Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Background
Renal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed.
Purpose
To develop a fully automatic framework for chronic MI delineation via deep learning on non–contrast material–enhanced cardiac cine MRI.
Materials and Methods
In this retrospective single-center study, a deep learning model was developed to extract motion features from the left ventricle and delineate MI regions on nonenhanced cardiac cine MRI collected between October 2015 and March 2017. Patients with chronic MI, as well as healthy control patients, had both nonenhanced cardiac cine (25 phases per cardiac cycle) and LGE MRI examinations. Eighty percent of MRI examinations were used for the training data set and 20% for the independent testing data set. Chronic MI regions on LGE MRI were defined as ground truth. Diagnostic performance was assessed by analysis of the area under the receiver operating characteristic curve (AUC). MI area and MI area percentage from nonenhanced cardiac cine and LGE MRI were compared by using the Pearson correlation, paired t test, and Bland-Altman analysis.
Results
Study participants included 212 patients with chronic MI (men, 171; age, 57.2 years ± 12.5) and 87 healthy control patients (men, 42; age, 43.3 years ± 15.5). Using the full cardiac cine MRI, the per-segment sensitivity and specificity for detecting chronic MI in the independent test set was 89.8% and 99.1%, respectively, with an AUC of 0.94. There were no differences between nonenhanced cardiac cine and LGE MRI analyses in number of MI segments (114 vs 127, respectively; P = .38), per-patient MI area (6.2 cm2 ± 2.8 vs 5.5 cm2 ± 2.3, respectively; P = .27; correlation coefficient, r = 0.88), and MI area percentage (21.5% ± 17.3 vs 18.5% ± 15.4; P = .17; correlation coefficient, r = 0.89).
Conclusion
The proposed deep learning framework on nonenhanced cardiac cine MRI enables the confirmation (presence), detection (position), and delineation (transmurality and size) of chronic myocardial infarction. However, future larger-scale multicenter studies are required for a full validation.
Renal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed.
Purpose
To develop a fully automatic framework for chronic MI delineation via deep learning on non–contrast material–enhanced cardiac cine MRI.
Materials and Methods
In this retrospective single-center study, a deep learning model was developed to extract motion features from the left ventricle and delineate MI regions on nonenhanced cardiac cine MRI collected between October 2015 and March 2017. Patients with chronic MI, as well as healthy control patients, had both nonenhanced cardiac cine (25 phases per cardiac cycle) and LGE MRI examinations. Eighty percent of MRI examinations were used for the training data set and 20% for the independent testing data set. Chronic MI regions on LGE MRI were defined as ground truth. Diagnostic performance was assessed by analysis of the area under the receiver operating characteristic curve (AUC). MI area and MI area percentage from nonenhanced cardiac cine and LGE MRI were compared by using the Pearson correlation, paired t test, and Bland-Altman analysis.
Results
Study participants included 212 patients with chronic MI (men, 171; age, 57.2 years ± 12.5) and 87 healthy control patients (men, 42; age, 43.3 years ± 15.5). Using the full cardiac cine MRI, the per-segment sensitivity and specificity for detecting chronic MI in the independent test set was 89.8% and 99.1%, respectively, with an AUC of 0.94. There were no differences between nonenhanced cardiac cine and LGE MRI analyses in number of MI segments (114 vs 127, respectively; P = .38), per-patient MI area (6.2 cm2 ± 2.8 vs 5.5 cm2 ± 2.3, respectively; P = .27; correlation coefficient, r = 0.88), and MI area percentage (21.5% ± 17.3 vs 18.5% ± 15.4; P = .17; correlation coefficient, r = 0.89).
Conclusion
The proposed deep learning framework on nonenhanced cardiac cine MRI enables the confirmation (presence), detection (position), and delineation (transmurality and size) of chronic myocardial infarction. However, future larger-scale multicenter studies are required for a full validation.
Date Issued
2019-06-01
Date Acceptance
2019-03-14
Citation
Radiology, 2019, 294 (1), pp.52-60
ISSN
0033-8419
Publisher
Radiological Society of North America (RSNA)
Start Page
52
End Page
60
Journal / Book Title
Radiology
Volume
294
Issue
1
Copyright Statement
© 2019 RSNA.
Sponsor
British Heart Foundation
Identifier
https://pubs.rsna.org/doi/10.1148/radiol.2019182304
Grant Number
PG/16/78/32402
Subjects
Science & Technology
Life Sciences & Biomedicine
Radiology, Nuclear Medicine & Medical Imaging
CARDIOVASCULAR MAGNETIC-RESONANCE
DELAYED-ENHANCEMENT
TEXTURE ANALYSIS
CONTRAST-MEDIA
PERFORMANCE
SEGMENTS
Adult
Aged
Chronic Disease
Databases, Factual
Deep Learning
Female
Humans
Image Interpretation, Computer-Assisted
Magnetic Resonance Imaging, Cine
Male
Middle Aged
Myocardial Infarction
Retrospective Studies
Sensitivity and Specificity
Humans
Myocardial Infarction
Chronic Disease
Image Interpretation, Computer-Assisted
Magnetic Resonance Imaging, Cine
Sensitivity and Specificity
Retrospective Studies
Databases, Factual
Adult
Aged
Middle Aged
Female
Male
Deep Learning
11 Medical and Health Sciences
Nuclear Medicine & Medical Imaging
Publication Status
Published
Article Number
182304
Date Publish Online
2019-04-30