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Retrospective artifact elimination in MEGA-PRESS using a correlation approach
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-7809-2481
2019 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 81, no 4, p. 2223-2237Article in journal (Refereed) Published
Abstract [en]

Purpose

To develop a method for retrospective artifact elimination of MRS data. This retrospective method was based on an approach that combines jackknife analyses with the correlation of spectral windows, and therefore termed “JKC.”

Methods

Twelve healthy volunteers performed 3 separate measurement protocols using a 3T MR system. One protocol consisted of 2 cerebellar MEGA‐PRESS measurements: 1 reference and 1 measurement including head movements. One‐third of the artifact‐influenced datasets were treated as training data for the implementation the JKC method, and the rest were used for validation.

Results

The implemented JKC method correctly characterized most of the validation data. Additionally, after elimination of the detected artifacts, the resulting concentrations were much closer to those computed for the reference datasets. Moreover, when the JKC method was applied to the reference data, the estimated concentrations were not affected, compared with standard averaging.

Conclusion

The implemented JKC method can be applied without any extra cost to MRS data, regardless of whether the dataset has been contaminated by artifacts. Furthermore, the results indicate that the JKC method could be used as a quality control of a dataset, or as an indication of whether a shift in voxel placement has occurred during the measurement.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019. Vol. 81, no 4, p. 2223-2237
National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-154902DOI: 10.1002/mrm.27590PubMedID: 30417930Scopus ID: 2-s2.0-85056358584OAI: oai:DiVA.org:liu-154902DiVA, id: diva2:1293319
Available from: 2019-03-04 Created: 2019-03-04 Last updated: 2019-03-26Bibliographically approved
In thesis
1. Neurotransmitter Imaging of the Human Brain: Detecting γ-Aminobutyric Acid (GABA) Using Magnetic Resonance Spectroscopy
Open this publication in new window or tab >>Neurotransmitter Imaging of the Human Brain: Detecting γ-Aminobutyric Acid (GABA) Using Magnetic Resonance Spectroscopy
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Introduction: In this thesis, MEGA-edited Magnetic Resonance Spectroscopy (MRS) has been used for the purpose of non-invasive detection of !- aminobutyric acid (GABA) within the brain. GABA is the main inhibitory neurotransmitter in the human central nervous system, and glutamate is the corresponding main excitatory neurotransmitter. A balance between GABA and glutamate is crucial for healthy neurotransmission within the brain, and regional altered concentrations have been linked to certain neurological disorders. However, it is challenging to measure GABA, and special editing approaches are needed in order to allow reliable quantification. In addition, the GABA measurement is further complicated due to disturbances such as movements during the acquisition that may lead to artifacts in the resulting spectrum. This thesis can be divided into two sections, where the first section focuses on three clinical applications (narcolepsy, irritable bowel syndrome (IBS), and essential tremor (ET)), which were all investigated using MEGA-edited single- voxel spectroscopy (SVS). The second section focuses on method development, where two statistical retrospective approaches were investigated for the purpose of improving MEGA-edited data. In addition, a new MRS imaging (MRSI) pulse sequence with the purpose of GABA detection using a high spatial resolution, short acquisition time, and full brain coverage was also investigated.

Materials and Methods: In total, 164 participants were included and 272 MRS measurements were performed with the voxel placed in the medial prefrontal cortex (mPFC, 136), thalamus (32), and cerebellum (104) using two different but “identical” MR systems. Nineteen narcolepsy patients and 21 matched healthy controls performed an fMRI working memory task using a simultaneous EEG followed by an mPFC GABA-edited MRS measurement. Sixty-four IBS patients and 32 matched healthy controls underwent an mPFC GABA-edited MRS measurement followed by resting state fMRI. In addition, psychological symptoms were assessed using questionnaires. Ten ET patients and six matched healthy controls underwent four GABA-edited MRS measurements with the voxels placed in the thalamus and cerebellum. In this study, the symptom severity was investigated using the essential tremor rating scale (ETRS). All clinical MRS datasets were analyzed using conventional methods for post-processing and quantification. Furthermore, 12 volunteers were recruited for the purpose of investigating statistical retrospective approaches for artifact detection and elimination of MRS data. Each participant underwent three reference measurements and three measurements with induced head movements conducted according to a movement paradigm. Order statistic filtering (OSF) and jackknife correlation (JKC) were investigated as regards to the elimination of artifact-influenced spectra and reliability of the resulting concentrations. Finally, phantom measurements were performed for the purpose of investigating MEGA-edited MRSI.

Results: In narcolepsy, a trend-level association was observed between the mPFC MRS concentrations and increased deactivation within the default mode network during the working memory task. A significantly higher mPFC GABA+ concentration was observed in IBS patients with a high severity of comorbid anxiety. In ET, a positive correlation was observed between cerebellar GABA+/Glx ratios and tremor severity. Moreover, movements during the measurement decreased the concentration estimates due to signal loss in the spectra. Both OSF and JKC resulted in trend-level improvement of the signal- intense metabolites in spectrum when artifacts were present in the data, while performing equally as well as conventional analysis methodology when no intentional movements were present in the data. Finally, using the fast MEGA- edited multi-voxel sequence developed for a conventional clinical scanner, our phantom measurements showed that GABA was detectable using a 1:45 min acquisition time and an MRSI voxel size of 1 mL.

Discussion: Several challenges such as time constraints, large voxel sizes, and protocol design were encountered when performing SVS MEGA-PRESS in the clinical research settings. In addition, artifacts in the MRS data originating for example, from motions, negatively impacted the resulting averaged spectra, which was evident in both data from clinical populations and healthy controls. In the presence of artifacts in the data, both OSF and JKC improved the SVS MEGA-edited spectra. In addition, the implemented JKC method can be used not only for artifact detection, but also as a generally applicable retrospective technique for the quality control of a dataset, or as an indication of whether a shift in voxel placement occurred during the measurement. Using the MEGA-edited MRSI pulse sequence, there are many technical challenges, including detrimental effects from eddy currents, spurious echoes, and field inhomogeneities. Even though there are many technical challenges when using MEGA-edited MRSI, an optimized version of the MRSI sequence would be extremely valuable in clinical research applications where high spatial resolution and short acquisition times are highly desired.

Conclusions: OSF and JKC improved the metabolite quantification when artifacts were present in the data, and JKC was preferable. Although there are many technical challenges, MEGA-edited MRSI with full brain coverage in combination with a minimal voxel size for the purpose of GABA detection, would be extremely useful in clinical research applications where disorders such as narcolepsy, IBS, or ET, are investigated.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 94
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1667
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-154904 (URN)10.3384/diss.diva-154904 (DOI)9789176851197 (ISBN)
Public defence
2019-04-12, Hugo Theorell, Campus US, Linköping, 13:00 (English)
Opponent
Supervisors
Available from: 2019-03-04 Created: 2019-03-04 Last updated: 2019-04-24Bibliographically approved

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