Analysis on SPECT myocardial perfusion imaging with a tool derived from dynamic programming to deep learning

Optik ◽  
2021 ◽  
Vol 240 ◽  
pp. 166842
Author(s):  
Haixing Wen ◽  
Qiuyue Wei ◽  
Jin-Long Huang ◽  
Shih-Chuan Tsai ◽  
Chi-Yen Wang ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Deep Learning ◽  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Perfusion Imaging ◽  
Spect Myocardial Perfusion Imaging ◽  
Spect Myocardial Perfusion

scholarly journals Deep Learning Analysis of Polar Maps from SPECT Myocardial Perfusion Imaging for Prediction of Coronary Artery Disease

2021 ◽  
Author(s):  
Narges Zahiri ◽  
Rhona Asgari ◽  
Seid-Kazem Razavi-Ratki ◽  
Ali-Asghar parach
Keyword(s):  
Deep Learning ◽  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Perfusion Imaging ◽  
Learning Model ◽  
Emission Computed Tomography ◽  
Spect Myocardial Perfusion Imaging ◽  
Spect Myocardial Perfusion ◽  
Deep Learning Model ◽  
Polar Map

Abstract Purpose: This study aimed to investigate the diagnostic accuracy of deep convolutional neural networks for classifying the polar map images in Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) by considering the physician’s diagnosis as reference.Methods: 3318 images of stress and rest polar maps related to patients (67% women and 33% men) who underwent 99mTc-sestamibi MPI were collected. The images were manually labeled with normal and abnormal labels according to the doctor’s diagnosis reports. The proposed deep learning model was trained using stress and rest polar maps and evaluated for prediction of obstructive disease in a stratified 5-fold cross-validation procedure.Results: The mean values of accuracy, sensitivity, accuracy, specificity, f1 score, and the area under the roc curve were 0.7562, 0.7856, 0.5748, 0.7434, 0.6646, and, 0.8450, respectively over 5 folds using both stress and rest scans. The inclusion of rest perfusion maps significantly improved AUC of the deep learning model (AUC: 0.845; 95% CI: 0.832-0.857), compared with using stress polar maps only (AUC: 0.827; 95% CI: 0.814-0.840); P < 0.05.Conclusion: The results of the present work reveal the possible applications of deep learning for polar map images classification in SPECT MPI.

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