20.10.6.3 Myocardial Infarction

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Myocardial Infarction.

Yokoyama, R.[Ryujiro], Zhang, X.J.[Xue-Jun], Uchiyama, Y.[Yoshikazu], Fujita, H.[Hiroshi], Hara, T.[Takeshi], Zhou, X.R.[Xiang-Rong], Kanematsu, M.[Masayuki], Asano, T.[Takahiko], Kondo, H.[Hiroshi], Goshima, S.[Satoshi], Hoshi, H.[Hiroaki], Iwama, T.[Toru],
Development of an Automated Method for the Detection of Chronic Lacunar Infarct Regions in Brain MR Images,
IEICE(E90-D), No. 6, June 2007, pp. 943-954.
DOI Link 0706
BibRef

Detsky, J.S., Paul, G., Dick, A.J., Wright, G.A.,
Reproducible Classification of Infarct Heterogeneity Using Fuzzy Clustering on Multicontrast Delayed Enhancement Magnetic Resonance Images,
MedImg(28), No. 10, October 2009, pp. 1606-1614.
IEEE DOI 0910
BibRef

Tripathy, R.K., Dandapat, S.,
Detection of myocardial infarction from vectorcardiogram using relevance vector machine,
SIViP(11), No. 6, September 2017, pp. 1139-1146.
WWW Link. 1708
BibRef

Duchateau, N., de Craene, M., Allain, P., Saloux, E., Sermesant, M.,
Infarct Localization From Myocardial Deformation: Prediction and Uncertainty Quantification by Regression From a Low-Dimensional Space,
MedImg(35), No. 10, October 2016, pp. 2340-2352.
IEEE DOI 1610
Computational modeling BibRef

Sharma, L.D.[Lakhan Dev], Sunkaria, R.K.[Ramesh Kumar],
Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach,
SIViP(12), No. 2, February 2018, pp. 199-206.
WWW Link. 1802
BibRef

Baloglu, U.B.[Ulas Baran], Talo, M.[Muhammed], Yildirim, O.[Ozal], Tan, R.S.[Ru San], Acharya, U.R.[U Rajendra],
Classification of myocardial infarction with multi-lead ECG signals and deep CNN,
PRL(122), 2019, pp. 23-30.
Elsevier DOI 1904
Myocardial infarction, Deep learning, Multi-lead ECG, Biomedical signal BibRef

Lin, Z.C.[Zhuo-Chen], Gao, Y.X.[Yong-Xiang], Chen, Y.M.[Yi-Min], Ge, Q.[Qi], Mahara, G.[Gehendra], Zhang, J.X.[Jin-Xin],
Automated detection of myocardial infarction using robust features extracted from 12-lead ECG,
SIViP(14), No. 5, July 2020, pp. 857-865.
Springer DOI 2006
BibRef

Prabhakararao, E., Dandapat, S.,
Attentive RNN-Based Network to Fuse 12-Lead ECG and Clinical Features for Improved Myocardial Infarction Diagnosis,
SPLetters(27), 2020, pp. 2029-2033.
IEEE DOI 2012
Electrocardiography, Myocardium, Feature extraction, Lead, Heart, Convolutional neural networks, Biological system modeling, recurrent neural network BibRef

Gullberg, G.T.[Grant T.], Shrestha, U.M.[Uttam M.], Veress, A.I.[Alexander I.], Segars, W.P.[W. Paul], Liu, J.[Jing], Ordovas, K.[Karen], Seo, Y.[Youngho],
Novel Methodology for Measuring Regional Myocardial Efficiency,
MedImg(40), No. 6, June 2021, pp. 1711-1725.
IEEE DOI 2106
Heart, Strain, Myocardium, Magnetic resonance imaging, Stress, Calcium, Mechanical variables measurement, Cardiac efficiency, PET BibRef


Rahman, T.[Tanjib], Moulin, K.[Kévin], Ennis, D.B.[Daniel B.], Perotti, L.E.[Luigi E.],
Diffusion Biomarkers in Chronic Myocardial Infarction,
FIMH21(137-147).
Springer DOI 2108
BibRef

Mom, K.[Kannara], Clarysse, P.[Patrick], Duchateau, N.[Nicolas],
Population-Based Personalization of Geometric Models of Myocardial Infarction,
FIMH21(3-11).
Springer DOI 2108
BibRef

Liu, H.[Hao], Narang, H.[Harshita], Gorman, R.[Robert], Gorman, J.[Joseph], Sacks, M.S.[Michael S.],
On the Interrelationship Between Left Ventricle Infarction Geometry and Ischemic Mitral Regurgitation Grade,
FIMH21(425-434).
Springer DOI 2108
BibRef

Serra, D.[Dolors], Romero, P.[Pau], Lozano, M.[Miguel], García-Fernández, I.[Ignacio], Liberos, A.[Alejandro], Rodrigo, M.[Miguel], Berruezo, A.[Antonio], Bueno-Orovio, A.[Alfonso], Sebastian, R.[Rafael],
Simplified Electrophysiology Modeling Framework to Assess Ventricular Arrhythmia Risk in Infarcted Patients,
FIMH21(531-539).
Springer DOI 2108
BibRef

