TY - GEN
T1 - Spatio-temporal dimension reduction of cardiac motion for group-wise analysis and statistical testing
AU - McLeod, Kiristin
AU - Seiler, Christof
AU - Sermnesant, Maxime
AU - Pennec, Xavier
PY - 2013
Y1 - 2013
N2 - Given the observed abnormal motion dynamics of patients with heart conditions, quantifying cardiac motion in both normal and pathological cases can provide useful insights for therapy planning. In order to be able to analyse the motion over multiple subjects in a robust manner, it is desirable to represent the motion by a low number of parameters. We propose a reduced order cardiac motion model, reduced in space through a polyaffine model, and reduced in time by statistical model order reduction. The method is applied to a data-set of synthetic cases with known ground truth to validate the accuracy of the left ventricular motion tracking, and to validate a patient-specific reduced-order motion model. Population-based statistics are computed on a set of 15 healthy volunteers to obtain separate spatial and temporal bases. Results demonstrate that the reduced model can efficiently detect abnormal motion patterns and even allowed to retrospectively reveal abnormal unnoticed motion within the control subjects.
AB - Given the observed abnormal motion dynamics of patients with heart conditions, quantifying cardiac motion in both normal and pathological cases can provide useful insights for therapy planning. In order to be able to analyse the motion over multiple subjects in a robust manner, it is desirable to represent the motion by a low number of parameters. We propose a reduced order cardiac motion model, reduced in space through a polyaffine model, and reduced in time by statistical model order reduction. The method is applied to a data-set of synthetic cases with known ground truth to validate the accuracy of the left ventricular motion tracking, and to validate a patient-specific reduced-order motion model. Population-based statistics are computed on a set of 15 healthy volunteers to obtain separate spatial and temporal bases. Results demonstrate that the reduced model can efficiently detect abnormal motion patterns and even allowed to retrospectively reveal abnormal unnoticed motion within the control subjects.
KW - Algorithms
KW - Data Interpretation, Statistical
KW - Heart Ventricles/diagnostic imaging
KW - Humans
KW - Image Enhancement/methods
KW - Image Interpretation, Computer-Assisted/methods
KW - Motion
KW - Myocardial Contraction/physiology
KW - Reference Values
KW - Reproducibility of Results
KW - Sensitivity and Specificity
KW - Spatio-Temporal Analysis
KW - Ultrasonography
KW - Ventricular Function, Left/physiology
U2 - 10.1007/978-3-642-40763-5_62
DO - 10.1007/978-3-642-40763-5_62
M3 - Conference article in proceeding
C2 - 24579178
VL - 16
T3 - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
SP - 501
EP - 508
BT - International Conference on Medical Image Computing and Computer-Assisted Intervention
ER -