Automated detection of left ventricle in arterial input function images for inline perfusion mapping using deep learning: A study of 15, 000 patients. Issue 5 (7th May 2020)

• Record Type: Journal Article
• Authors: Xue, Hui; Tseng, Ethan; Knott, Kristopher D.; Kotecha, Tushar; Brown, Louise; Plein, Sven; Fontana, Marianna; Moon, James C.; Kellman, Peter
• Is Part Of: Magnetic resonance in medicine. Volume 84:Issue 5(2020)
• Page Start: 2788
• Page End: 2800
• Publication Date: 2020-05-07
• Subjects: arterial input function -- deep learning -- Gadgetron -- Inline AI -- myocardial perfusion -- perfusion quantification; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28291
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: Quantification of myocardial perfusion has the potential to improve the 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. Failure to accurately identify the left ventricle (LV) prevents AIF estimation required for quantification, therefore high detection accuracy is required. This study presents a robust LV detection method using the convolutional neural network (CNN). Methods: CNN models were trained by assembling 25, 027 scans ( N = 12, 984 patients) from three hospitals, seven scanners. Performance was evaluated using a hold‐out test set of 5721 scans ( N = 2805 patients). Model inputs were a time series of AIF images (2D+T). Two variations were investigated: (1) two classes (2CS) for background and foreground (LV mask), and (2) three classes (3CS) for background, LV, and RV. The final model was deployed on MRI scanners using the Gadgetron reconstruction software framework. Results: Model loading on the MRI scanner took ~340 ms and applying the model took ~180 ms. The 3CS model successfully detected the LV in 99.98% of all test cases (1 failure out of 5721). The mean Dice ratio for 3CS was 0.87 ± 0.08 with 92.0% of all cases having Dice >0.75. The 2CS model gave a lower Dice ratio of 0.82 ± 0.22 ( P < 1e−5). There was no significant difference in foot‐time, peak‐time, first‐pass duration, peak value, and area‐under‐curve ( P > .2) comparing automatically extracted AIF signals with signals from manually drawn contours. Conclusions: A CNN‐based solution to detect the LV blood pool from the arterial input function image series was developed, validated, and deployed. A high LV detection accuracy of 99.98% was achieved.