TY - GEN
T1 - Preventing signal degradation during elastic matching of noisy DCE-MR eye images
AU - Mosaliganti, Kishore
AU - Jia, Guang
AU - Heverhagen, Johannes
AU - Machiraju, Raghu
AU - Saltz, Joel
AU - Knopp, Michael
PY - 2006
Y1 - 2006
N2 - Motion during the acquisition of dynamic contrast enhanced MRI can cause model-fitting errors requiring co-registration. Clinical implementations use a pharmacokinetic model to determine lesion parameters from the contrast passage. The input to the model is the time-intensity plot from a region of interest (ROI) covering the lesion extent. Motion correction meanwhile involves interpolation and smoothing operations thereby affecting the time-intensity plots. This paper explores the trade-offs in applying an elastic matching procedure on the lesion detection and proposes enhancements. The method of choice is the 3D realization of the Demon's elastic matching procedure. We validate our enhancements using synthesized deformation of stationary datasets that also serve as ground-truth. The framework is tested on 42 human eye datasets. Hence, we show that motion correction is beneficial in improving the model-fit and yet needs enhancements to correct for the intensity reductions during parameter estimation.
AB - Motion during the acquisition of dynamic contrast enhanced MRI can cause model-fitting errors requiring co-registration. Clinical implementations use a pharmacokinetic model to determine lesion parameters from the contrast passage. The input to the model is the time-intensity plot from a region of interest (ROI) covering the lesion extent. Motion correction meanwhile involves interpolation and smoothing operations thereby affecting the time-intensity plots. This paper explores the trade-offs in applying an elastic matching procedure on the lesion detection and proposes enhancements. The method of choice is the 3D realization of the Demon's elastic matching procedure. We validate our enhancements using synthesized deformation of stationary datasets that also serve as ground-truth. The framework is tested on 42 human eye datasets. Hence, we show that motion correction is beneficial in improving the model-fit and yet needs enhancements to correct for the intensity reductions during parameter estimation.
UR - https://www.scopus.com/pages/publications/84883849378
U2 - 10.1007/11866565_102
DO - 10.1007/11866565_102
M3 - Conference contribution
C2 - 17354968
SN - 3540447075
SN - 9783540447078
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 832
EP - 839
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings
PB - Springer Verlag
T2 - 9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006
Y2 - 1 October 2006 through 6 October 2006
ER -