Keywords: Motion Correction, Motion Correction, Fast free-breathing abdominal MRI
Motivation: State-of-the-art and clinical abdominal MRI techniques are still limited by motion, acquisition time, and reconstruction time.
Goal(s): To develop an end-to-end deep learning approach including auto-navigation and motion-resolved reconstruction for fast and robust free-breathing T1-weighted MRI with a scan time of only 1 minute and reconstruction times of <1 minute.
Approach: Acquisition used a 3D T1-weighted golden-angle stack-of-stars pulse sequence with RANGR auto-navigation and Movienet reconstruction for motion-resolved imaging implemented on a clinical scanner. Two expert radiologists evaluated image quality.
Results: The proposed deep learning technique enables robust free-breathing MRI with a 1-minute scan time that compares favorably to the clinical standard.
Impact: The combination of deep learning auto-navigation and motion-resolved reconstruction enables fast and robust free-breathing abdominal MRI, which has the potential to reduce the number of repeat scans and increase efficiency compared to current clinical standards.
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