Deep learning reconstruction (DLR) algorithms have been brought into use in magnetic resonance imaging (MRI). In DLR algorithms, the acquisition is sped up by collecting fewer k-space samples or signal averages and compensating inferior data quality with artificial intelligence-based reconstruction. DLR methods have been demonstrated to enable significant reductions in acquisition times and improve signal-to-noise ratio (SNR) [1-5].

Since DLR generates the diagnostic images, special attention is needed to identify the limits of optimal operation and to ensure diagnostic image quality. The aim of this study was to evaluate quantitative and subjective image quality with commercial Deep Resolve Boost (DRB) (Siemens Healthineers Gmbh, Erlangen, Germany) DLR technique in contrast to conventional sequences.