  Quantification of myocardial perfusion has the potential to improve detection
of regional and global flow reduction. Significant effort has been made to
automate the workflow, where one essential step is the arterial input function
(AIF) extraction. Since failure here invalidates quantification, high accuracy
is required. For this purpose, this study presents a robust AIF detection
method using the convolutional neural net (CNN) model. CNN models were trained
by assembling 25,027 scans (N=12,984 patients) from three hospitals, seven
scanners. A test set of 5,721 scans (N=2,805 patients) evaluated model
performance. The 2D+T AIF time series was inputted into CNN. Two variations
were investigated: a) Two Classes (2CS) for background and foreground (LV
mask); b) Three Classes (3CS) for background, foreground LV and RV. Final model
was deployed on MR scanners via the Gadgetron InlineAI. Model loading time on
MR scanner was ~340ms and applying it took ~180ms. The 3CS model successfully
detect LV for 99.98% of all test cases (1 failed out of 5,721 cases). The mean
Dice ratio for 3CS was 0.87+/-0.08 with 92.0% of all test cases having Dice
ratio >0.75, while the 2CS model gave lower Dice of 0.82+/-0.22 (P<1e-5).
Extracted AIF signals using CNN were further compared to manual ground-truth
for foot-time, peak-time, first-pass duration, peak value and area-under-curve.
No significant differences were found for all features (P>0.2). This study
proposed, validated, and deployed a robust CNN solution to detect the LV for
the extraction of the AIF signal used in fully automated perfusion flow
mapping. A very large data cohort was assembled and resulting models were
deployed to MR scanners for fully inline AI in clinical hospitals.
