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Segmentation of MRS signals using ASPECT (analysis of SPectra using Eigenvector Decomposition of Targets)
J R Roebuck1, J P Windham, D O Hearshen
1Bioengineering Program, University of Michigan, Ann Arbor 48109.
Abstract:
Efforts to minimize the effects of partial volume contamination (PVC) in in vivo magnetic resonance spectroscopy (MRS) have focused upon improving the sensitivity and efficiency of spatially localized MRS measurements. Such improvements may improve spatial resolution and reduce the time required to acquire multiple spectra, however, PVC can affect in vivo spectra at any resolution. In this paper, a model for segmenting in vivo MRS signals compromised by PVC in selected applications is introduced. The segmentation algorithm used is linear and is based on filters originally developed for image processing applications. The model is developed from first principles and evaluated using computer simulations. It is suited for segmenting multivoxel or chemical shift imaging data, and can be used with spectra acquired at any spatial resolution. It is used to estimate the size of the partial volumes contributing to a voxel compromised by PVC and the spatially selective signal components that would be expected to arise from these partial volumes if they could be measured directly. Several spectral perturbants present in in vivo MRS measurements violate the linearity assumptions underlying the model and produce systematic errors that must be accounted for. A number of perturbants are discussed, and the potential in vivo applications of the model are illustrated using solvent-suppressed 1H-CSI spectra from the normal human brain.
Insights
This study introduces a linear model to segment in vivo magnetic resonance spectroscopy (MRS) signals affected by partial volume contamination (PVC). The model estimates partial volume contributions and spatially selective signals, improving data analysis in the human brain.
Area of Science:
- Magnetic Resonance Spectroscopy (MRS)
- Medical Imaging Analysis
- Biomedical Signal Processing
Background:
- Partial volume contamination (PVC) significantly impacts in vivo MRS data quality.
- Current methods focus on improving spatial resolution, but PVC affects spectra regardless of resolution.
Purpose of the Study:
- To introduce a novel linear model for segmenting in vivo MRS signals compromised by PVC.
- To estimate partial volume contributions and spatially selective signal components within voxels.
Main Methods:
- Developed a linear segmentation algorithm based on image processing filters.
- Utilized first principles for model development and computer simulations for evaluation.
- Applied the model to multivoxel or chemical shift imaging data at any spatial resolution.
Main Results:
- The model effectively estimates the size of partial volumes contributing to PVC-affected voxels.
- It can determine spatially selective signal components expected from these partial volumes.
- Identified spectral perturbants that violate linearity assumptions and cause systematic errors.
Conclusions:
- The developed model provides a method for segmenting in vivo MRS signals affected by PVC.
- It is applicable to various data types, including 1H-CSI from the human brain.
- Understanding and accounting for model limitations is crucial for accurate in vivo applications.