The MIDAS (Metabolic Imaging Data Analysis System) software package provides unique functionality for processing, display, and analysis of MR Spectroscopic imaging (MRSI) data, and includes close integration with structural MRI and other imaging modalities. This system supports processing for a volumetric echo-planar spectroscopic imaging (EPSI) acquisition that enables mapping of tissue metabolites and is of particular value for multiparametric in vivo imaging studies of the brain. The EPSI and MIDAS software have been distributed to multiple sites worldwide and are being used for multiple neuroimaging research applications. Under three specific aims, this project will maintain and develop new functionality for these packages.
Aim 1 will add new spectroscopy and image processing functions, which will extend statistical image analyses, improve performance of spectral analysis for overlapping signal contributions, and implement improved methods for brain temperature mapping.
Aim 2 will support the EPSI acquisition through future system upgrades, add motion correction, extend spatial sampling acceleration, implement multi-echo sampling for improved sampling accuracy, and enable studies at higher field strengths. Studies at 7 Tesla will evaluate these methods for mapping of additional brain metabolites.
Aim 3 will support dissemination of the developed software products with continued development of user documentation, educational materials and seminars, and maintain the project web site. Through these aims, this project will provide continued support and development for a highly innovative suite of functions that facilitate clinical and basic biomedical imaging research studies using MRI and MRSI.
This project will further develop, maintain, and disseminate novel data acquisition and processing software packages that provide volumetric non-invasive mapping of tissue metabolites using magnetic resonance spectroscopy. The developed methods have widespread applications for clinical diagnostic purposes and biomedical studies.
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|Goryawala, Mohammed Z; Sheriff, Sulaiman; Stoyanova, Radka et al. (2018) Spectral decomposition for resolving partial volume effects in MRSI. Magn Reson Med 79:2886-2895|
|Mauler, Jörg; Maudsley, Andrew A; Langen, Karl-Josef et al. (2018) Spatial Relationship of Glioma Volume Derived from 18F-FET PET and Volumetric MR Spectroscopy Imaging: A Hybrid PET/MRI Study. J Nucl Med 59:603-609|
|Maudsley, Andrew A (2018) Lesion segmentation for MR spectroscopic imaging using the convolution difference method. Magn Reson Med :|
|Goryawala, Mohammed Z; Heros, Deborah O; Komotar, Ricardo J et al. (2018) Value of diffusion kurtosis imaging in assessing low-grade gliomas. J Magn Reson Imaging 48:1551-1558|
|Gurbani, Saumya S; Schreibmann, Eduard; Maudsley, Andrew A et al. (2018) A convolutional neural network to filter artifacts in spectroscopic MRI. Magn Reson Med 80:1765-1775|
|Zhang, Yue; Taub, Edward; Salibi, Nouha et al. (2018) Comparison of reproducibility of single voxel spectroscopy and whole-brain magnetic resonance spectroscopy imaging at 3T. NMR Biomed 31:e3898|
|Maudsley, Andrew A; Govind, Varan; Saigal, Gaurav et al. (2017) Longitudinal MR Spectroscopy Shows Altered Metabolism in Traumatic Brain Injury. J Neuroimaging 27:562-569|
|Maudsley, Andrew A; Goryawala, Mohammed Z; Sheriff, Sulaiman (2017) Effects of tissue susceptibility on brain temperature mapping. Neuroimage 146:1093-1101|
|Lopez, Christopher J; Nagornaya, Natalya; Parra, Nestor A et al. (2017) Association of Radiomics and Metabolic Tumor Volumes in Radiation Treatment of Glioblastoma Multiforme. Int J Radiat Oncol Biol Phys 97:586-595|
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