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Abstract #4814

Deep Learning Accelerated Single Breath-hold abdominal DWI with full liver and kidney coverage

Caixia Fu1, Li Yang1, Marcel Dominik Nickel2, Robert Grimm2, and Dehe Weng1
1MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China, 2MR Application Predevelopment, Siemens Healthineers AG,, Forchheim, Germany

Synopsis

Keywords: Diffusion Acquisition, AI/ML Image Reconstruction, single breath-hold DWI

Motivation: Free-breathing (FB) abdominal DWI is prone to motion artifacts, leading to unstable and unreliable ADC map estimations.

Goal(s): Implement and evaluate a single breath-hold DWI sequence utilizing deep learning (DL) reconstruction.

Approach: Healthy volunteer underwent FB DWI, respiratory triggered (RT) DWI and single breath-hold DWI accelerated by deep learning reconstruction. Image quality and ADC histograms were compared.

Results: DL-reconstructed single breath-hold DWI showed more reliable and stable ADC estimations, with fewer motion artifacts, image mismatches, and slice discontinuities compared to FB DWI and RT DWI.

Impact: Single breath-hold DWI can shorten scanning time, reduce artifacts and improve ADC estimation, potentially improving diagnostic reliability in abdominal MRI.

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