  Cardiac Magnetic Resonance (CMR) is established as a non-invasive imaging
technique for evaluation of heart function, anatomy, and myocardial tissue
characterization. Quantitative biomarkers are central for diagnosis and
management of heart disease. Deep learning (DL) is playing an ever more
important role in extracting these quantitative measures from CMR images. While
many researchers have reported promising results in training and evaluating
models, model deployment into the imaging workflow is less explored.
  A new imaging AI framework, the InlineAI, was developed and open-sourced. The
main innovation is to enable the model inference inline as a part of imaging
computation, instead of as an offline post-processing step and to allow users
to plug in their models. We demonstrate the system capability on three
applications: long-axis CMR cine landmark detection, short-axis CMR cine
analysis of function and anatomy, and quantitative perfusion mapping.
  The InlineAI allowed models to be deployed into imaging workflow in a
streaming manner directly on the scanner. The model was loaded and inference on
incoming images were performed while the data acquisition was ongoing, and
results were sent back to scanner. Several biomarkers were extracted from model
outputs in the demonstrated applications and reported as curves and tabular
values. All processes are full automated. the model inference was completed
within 6-45s after the end of imaging data acquisition.
