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Abstract #4643

SmartBlade: AI-based reconstruction for motion robust abdominal PROPELLER imaging

Alexander Selivanov1, Holger Eggers1, Jakob Meineke1, Max-Heinrich Laves1, and Mariya Doneva1
1Philips Research, Hamburg, Germany

Synopsis

Keywords: Motion Correction, Motion Correction, PROPELLER, free breathing, registration, body MRI

Motivation: The motion correction in PROPELLER is typically limited to rigid body motion and only the averaging effect is exploited for motion artifact reduction for abdominal scans.

Goal(s): Our goal was to reduce motion artifacts and improve image quality in abdominal MRI.

Approach: We proposed an AI framework for reconstructing high-resolution motion-free image from a free-breathing abdominal PROPELLER scan. The AI model was trained on synthetic data and tested on abdominal T2W TSE scans.

Results: The proposed AI reconstruction outperforms the conventional PROPELLER reconstruction and PROPELLER with non-rigid motion correction in terms of residual motion artifacts and general image quality.

Impact: The proposed AI-based reconstruction allows obtaining motion-free images with high-spatial resolution from PROPELLER MRI scans, which facilitates abnormality detection.

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Keywords