Automated selection of myocardial inversion time with a convolutional neural network: Spatial temporal ensemble myocardium inversion network (STEMI‐NET). Issue 5 (3rd February 2019)

• Record Type: Journal Article
• Authors: Bahrami, Naeim; Retson, Tara; Blansit, Kevin; Wang, Kang; Hsiao, Albert
• Is Part Of: Magnetic resonance in medicine. Volume 81:Issue 5(2019)
• Page Start: 3283
• Page End: 3291
• Publication Date: 2019-02-03
• Subjects: cardiac magnetic resonance imaging (cMRI) -- convolutional neural network (CNN) -- deep learning -- inversion recovery time; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.27680
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: Delayed enhancement imaging is an essential component of cardiac MRI, which is used widely for the evaluation of myocardial scar and viability. The selection of an optimal inversion time (TI) or null point (TINP ) to suppress the background myocardial signal is required. The purpose of this study was to assess the feasibility of automated selection of TINP using a convolutional neural network (CNN). We hypothesized that a CNN may use spatial and temporal imaging characteristics from an inversion‐recovery scout to select TINP, without the aid of a human observer. Methods: We retrospectively collected 425 clinically acquired cardiac MRI exams performed at 1.5 T that included inversion‐recovery scout acquisitions. We developed a VGG19 classifier ensembled with long short‐term memory to identify the TINP . We compared the performance of the ensemble CNN in predicting TINP against ground truth, using linear regression analysis. Ground truth was defined as the expert physician annotation of the optimal TI. In a backtrack approach, saliency maps were generated to interpret the classification outcome and to increase the model's transparency. Results: Prediction of TINP from our ensemble VGG19 long short‐term memory closely matched with expert annotation (ρ = 0.88). Ninety‐four percent of the predicted TINP were within ±36 ms, and 83% were at or after expert TI selection. Conclusion: In this study, we show that a CNN is capable of automated prediction of myocardial TI from an inversion‐recovery experiment. Merging the spatial and temporal characteristics of the VGG‐19 and long short‐term‐memory CNN structures appears to be sufficient to predict myocardial TI from TI scout.