TY - GEN
T1 - Computer-aided infarction identification from cardiac CT images
T2 - 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
AU - Wong, Ken C.L.
AU - Tee, Michael
AU - Chen, Marcus
AU - Bluemke, David A.
AU - Summers, Ronald M.
AU - Yao, Jianhua
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Compared with global measurements such as ejection fraction, regional myocardial deformation can better aid detection of cardiac dysfunction. Although tagged and strain-encoded MR images can provide such regional information, they are uncommon in clinical routine. In contrast, cardiac CT images are more common with lower cost, but only provide motion of cardiac boundaries and additional constraints are required to obtain the myocardial strains. To verify the potential of contrast-enhanced CT images on computer-aided infarction identification, we propose a biomechanical approach combined with the support vector machine (SVM). A biomechanical model is used with deformable image registration to estimate 3D myocardial strains from CT images, and the regional strains and CT image intensities are input to the SVM classifier for regional infarction identification. Cross-validations on ten canine image sequences with artificially induced infarctions showed that the normalized radial and first principal strains were the most discriminative features, with respective classification accuracies of 87±13% and 84±10% when used with the normalized CT image intensity.
AB - Compared with global measurements such as ejection fraction, regional myocardial deformation can better aid detection of cardiac dysfunction. Although tagged and strain-encoded MR images can provide such regional information, they are uncommon in clinical routine. In contrast, cardiac CT images are more common with lower cost, but only provide motion of cardiac boundaries and additional constraints are required to obtain the myocardial strains. To verify the potential of contrast-enhanced CT images on computer-aided infarction identification, we propose a biomechanical approach combined with the support vector machine (SVM). A biomechanical model is used with deformable image registration to estimate 3D myocardial strains from CT images, and the regional strains and CT image intensities are input to the SVM classifier for regional infarction identification. Cross-validations on ten canine image sequences with artificially induced infarctions showed that the normalized radial and first principal strains were the most discriminative features, with respective classification accuracies of 87±13% and 84±10% when used with the normalized CT image intensity.
UR - http://www.scopus.com/inward/record.url?scp=84950994768&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84950994768&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-24571-3_18
DO - 10.1007/978-3-319-24571-3_18
M3 - Conference contribution
AN - SCOPUS:84950994768
SN - 9783319245706
SN - 9783319245706
SN - 9783319245706
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 144
EP - 151
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings
A2 - Hornegger, Joachim
A2 - Frangi, Alejandro F.
A2 - Wells, William M.
A2 - Frangi, Alejandro F.
A2 - Navab, Nassir
A2 - Hornegger, Joachim
A2 - Navab, Nassir
A2 - Wells, William M.
A2 - Wells, William M.
A2 - Frangi, Alejandro F.
A2 - Hornegger, Joachim
A2 - Navab, Nassir
PB - Springer Verlag
Y2 - 5 October 2015 through 9 October 2015
ER -