Financial impact of deep learning reconstruction in magnetic resonance imaging: experiences after widespread deployment

Abstract

DLR deployment was associated with improved MRI suite productivity, enabling nearly pre-DLR throughput despite operating with one fewer scanner. The results demonstrate the utility of DLR in improving MRI productivity and support the predictive accuracy of simulation-based health technology assessment. Nevertheless, unpredictable performance, particularly in neuroimaging, where artifacts in T2-weighted sequences and reduced quality in contrast-enhanced studies were observed, limits the applicability of DLR and underscores the need for rigorous quality assurance.

Ähnliche Arbeiten