TY - GEN
T1 - Slice-based combination of rest and dobutamine-stress cardiac MRI using a statistical motion model to identify myocardial infarction
T2 - 6th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2011
AU - Suinesiaputra, Avan
AU - Frangi, Alejandro F.
AU - Kaandorp, Theodorus A.M.
AU - Lamb, Hildo J.
AU - Bax, Jeroen J.
AU - Reiber, Johan H.C.
AU - Lelieveldt, Boudewijn P.F.
PY - 2011
Y1 - 2011
N2 - This paper presents an automated method for regional wall motion abnormality detection (RWMA) from rest and stress cardiac MRI. The automated RWMA detection is based on a statistical shape model of myocardial contraction trained on slice-based myocardial contours from in ED and ES. A combination of rigid and non-rigid registrations is introduced to align a patient shape to the normokinetic myocardium model, where pure contractility information is kept. The automated RWMA method is applied to identify potentially infarcted myocardial segments from rest-stress MRI alone. In this study, 41 cardiac MRI studies of healthy subjects were used to build the statistical normokinetic model, while 12 myocardial infarct patients were included for validation. The rest-stress data produced a better separation between scar and normal segments compared to the rest-only data. The sensitivity, specificity and accuracy were increased by 34%, 30%, and 32%, respectively. The area under the ROC curve for the rest-stress data was improved to 0.87 compared to 0.63 for the rest-only data.
AB - This paper presents an automated method for regional wall motion abnormality detection (RWMA) from rest and stress cardiac MRI. The automated RWMA detection is based on a statistical shape model of myocardial contraction trained on slice-based myocardial contours from in ED and ES. A combination of rigid and non-rigid registrations is introduced to align a patient shape to the normokinetic myocardium model, where pure contractility information is kept. The automated RWMA method is applied to identify potentially infarcted myocardial segments from rest-stress MRI alone. In this study, 41 cardiac MRI studies of healthy subjects were used to build the statistical normokinetic model, while 12 myocardial infarct patients were included for validation. The rest-stress data produced a better separation between scar and normal segments compared to the rest-only data. The sensitivity, specificity and accuracy were increased by 34%, 30%, and 32%, respectively. The area under the ROC curve for the rest-stress data was improved to 0.87 compared to 0.63 for the rest-only data.
UR - http://www.scopus.com/inward/record.url?scp=79957635883&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-21028-0_33
DO - 10.1007/978-3-642-21028-0_33
M3 - Conference contribution
AN - SCOPUS:79957635883
SN - 9783642210273
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 267
EP - 274
BT - Functional Imaging and Modeling of the Heart - 6th International Conference, FIMH 2011, Proceedings
Y2 - 25 May 2011 through 27 May 2011
ER -