The MIDAS (Metabolic Imaging Data Analysis System) software package provides comprehensive and unique functionality for processing, display, and analysis of MR Spectroscopic imaging (MRSI) data, and includes close integration with information available from structural and other parametric MRI modalities. This system supports a volumetric echo- planar spectroscopic imaging (EPSI) acquisition that provides high-resolution images of a wide extent of the brain, including cortical surface regions, which has been implemented on MR instruments from three major manufacturers. The EPSI and MIDAS software have been distributed to multiple sites worldwide. Under four 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 include capabilities for volumetric mapping of brain temperature, metabolite T2, and parametric changes between studies.
Aim 2 will improve performance, software organization, and maintainability by refactoring historically diverse pieces of code under a common programming environment.
Aim 3 will support the EPSI acquisition through future system upgrades and add new capabilities to the data acquisition methods.
Aim 4 will continue development of user documentation, educational materials, and the project web site, and make available data acquired under previous projects. 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 make available to other investigators 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.
| 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 |
| Ding, Xiao-Qi; Maudsley, Andrew A; Schweiger, Ulrich et al. (2018) Effects of a 72 hours fasting on brain metabolism in healthy women studied in vivo with magnetic resonance spectroscopic imaging. J Cereb Blood Flow Metab 38:469-478 |
| 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 |
| 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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