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