Multi-input deep learning approach for Cardiovascular Disease diagnosis using Myocardial Perfusion Imaging and clinical data

2021 ◽  
Vol 84 ◽  
pp. 168-177
Author(s):  
Ioannis D. Apostolopoulos ◽  
Dimitris I. Apostolopoulos ◽  
Trifon I. Spyridonidis ◽  
Nikolaos D. Papathanasiou ◽  
George S. Panayiotakis
Keyword(s):  
Cardiovascular Disease ◽  
Deep Learning ◽  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Clinical Data ◽  
Perfusion Imaging ◽  
Disease Diagnosis ◽  
Learning Approach

Sarcoid heart disease: Diagnosis by thallium 201 myocardial perfusion imaging

1976 ◽  
Vol 37 (1) ◽  
pp. 125 ◽  
Author(s):  
Bernadine H. Bulkley ◽  
Jacques Rouleau ◽  
H.William Strauss ◽  
Bertram Pitt
Keyword(s):  
Heart Disease ◽  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Perfusion Imaging ◽  
Disease Diagnosis ◽  
Thallium 201 ◽  
Sarcoid Heart Disease ◽  
Heart Disease Diagnosis

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.

MS01.2 Identifying Mediate Adverse Events Through Deep Learning and PET Myocardial Perfusion Imaging

Global Heart ◽  
2018 ◽  
Vol 13 (4) ◽  
pp. 373
Author(s):  
L.E. Juarez-Orozco ◽  
R.J. Knol ◽  
O. Martinez-Manzanera ◽  
F.M. van der Zant ◽  
J. Knuuti
Keyword(s):  
Deep Learning ◽  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Adverse Events ◽  
Perfusion Imaging ◽  
Pet Myocardial Perfusion Imaging

scholarly journals Automatic Diagnosis of Coronary Artery Disease in SPECT Myocardial Perfusion Imaging Employing Deep Learning

2021 ◽  
Vol 11 (14) ◽  
pp. 6362
Author(s):  
Nikolaos Papandrianos ◽  
Elpiniki Papageorgiou
Keyword(s):  
Coronary Artery Disease ◽  
Deep Learning ◽  
Coronary Artery ◽  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Perfusion Imaging ◽  
Emission Computed Tomography ◽  
Automatic Diagnosis ◽  
Spect Mpi ◽  
Artery Disease

Focusing on coronary artery disease (CAD) patients, this research paper addresses the problem of automatic diagnosis of ischemia or infarction using single-photon emission computed tomography (SPECT) (Siemens Symbia S Series) myocardial perfusion imaging (MPI) scans and investigates the capabilities of deep learning and convolutional neural networks. Considering the wide applicability of deep learning in medical image classification, a robust CNN model whose architecture was previously determined in nuclear image analysis is introduced to recognize myocardial perfusion images by extracting the insightful features of an image and use them to classify it correctly. In addition, a deep learning classification approach using transfer learning is implemented to classify cardiovascular images as normal or abnormal (ischemia or infarction) from SPECT MPI scans. The present work is differentiated from other studies in nuclear cardiology as it utilizes SPECT MPI images. To address the two-class classification problem of CAD diagnosis, achieving adequate accuracy, simple, fast and efficient CNN architectures were built based on a CNN exploration process. They were then employed to identify the category of CAD diagnosis, presenting its generalization capabilities. The results revealed that the applied methods are sufficiently accurate and able to differentiate the infarction or ischemia from healthy patients (overall classification accuracy = 93.47% ± 2.81%, AUC score = 0.936). To strengthen the findings of this study, the proposed deep learning approaches were compared with other popular state-of-the-art CNN architectures for the specific dataset. The prediction results show the efficacy of new deep learning architecture applied for CAD diagnosis using SPECT MPI scans over the existing ones in nuclear medicine.

An assessment of attenuation correction of SPECT MPI images generated by deep learning model

2021 ◽  
pp. 93-101
Author(s):  
Nguyen Chi Thanh
Keyword(s):  
Deep Learning ◽  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Attenuation Correction ◽  
Perfusion Imaging ◽  
Emission Computed Tomography ◽  
Years Of Experience ◽  
Data Set ◽  
Spect Mpi ◽  
Attenuation Corrected

This article evaluates the effectiveness of using a deep learning network model to generate reliable attenuation corrected the single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). The authors collected myocardial perfusion imaging data of 88 patients from a SPECT/CT machine, with an average age of 62.47 years. Then, two datasets are created from the original data: set A includes the deep learning-based attenuation corrected images (Generated Attenuation Correction - GenAC), and the non-attenuation corrected images; set B contains only non-attenuation corrected images. These datasets were diagnosed by two doctors (in which, one has 7 years of experience and the other has 10 years of experience in reading SPECT MPI). The doctors diagnose based on the image data without knowing which dataset it belongs to. The sensitivity, specificity, diagnostic accuracy, and lesion rate were evaluated between the two data sets. Results: The average specificity, sensitivity, and accuracy of the set with the deep learning-based attenuation corrected images were 0.87, 0.86, 0.86, while the results with the non-attenuation corrected images are 0.69, 0.83, and 0.78.

scholarly journals Deep Learning in Quantitative PET Myocardial Perfusion Imaging

2020 ◽  
Vol 13 (1) ◽  
pp. 180-182 ◽  
Author(s):  
Luis Eduardo Juarez-Orozco ◽  
Octavio Martinez-Manzanera ◽  
Friso M. van der Zant ◽  
Remco J.J. Knol ◽  
Juhani Knuuti
Keyword(s):  
Deep Learning ◽  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Perfusion Imaging ◽  
Quantitative Pet ◽  
Pet Myocardial Perfusion Imaging
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