Improving Abdominal MR Image Quality at 0.55T Using Deep Learning Reconstruction: A Comparative Study with Commercial 0.55T and High-Field Scans

Motivation: Deep-learning reconstruction may overcome two shortcomings of 0.55T, low SNR and extended scan time, without compromising lesion conspicuity. Goal(s): To demonstrate that image quality and SNR of deep-learning reconstructed 0.55T images are at least similar to 1.5T/3T images, while maintaining visibility of pathologies. Approach: 23 patients imaged at 0.55T using standard and deep-learning HASTE and DWI. Three radiologists rated IQ and SNR at 0.55T and HF. Pathologies were evaluated in deep-learning images. Results: Deep-learning reconstructed HASTE and DWI 0.55T images were of same or better quality and SNR than 1.5T/3T images. All pathologies were visible on deep-learning 0.55T images. DL reduced HASTE scan-time. Impact: Deep-learning reconstruction algorithms of select sequences at 0.55T can help overcome low SNR and extended scan times of current 0.55T abdominal imaging, making it comparable or superior to standard-of-care 1.5/3T, thereby expanding global use of a more accessible MRI system.