaging, Annapolis, MD, United States

Synopsis

Keywords: Quantitative Imaging, MR Value

Motivation: High-resolution multi-parametric quantitative MRI (qMRI) is desirable for accurate tissue characterization. However, it comes with the cost of longer scan-time and reduced SNR. Parallel Imaging can mitigate the long scan-time, but it further compromises SNR.

Goal(s): To use deep learning denoising and supper-resolution reconstruction (DLR) along with rapid k-space sampling methods to achieve a 5min 3D volumetric qMRI protocol.

Approach: GRE images reconstructed with DLR were used for calculating quantitative maps and qualitative weightings. Quantitative values and image quality metrics were measured and compared between original and DLR.

Results: DLR improved resolution, sharpness, SNR, CNR, and precision of qMRI while maintaining quantitative values.

Impact: High-resolution and high-SNR whole-brain qMRI can be acquired within 5-6min using acceleration and DLR. qMRI’s qualitative maps and quantitative weightings may be used for comprehensive evaluation of pathology and longitudinal follow-up.

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Keywords