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.

Public Health Relevance

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.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Pai, Vinay Manjunath
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University of Miami School of Medicine
Schools of Medicine
Coral Gables
United States
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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
Ding, Xiao-Qi; Maudsley, Andrew A; Schweiger, Ulrich et al. (2017) 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 :271678X17697721
Mauler, Jörg; Maudsley, Andrew A; Langen, Karl-Josef et al. (2017) Spatial Relationship of Glioma Volume Derived from FET PET and Volumetric MRSI: a hybrid PET-MRI study. J Nucl Med :
Goryawala, Mohammed Z; Sheriff, Sulaiman; Stoyanova, Radka et al. (2017) Spectral decomposition for resolving partial volume effects in MRSI. Magn Reson Med :
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
Ding, Xiao-Qi; Maudsley, Andrew A; Sabati, Mohammad et al. (2016) Physiological neuronal decline in healthy aging human brain - An in vivo study with MRI and short echo-time whole-brain (1)H MR spectroscopic imaging. Neuroimage 137:45-51
Donadieu, Maxime; Le Fur, Yann; Lecocq, Angèle et al. (2016) Metabolic voxel-based analysis of the complete human brain using fast 3D-MRSI: Proof of concept in multiple sclerosis. J Magn Reson Imaging 44:411-9
Abdoli, Abas; Maudsley, Andrew A (2016) Phased-array combination for MR spectroscopic imaging using a water reference. Magn Reson Med 76:733-41
Abdoli, Abas; Stoyanova, Radka; Maudsley, Andrew A (2016) Denoising of MR spectroscopic imaging data using statistical selection of principal components. MAGMA 29:811-822

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