Spectral decomposition for resolving partial volume effects in <scp>MRSI</scp>

Goryawala, Mohammed; Sheriff, Sulaiman; Stoyanova, Radka; Maudsley, Andrew A.

doi:10.1002/mrm.26991

Cited by 14 publications

(17 citation statements)

References 26 publications

(52 reference statements)

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“…Voxels not meeting the quality evaluation criteria should be excluded, and for spectral summation the phase and frequency correction must be performed prior to averaging. Both methods can be extended to incorporate information on the tissue volume fraction in each voxel to separate multiple contributions such as gray and white matter 193,194 . Other examples include measurements over specific neuronal tracts, 195,196 smaller tissue regions with volume contributions from neighboring regions, 194 and different tumor regions 197,198 .…”

Section: Data Analysis Methodsmentioning

confidence: 99%

“…Both methods can be extended to incorporate information on the tissue volume fraction in each voxel to separate multiple contributions such as gray and white matter 193,194 . Other examples include measurements over specific neuronal tracts, 195,196 smaller tissue regions with volume contributions from neighboring regions, 194 and different tumor regions 197,198 . ROIs can also be automatically defined using atlas registration methods, which greatly benefit from having fully 3D information to support nonlinear registration 86,192 …”

Section: Data Analysis Methodsmentioning

confidence: 99%

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Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations

et al. 2020

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Magnetic resonance spectroscopic imaging (MRSI) offers considerable promise for monitoring metabolic alterations associated with disease or injury; however, to date, these methods have not had a significant impact on clinical care, and their use remains largely confined to the research community and a limited number of clinical sites. The MRSI methods currently implemented on clinical MRI instruments have remained essentially unchanged for two decades, with only incremental improvements in sequence implementation. During this time, a number of technological developments have taken place that have already greatly benefited the quality of MRSI measurements within the research community and which promise to bring advanced MRSI studies to the point where the technique becomes a true imaging modality, while making the traditional review of individual spectra a secondary requirement. Furthermore, the increasing use of biomedical MR spectroscopy studies has indicated clinical areas where advanced MRSI methods can provide valuable information for clinical care. In light of this rapidly changing technological environment and growing understanding of the value of MRSI studies for biomedical studies, this article presents a consensus from a group of experts in the field that reviews the state-of-the-art for clinical proton MRSI studies of the human brain, recommends minimal standards for further development of vendor-provided MRSI implementations, and identifies areas which need further technical development.

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Section: Data Analysis Methodsmentioning

confidence: 99%

Section: Data Analysis Methodsmentioning

confidence: 99%

Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations

et al. 2020

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“…1). This has been demonstrated previously with a high-resolution whole-brain MRSI protocol (Goryawala et al, 2018). A dedicated comparison between chemometric and conventional fitting approaches is warranted for larger MRS datasets, and we anticipate this will be aided by recent effects to lower barriers associated with MRS data sharing Soher et al, 2022).…”

Section: Discussionmentioning

confidence: 73%

“…To address this limitation, we employ a 2D semi-LASER magnetic resonance spectroscopic imaging (MRSI) sequence to acquire spectral data from an axial slice just above the corpus collosum. These spectra, containing a mixture of white and grey matter contributions, are first aligned in terms of phase and frequency (Wilson, 2019), before applying spectral decomposition (Goryawala et al, 2018) to obtain "pure" white and grey matter spectra with high SNR and spectral resolution. A series of spectral processing steps are then applied to minimise the expected spectral variations related to experimental, rather than metabolic, variability.…”

Section: Introductionmentioning

confidence: 99%

SLIPMAT: a pipeline for extracting tissue-specific spectral profiles from¹H MR spectroscopic imaging data

Vella¹,

Bagshaw²,

Wilson³

2022

Preprint

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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.

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“…A second, often overlooked, advantage of MRSI is the ability to separate white and gray matter components using post-processing methods. This is often done by linear regression (Hetherington et al, 1996;Tal, Kirov, Grossman, & Gonen, 2012) or spectral decomposition methods (Goryawala, Sheriff, Stoyanova, & Maudsley, 2018), which combine information from multiple voxels with knowledge of the relative tissue content in each voxel. Linear regression can be applied in any number of (n≥2) voxels to investigate small regions such as the hippocampus (<10 voxels; Meyer et al, 2016), larger areas such as 2D slabs (McLean et al, 2000;Gasparovic et al, 2011), in entire brain lobes (Maudsley et al, 2009), and/or in global brain white matter/gray matter metabolism (100s of voxels; Tal et al, 2012).…”

Section: Mr Spectroscopic Imaging (Mrsi)mentioning

confidence: 99%

The Clinical Utility of Magnetic Resonance Spectroscopy in Traumatic Brain Injury: Recommendations from the ENIGMA MRS Working Group

Bartnik‐Olson¹,

Tate²,

Babikian³

et al. 2019

Preprint

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Proton magnetic resonance spectroscopy provides a non-invasive and quantitative measure of brain metabolites. Traumatic brain injury impacts cerebral metabolism and a number of research groups have successfully used this technique as a biomarker of injury and/or outcome in both pediatric and adult TBI populations. However, this technique is underutilized, with studies being performed primarily at centers with access to MR research support. In this paper we present a technical introduction to the acquisition and analysis of in vivo magnetic resonance spectroscopy and review magnetic resonance spectroscopy findings in different injury populations. In addition, we propose a basic data acquisition scheme that can be added to any imaging protocol, regardless of clinical magnetic resonance platform. We outline a number of considerations for study design as a way of encouraging the use of magnetic resonance spectroscopy in the study of traumatic brain injury, as well as recommendations to improve data harmonization across groups already using this technique.

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Spectral decomposition for resolving partial volume effects in MRSI

Cited by 14 publications

References 26 publications

Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations

Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations

SLIPMAT: a pipeline for extracting tissue-specific spectral profiles from¹H MR spectroscopic imaging data

The Clinical Utility of Magnetic Resonance Spectroscopy in Traumatic Brain Injury: Recommendations from the ENIGMA MRS Working Group

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Spectral decomposition for resolving partial volume effects in MRSI

Cited by 14 publications

References 26 publications

Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations

Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations

SLIPMAT: a pipeline for extracting tissue-specific spectral profiles from1H MR spectroscopic imaging data

The Clinical Utility of Magnetic Resonance Spectroscopy in Traumatic Brain Injury: Recommendations from the ENIGMA MRS Working Group

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Product

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SLIPMAT: a pipeline for extracting tissue-specific spectral profiles from¹H MR spectroscopic imaging data