Fig. 3
Schematic of the implemented inline integration of MyoMapNet using the Siemens Framework for Image Reconstruction (FIRE) prototype. The pre-trained MyoMapNet model was deployed in a containerized (chroot) Python 3.6 environment compatible with the FIRE framework. Data acquired on the scanner underwent standard image reconstruction and motion correction in the Siemens ICE pipeline, and T1-weighted images were converted into ISMRM Raw Data format (ISMRMRD) and sent to the MyoMapNet model. In the pre-processing step, the T1-weighted signals were normalized to the range of 0–1.1. After prediction, T1 map was sent back to the ICE pipeline in the ISMRMRD format where distortion correction and DICOM images were generated and displayed on the CMR console