Brahim, K.[Khawla], Qayyum, A.[Abdul], Lalande, A.[Alain], Boucher, A.[Arnaud], Sakly, A.[Anis], Meriaudeau, F.[Fabrice],
A deep learning approach for the segmentation of myocardial diseases,
ICPR21(4544-4551)
IEEE DOI 2105
Deep learning, Image segmentation, Solid modeling, Magnetic resonance imaging, Myocardium, Tools, Reliability BibRef

Boujnouni, I.E., Tali, A., Bentaleb, K.,
Capsule Network Based on Scalograms of Electrocardiogram for Myocardial Infarction Classification,
ISCV20(1-5)
IEEE DOI 2011
convolutional neural nets, diseases, electrocardiography, feature extraction, image classification, Capsule network BibRef

Rumindo, G.K.[Gerardo Kenny], Duchateau, N.[Nicolas], Croisille, P.[Pierre], Ohayon, J.[Jacques], Clarysse, P.[Patrick],
Strain-Based Parameters for Infarct Localization: Evaluation via a Learning Algorithm on a Synthetic Database of Pathological Hearts,
FIMH17(106-114).
Springer DOI 1706
BibRef

Tang, Y.C.[Yee Chia], Bishop, M.J.[Martin J.],
Application of Diffuse Optical Reflectance to Measure Myocardial Wall Thickness and Presence of Infarct Scar: A Monte Carlo Simulation Study,
FIMH15(248-255).
Springer DOI 1507
BibRef

Denisko, D.[Danielle], Oduneye, S.[Samuel], Krahn, P.[Philippa], Ghate, S.[Sudip], Lashevsky, I.[Ilan], Wright, G.[Graham], Pop, M.[Mihaela],
Analysis of Activation-Recovery Intervals from Intra-cardiac Electrograms in a Pre-clinical Chronic Model of Myocardial Infarction,
FIMH17(280-288).
Springer DOI 1706
BibRef

Viallon, M., Spaltenstein, J.[Joel], de Bourguignon, C., Vandroux, C., Ammor, A., Romero, W., Bernard, O., Croisille, P., Clarysse, P.,
Automated Quantification of Myocardial Infarction Using a Hidden Markov Random Field Model and the EM Algorithm,
FIMH15(256-264).
Springer DOI 1507
BibRef

Eftestol, T., Maloy, F., Engan, K., Kotu, L.P., Woie, L., Orn, S.,
A texture-based probability mapping for localisation of clinically important cardiac segments in the myocardium in cardiac magnetic resonance images from myocardial infarction patients,
ICIP14(2227-2231)
IEEE DOI 1502
Entropy BibRef

Fritz, T., Jarrousse, O., Keller, D.U.J., Seemann, G., Dössel, O.,
In Silico Analysis of the Impact of Transmural Myocardial Infarction on Cardiac Mechanical Dynamics for the 17 AHA Segments,
FIMH11(241-249).
Springer DOI 1105
BibRef

Suinesiaputra, A.[Avan], Frangi, A.F.[Alejandro F.], Kaandorp, T.A.M.[Theodorus A. M.], Lamb, H.J.[Hildo J.], Bax, J.J.[Jeroen J.], Reiber, J.H.C.[Johan H. C.], Lelieveldt, B.P.F.[Boudewijn P. F.],
Slice-Based Combination of Rest and Dobutamine: Stress Cardiac MRI Using a Statistical Motion Model to Identify Myocardial Infarction: Validation against Contrast-Enhanced MRI,
FIMH11(267-274).
Springer DOI 1105
BibRef

Esteves, T.[Tiago], Valente, M.[Mariana], Nascimento, D.S.[Diana S.], Pinto-do-Ó, P.[Perpétua], Quelhas, P.[Pedro],
Automatic and Semi-automatic Analysis of the Extension of Myocardial Infarction in an Experimental Murine Model,
IbPRIA11(151-158).
Springer DOI 1106
BibRef

Metwally, M.K.[Mohamed K.], El-Gayar, N.[Neamat], Osman, N.F.[Nael F.],
Improved Technique to Detect the Infarction in Delayed Enhancement Image Using K-Mean Method,
ICIAR10(II: 108-119).
Springer DOI 1006
BibRef

Kerckhoffs, R.C.P.[Roy C. P.], McCulloch, A.D.[Andrew D.], Omens, J.H.[Jeffrey H.], Mulligan, L.J.[Lawrence J.],
Effect of Pacing Site and Infarct Location on Regional Mechanics and Global Hemodynamics in a Model Based Study of Heart Failure,
FIMH07(350-360).
Springer DOI 0706
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Coronary Arteries, Carotid Arteries .


Last update:Oct 16, 2021 at 11:54:21