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Published on: February 23, 2017
Preventing signal degradation during elastic matching of noisy DCE-MR eye images
Kishore Mosaliganti1, Guang Jia, Johannes Heverhagen
1Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA. kishore@bmi.osu.edu
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.
Insights
Motion correction in dynamic contrast-enhanced MRI improves pharmacokinetic model fitting for lesion detection. Enhancements are proposed to address intensity reductions during parameter estimation, optimizing diagnostic accuracy.
Area of Science:
- Medical Imaging
- Radiology
- Biophysics
Background:
- Dynamic contrast-enhanced MRI (DCE-MRI) is crucial for lesion characterization.
- Motion artifacts during DCE-MRI acquisition can lead to pharmacokinetic model-fitting errors.
- Current motion correction methods may alter time-intensity plots, affecting parameter estimation.
Purpose of the Study:
- To explore trade-offs of elastic matching for motion correction in DCE-MRI.
- To propose enhancements to the Demon's elastic matching procedure for improved lesion detection.
- To evaluate the impact of motion correction on pharmacokinetic model fitting.
Main Methods:
- Utilized a 3D elastic matching procedure (Demon's algorithm).
- Validated enhancements using synthesized deformations of stationary datasets as ground-truth.
- Tested the framework on 42 human eye DCE-MRI datasets.
Main Results:
- Motion correction was found to be beneficial for improving pharmacokinetic model-fit.
- Elastic matching enhancements were proposed to mitigate motion-induced errors.
- Intensity reductions during parameter estimation necessitate further improvements in motion correction.
Conclusions:
- Motion correction is essential for accurate pharmacokinetic modeling in DCE-MRI.
- The proposed elastic matching enhancements show promise but require further refinement.
- Addressing intensity reduction is critical for robust lesion parameter estimation in DCE-MRI.
