scholarly journals Lipid‐suppressed and tissue‐fraction corrected metabolic distributions in human central brain structures using 2D 1 H magnetic resonance spectroscopic imaging at 7 T

2020 ◽  
Vol 10 (12) ◽  
Author(s):  
Alex A. Bhogal ◽  
Tommy A. A. Broeders ◽  
Lisan Morsinkhof ◽  
Mirte Edens ◽  
Sahar Nassirpour ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Spectroscopic Imaging ◽  
Brain Structures ◽  
Magnetic Resonance Spectroscopic Imaging ◽  
Central Brain ◽  
Tissue Fraction

scholarly journals Lipid suppressed and tissue-fraction corrected metabolic distributions in human central brain structures using 2D 1H Magnetic Resonance Spectroscopic Imaging at 7 tesla

2020 ◽  
Author(s):  
Alex A. Bhogal ◽  
Tommy A.A. Broeders ◽  
Lisan Morsinkhof ◽  
Mirte Edens ◽  
Sahar Nassirpour ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
High Resolution ◽  
Spectroscopic Imaging ◽  
Magnetic Resonance Spectroscopic Imaging ◽  
Brain Diseases ◽  
7 Tesla ◽  
Partial Volume ◽  
Scan Time ◽  
Central Brain

ABSTRACTMagnetic resonance spectroscopic imaging (MRSI) has the potential to add a layer of understanding of the neurobiological mechanisms underlying brain diseases, disease progression, and treatment efficacy. Limitations related to metabolite fitting of low SNR data, signal variations due to partial volume effects, acquisition and extra-cranial lipid artefacts, along with clinically relevant aspects such as scan-time constraints, are among the factors that hinder the widespread implementation of in vivo MRSI. The aim of this work was to address these factors and to develop an acquisition, reconstruction and post-processing pipeline to derive lipid suppressed metabolite values based on Free Induction Decay (FID-MRSI) measurements made using a 7 tesla MR scanner. Anatomical images were used to perform high-resolution (1mm3) partial-volume correction to account for grey matter, white matter and cerebral-spinal fluid signal contributions. Implementation of automatic quality control thresholds and normalization of metabolic maps from 23 subjects to the MNI standard atlas facilitated the creation of high-resolution average metabolite maps of several clinically relevant metabolites in central brain regions, while accounting for macromolecular distributions. Reported metabolite values include glutamate, choline, (phospo)creatine, myo-inositol, glutathione, N-acetyl aspartyl glutamate(and glutamine) and N-acetyl aspartate. MNI-registered average metabolite maps facilitate group-based analysis; thus offering the possibility to mitigate uncertainty in variable MRSI.

Grey matter abnormalities in multiple sclerosis: proton magnetic resonance spectroscopic imaging

2001 ◽  
Vol 7 (4) ◽  
pp. 221-226 ◽  
Author(s):  
Rakesh Sharma ◽  
Ponnada A Narayana ◽  
Jerry S Wolinsky
Keyword(s):  
Multiple Sclerosis ◽  
Magnetic Resonance ◽  
Proton Magnetic Resonance ◽  
Magnetization Transfer ◽  
Spectroscopic Imaging ◽  
Magnetic Resonance Spectroscopic Imaging ◽  
Relapsing Remitting ◽  
Cortical Gray Matter ◽  
Magnetic Resonance Imaging Mri ◽  
Mri Characteristics

Pathologically defined abnormalities in the cortical gray matter (GM) are well described in multiple sclerosis (MS) but are infrequently seen by conventional magnetic resonance imaging (MRI). We systematically evaluated 52 relapsing - remitting MS patients and 20 normal volunteers with high resolution MRI and short echo proton magnetic resonance spectroscopic imaging (MRSI). Individual tissue contributions to the spectroscopic voxels were estimated based on MRI that incorporated both CSF suppression and magnetization transfer, or double inversion images in which both CSF and GM were suppressed. Strong resonances in the 0.8 to 1.5 p.p.m. spectral region were observed in 13 MS patients. Image segmentation based on the MRI characteristics of tissues contributing to the spectroscopic voxels showed that these additional peaks originated mainly from GM. The presence of these additional peaks suggests that the normal appearance GM on MRI, is biochemically abnormal in a substantial proportion of relapsing-remitting MS patients.

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