BibTex format

author = {Zhang, N and Yang, G and Gao, Z and Xu, C and Zhang, Y and Shi, R and Keegan, J and Xu, L and Zhang, H and Fan, Z and Firmin, D},
doi = {10.1148/radiol.2019182304},
journal = {Radiology},
pages = {52--60},
title = {Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI},
url = {},
volume = {294},
year = {2019}

RIS format (EndNote, RefMan)

AB - BackgroundRenal 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.PurposeTo develop a fully automatic framework for chronic MI delineation via deep learning on non–contrast material–enhanced cardiac cine MRI.Materials and MethodsIn 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.ResultsStudy 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).ConclusionThe proposed deep learning f
AU - Zhang,N
AU - Yang,G
AU - Gao,Z
AU - Xu,C
AU - Zhang,Y
AU - Shi,R
AU - Keegan,J
AU - Xu,L
AU - Zhang,H
AU - Fan,Z
AU - Firmin,D
DO - 10.1148/radiol.2019182304
EP - 60
PY - 2019///
SN - 0033-8419
SP - 52
TI - Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI
T2 - Radiology
UR -
UR -
UR -
VL - 294
ER -