2023
DOI: 10.3174/ajnr.a7895
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Exploratory Multisite MR Spectroscopic Imaging Shows White Matter Neuroaxonal Loss Associated with Complications of Type 1 Diabetes in Children

L.Y. Cai,
C. Tanase,
A.W. Anderson
et al.

Abstract: BACKGROUND AND PURPOSE: Type 1 diabetes affects over 200,000 children in the United States and is associated with an increased risk of cognitive dysfunction. Prior single-site, single-voxel MRS case reports and studies have identified associations between reduced NAA/Cr, a marker of neuroaxonal loss, and type 1 diabetes. However, NAA/Cr differences among children with various disease complications or across different brain tissues remain unclear. To better understand this phenomenon and the role of MRS in char… Show more

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Cited by 4 publications
(5 citation statements)
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“…Additionally, the quality of spectra for assessing water-soluble metabolites can be impacted by other structures in the brain, namely dural lipids which can produce large spectral distortions under 2.0 ppm. Using this framework, we demonstrate how spatiospectral processing allowed the identification and removal of CSI voxels affected by lipid interference as described by [7] in the T1D cohort.…”
Section: Resultsmentioning
confidence: 99%
See 4 more Smart Citations
“…Additionally, the quality of spectra for assessing water-soluble metabolites can be impacted by other structures in the brain, namely dural lipids which can produce large spectral distortions under 2.0 ppm. Using this framework, we demonstrate how spatiospectral processing allowed the identification and removal of CSI voxels affected by lipid interference as described by [7] in the T1D cohort.…”
Section: Resultsmentioning
confidence: 99%
“…Additionally, we note that a reliable spatiospectral framework opens the door for the development of new image processing routines for MRS and especially CSI. For instance, CSI suffers from known spatial biases, distortions, and nonlinearities as well as lower signal-to-noise ratios than SVS and interscanner variability [6], [7]. We hope that the introduction of this framework will spur image processing innovation in this field to overcome these challenges for more robust imaging and subsequent biological inference.…”
Section: Discussionmentioning
confidence: 99%
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