Deep Learning based Acquisition and Reconstruction for Cardiac Magnetic Resonance Imaging in Patients with Chronic Kidney Disease

Motivation: Cardiac abnormalities and arrhythmias increased risk of cardiovascular mortality especially in patients with Chronic Kidney Disease (CKD). Goal(s): To provide objective and subjective assessment in supproting the feasibility of deep learning acqusition and reconstruction cardiac magnetic resonance (CMR) imaging in patients with Chronic Kidney Disease (CKD). Approach: Qualitative and quantitative evaluation of Sonic DL-based CMR images including breath-hold acquisition within one-beat-interval, free-breathing acquisition in comparison with traditional cine images. Results: Sonic DL significantly accelerated acuqisition time (e.g. 11 seconds) but offered diagnosis-suffcient image quality and reliable strain values ompared to conventional sequences. Impact: Sonic DL cine MRI showed equivalent image quality to conventional one, offered reliable strain values for diagnosis of a CKD patient lack of breath-hold ability and presentce with arrhythmias. Even it acquire a whole heart cine image in 11 seconds.