Automated inversion time selection for late gadolinium–enhanced cardiac magnetic resonance imaging

作者：Cheng Xie, R Zhang, Sebastian Mensink, Rahul Gandharva, Mustafa Awni, Hester Lim, Stefan E. Kachel, Ernest Cheung, Richard Crawley, Leonid Churilov, Nuno Bettencourt, Amedeo Chiribiri, Cian M. Scannell, Ruth Lim · 发表于：European Radiology · 年份：2024 · DOI：10.1007/s00330-024-10630-w · 被引用次数：8 · 研究领域：Cardiac Imaging and Diagnostics、Advanced MRI Techniques and Applications、Cardiac pacing and defibrillation studies

OBJECTIVES: To develop and share a deep learning method that can accurately identify optimal inversion time (TI) from multi-vendor, multi-institutional and multi-field strength inversion scout (TI scout) sequences for late gadolinium enhancement cardiac MRI. MATERIALS AND METHODS: Retrospective multicentre study conducted on 1136 1.5-T and 3-T cardiac MRI examinations from four centres and three scanner vendors. Deep learning models, comprising a convolutional neural network (CNN) that provides input to a long short-term memory (LSTM) network, were trained on TI scout pixel data from centres 1 to 3 to identify optimal TI, using ground truth annotations by two readers. Accuracy within 50 ms, mean absolute error (MAE), Lin's concordance coefficient (LCCC) and reduced major axis regression (RMAR) were used to select the best model from validation results, and applied to holdout test data. Robustness of the best-performing model was also tested on imaging data from centre 4. RESULTS: The best model (SE-ResNet18-LSTM) produced accuracy of 96.1%, MAE 22.9 ms and LCCC 0.47 compared to ground truth on the holdout test set and accuracy of 97.3%, MAE 15.2 ms and LCCC 0.64 when tested on unseen external (centre 4) data. Differences in vendor performance were observed, with greatest accuracy for the most commonly represented vendor in the training data. CONCLUSION: A deep learning model was developed that can identify optimal inversion time from TI scout images on multi-vendor data with ...