SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data
1H Magnetic Resonance Spectroscopy (MRS) is an important non-invasive tool for measuring brain metabolism, with numerous applications in the neuroscientific and clinical domains. In this work we present a new analysis pipeline (SLIPMAT), designed to extract high-quality, tissue-specific, spectral pr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811923003865 |
| _version_ | 1797798097078714368 |
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| author | Olivia Vella Andrew P. Bagshaw Martin Wilson |
| author_facet | Olivia Vella Andrew P. Bagshaw Martin Wilson |
| author_sort | Olivia Vella |
| collection | DOAJ |
| description | 1H Magnetic Resonance Spectroscopy (MRS) is an important non-invasive tool for measuring brain metabolism, with numerous applications in the neuroscientific and clinical domains. In this work we present a new analysis pipeline (SLIPMAT), designed to extract high-quality, tissue-specific, spectral profiles from MR spectroscopic imaging data (MRSI). Spectral decomposition is combined with spatially dependant frequency and phase correction to yield high SNR white and grey matter spectra without partial-volume contamination. A subsequent series of spectral processing steps are applied to reduce unwanted spectral variation, such as baseline correction and linewidth matching, before direct spectral analysis with machine learning and traditional statistical methods. The method is validated using a 2D semi-LASER MRSI sequence, with a 5-minute duration, from data acquired in triplicate across 8 healthy participants. Reliable spectral profiles are confirmed with principal component analysis, revealing the importance of total-choline and scyllo-inositol levels in distinguishing between individuals – in good agreement with our previous work. Furthermore, since the method allows the simultaneous measurement of metabolites in grey and white matter, we show the strong discriminative value of these metabolites in both tissue types for the first time. In conclusion, we present a novel and time efficient MRSI acquisition and processing pipeline, capable of detecting reliable neuro-metabolic differences between healthy individuals, and suitable for the sensitive neurometabolic profiling of in-vivo brain tissue. |
| first_indexed | 2024-03-13T03:59:22Z |
| format | Article |
| id | doaj.art-c570c044a00e4bafbd5db4ee799f5762 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-03-13T03:59:22Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-c570c044a00e4bafbd5db4ee799f57622023-06-22T05:02:30ZengElsevierNeuroImage1095-95722023-08-01277120235SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging dataOlivia Vella0Andrew P. Bagshaw1Martin Wilson2Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UKCentre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UKCorresponding author.; Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK1H Magnetic Resonance Spectroscopy (MRS) is an important non-invasive tool for measuring brain metabolism, with numerous applications in the neuroscientific and clinical domains. In this work we present a new analysis pipeline (SLIPMAT), designed to extract high-quality, tissue-specific, spectral profiles from MR spectroscopic imaging data (MRSI). Spectral decomposition is combined with spatially dependant frequency and phase correction to yield high SNR white and grey matter spectra without partial-volume contamination. A subsequent series of spectral processing steps are applied to reduce unwanted spectral variation, such as baseline correction and linewidth matching, before direct spectral analysis with machine learning and traditional statistical methods. The method is validated using a 2D semi-LASER MRSI sequence, with a 5-minute duration, from data acquired in triplicate across 8 healthy participants. Reliable spectral profiles are confirmed with principal component analysis, revealing the importance of total-choline and scyllo-inositol levels in distinguishing between individuals – in good agreement with our previous work. Furthermore, since the method allows the simultaneous measurement of metabolites in grey and white matter, we show the strong discriminative value of these metabolites in both tissue types for the first time. In conclusion, we present a novel and time efficient MRSI acquisition and processing pipeline, capable of detecting reliable neuro-metabolic differences between healthy individuals, and suitable for the sensitive neurometabolic profiling of in-vivo brain tissue.http://www.sciencedirect.com/science/article/pii/S1053811923003865NeurochemicalMetabolismMR spectroscopyMRSSpantmachine learning |
| spellingShingle | Olivia Vella Andrew P. Bagshaw Martin Wilson SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data NeuroImage Neurochemical Metabolism MR spectroscopy MRS Spant machine learning |
| title | SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data |
| title_full | SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data |
| title_fullStr | SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data |
| title_full_unstemmed | SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data |
| title_short | SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data |
| title_sort | slipmat a pipeline for extracting tissue specific spectral profiles from 1h mr spectroscopic imaging data |
| topic | Neurochemical Metabolism MR spectroscopy MRS Spant machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1053811923003865 |
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