Preventing signal degradation during elastic matching of noisy DCE-MR eye images

Authors:
Kishore Mosaliganti;Guang Jia;Johannes Heverhagen;Raghu Machiraju;Joel Saltz;Michael Knopp
Affiliations:
Department of Computer Science and Engineering, The Ohio State University, Columbus, OH;Department of Radiology, The Ohio State University, Columbus, OH;Department of Radiology, The Ohio State University, Columbus, OH;Department of Computer Science and Engineering, The Ohio State University, Columbus, OH;Department of Biomedical Informatics, The Ohio State University, Columbus, OH;Department of Radiology, The Ohio State University, Columbus, OH
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2006

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Abstract

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.