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Abstract #0124

End-to-end deep learning auto-navigation and reconstruction for accelerated free-breathing motion-resolved MRI

Victor Murray1, Yan Wen2, Gerald Behr3, Oguz Akin3, Arnaud Guidon2, and Ricardo Otazo1,3
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY, United States, 2GE HealthCare, Boston, MA, United States, 3Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY, United States

Synopsis

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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Keywords