Partial volume correction of magnetic resonance spectroscopic imaging
Yao Lu, Dee H. Wu, Vincent A. Magnotta
Abstract
Yao Lu, Dee H. Wu, Vincent A. Magnotta
Abstract
The ability to study the biochemical composition of the brain is becoming important to better understand neurodegenerative and neurodevelopmental disorders. Magnetic Resonance Spectroscopy (MRS) can non-invasively provide quantification of brain metabolites in localized regions. The reliability of MRS is limited in part due to partial volume artifacts. This results from the relatively large voxels that are required to acquire sufficient signal-to-noise ratios for the studies. Partial volume artifacts result when a MRS voxel contains a mixture of tissue types. Concentrations of metabolites vary from tissue to tissue. When a voxel contains a heterogeneous tissue composition, the spectroscopic signal acquired from this voxel will consist of the signal from different tissues making reliable measurements difficult. We have developed a novel tool for the estimation of partial volume tissue composition within MRS voxels thus allowing for the correction of partial volume artifacts. In addition, the tool can localize MR spectra to anatomical regions of interest. The tool uses tissue classification information acquired as part of a structural MR scan for the same subject. The tissue classification information is co-registered with the spectroscopic data. The user can quantify the partial volume composition of each voxel and use this information as covariates for metabolite concentrations.
OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
The ability to study the biochemical composition of the brain is becoming important to better understand neurodegenerative and neurodevelopmental disorders. Magnetic Resonance Spectroscopy (MRS) can non-invasively provide quantification of brain metabolites in localized regions. The reliability of MRS is limited in part due to partial volume artifacts. This results from the relatively large voxels that are required to acquire sufficient signal-to-noise ratios for the studies. Partial volume artifacts result when a MRS voxel contains a mixture of tissue types. Concentrations of metabolites vary from tissue to tissue. When a voxel contains a heterogeneous tissue composition, the spectroscopic signal acquired from this voxel will consist of the signal from different tissues making reliable measurements difficult. We have developed a novel tool for the estimation of partial volume tissue composition within MRS voxels thus allowing for the correction of partial volume artifacts. In addition, the tool can localize MR spectra to anatomical regions of interest. The tool uses tissue classification information acquired as part of a structural MR scan for the same subject. The tissue classification information is co-registered with the spectroscopic data. The user can quantify the partial volume composition of each voxel and use this information as covariates for metabolite concentrations.
Key concepts: Voxel, Partial volume, Magnetic resonance imaging, Volume (thermodynamics), Brain tissue, Computer science, SIGNAL (programming language), Nuclear magnetic resonance