TY - CHAP
T1 - Strain rate tensor estimation in cine cardiac MRI based on elastic image registration
AU - Vegas Sánchez-Ferrero, Gonzalo
AU - Tristán Vega, Antonio
AU - Cordero Grande, Lucilio
AU - de la Higuera, Pablo Casaseca
AU - Aja Fernández, Santiago
AU - Martín Fernández , Marcos
AU - Alberola López, Carlos
PY - 2009
Y1 - 2009
N2 - In this work we propose an alternative method to estimate and visualize the Strain Rate Tensor (SRT) in Magnetic Resonance Images (MRI) when Phase Contrast MRI (PCMRI) and Tagged MRI (TMRI) are not available. This alternative is based on image processing techniques. Concretely, image registration algorithms are used to estimate the movement of the myocardium at each point. Additionally, a consistency checking method is presented to validate the accuracy of the estimates when no golden standard is available. Results prove that the consistency checking method provides an upper bound of the mean squared error of the estimate. Our experiments with real data show that the registration algorithm provides a useful deformation field to estimate the SRT fields. A classification between regional normal and dysfunctional contraction patterns, as compared with experts diagnosis, points out that the parameters extracted from the estimated SRT can represent these patterns. Additionally, a scheme for visualizing and analyzing the local behavior of the SRT field is presented.
AB - In this work we propose an alternative method to estimate and visualize the Strain Rate Tensor (SRT) in Magnetic Resonance Images (MRI) when Phase Contrast MRI (PCMRI) and Tagged MRI (TMRI) are not available. This alternative is based on image processing techniques. Concretely, image registration algorithms are used to estimate the movement of the myocardium at each point. Additionally, a consistency checking method is presented to validate the accuracy of the estimates when no golden standard is available. Results prove that the consistency checking method provides an upper bound of the mean squared error of the estimate. Our experiments with real data show that the registration algorithm provides a useful deformation field to estimate the SRT fields. A classification between regional normal and dysfunctional contraction patterns, as compared with experts diagnosis, points out that the parameters extracted from the estimated SRT can represent these patterns. Additionally, a scheme for visualizing and analyzing the local behavior of the SRT field is presented.
U2 - 10.1007/978-1-84882-299-3_17
DO - 10.1007/978-1-84882-299-3_17
M3 - Chapter
SN - 9781848822986
T3 - Advances in Pattern Recognition
SP - 355
EP - 379
BT - Tensors in Image Processing and Computer Vision
PB - Spring-Verlag London
CY - London
ER -