Abstract
The field of application of magnetic resonance spectroscopy (MRS) in biomedical research is expanding all the time and providing opportunities to investigate tissue metabolism and function. The data derived can be integrated with the information on tissue structure gained from conventional and non-conventional magnetic resonance imaging (MRI) techniques. Clinical MRS is also strongly expected to play an important role as a diagnostic tool. Essential for the future success of MRS as a clinical and research tool in biomedical sciences, both in vivo and in vitro, is the development of an accurate, biochemically relevant and physically consistent and reliable data analysis standard. Stable and well established analysis algorithms, in both the time and the frequency domain, are already available, as is free commercial software for implementing them. In this study, we propose an automatic algorithm that takes into account anatomical localisation, relative concentrations of white matter, grey matter, cerebrospinal fluid and signal abnormalities and inter-scan patient movement. The endpoint is the collection of a series of covariates that could be implemented in a multivariate analysis of covariance (MANCOVA) of the MRS data, as a tool for dealing with differences that may be ascribed to the anatomical variability of the subjects, to inaccuracies in the localisation of the voxel or slab, or to movement, rather than to the pathology under investigation. The aim was to develop an analysis procedure that can be consistently and reliably applied in the follow up of brain tumour. In this study, we demonstrate that the inclusion of such variables in the data analysis of quantitative MRS is fundamentally important (especially in view of the reduced reduced accuracy typical of MRS measures compared to other MRI techniques), reducing the occurrence of false positives.
| Original language | English |
|---|---|
| Pages (from-to) | 207-213 |
| Number of pages | 7 |
| Journal | Functional Neurology |
| Volume | 23 |
| Issue number | 4 |
| Publication status | Published - Oct 2008 |
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Keywords
- Brain tumour
- Magnetic resonance spectroscopy
- Methodology
- Post-processing
ASJC Scopus subject areas
- Clinical Neurology
- Neuroscience(all)
Cite this
Spectroscopic magnetic resonance imaging of the brain : Voxel localisation and tissue segmentation in the follow up of brain tumour. / Poloni, Guy; Bastianello, Stefano; Vultaggio, Angela; Pozzi, Sara; Maccabelli, Gloria; Germani, Giancarlo; Chiarati, Patrizia; Pichiecchio, Anna.
In: Functional Neurology, Vol. 23, No. 4, 10.2008, p. 207-213.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Spectroscopic magnetic resonance imaging of the brain
T2 - Voxel localisation and tissue segmentation in the follow up of brain tumour
AU - Poloni, Guy
AU - Bastianello, Stefano
AU - Vultaggio, Angela
AU - Pozzi, Sara
AU - Maccabelli, Gloria
AU - Germani, Giancarlo
AU - Chiarati, Patrizia
AU - Pichiecchio, Anna
PY - 2008/10
Y1 - 2008/10
N2 - The field of application of magnetic resonance spectroscopy (MRS) in biomedical research is expanding all the time and providing opportunities to investigate tissue metabolism and function. The data derived can be integrated with the information on tissue structure gained from conventional and non-conventional magnetic resonance imaging (MRI) techniques. Clinical MRS is also strongly expected to play an important role as a diagnostic tool. Essential for the future success of MRS as a clinical and research tool in biomedical sciences, both in vivo and in vitro, is the development of an accurate, biochemically relevant and physically consistent and reliable data analysis standard. Stable and well established analysis algorithms, in both the time and the frequency domain, are already available, as is free commercial software for implementing them. In this study, we propose an automatic algorithm that takes into account anatomical localisation, relative concentrations of white matter, grey matter, cerebrospinal fluid and signal abnormalities and inter-scan patient movement. The endpoint is the collection of a series of covariates that could be implemented in a multivariate analysis of covariance (MANCOVA) of the MRS data, as a tool for dealing with differences that may be ascribed to the anatomical variability of the subjects, to inaccuracies in the localisation of the voxel or slab, or to movement, rather than to the pathology under investigation. The aim was to develop an analysis procedure that can be consistently and reliably applied in the follow up of brain tumour. In this study, we demonstrate that the inclusion of such variables in the data analysis of quantitative MRS is fundamentally important (especially in view of the reduced reduced accuracy typical of MRS measures compared to other MRI techniques), reducing the occurrence of false positives.
AB - The field of application of magnetic resonance spectroscopy (MRS) in biomedical research is expanding all the time and providing opportunities to investigate tissue metabolism and function. The data derived can be integrated with the information on tissue structure gained from conventional and non-conventional magnetic resonance imaging (MRI) techniques. Clinical MRS is also strongly expected to play an important role as a diagnostic tool. Essential for the future success of MRS as a clinical and research tool in biomedical sciences, both in vivo and in vitro, is the development of an accurate, biochemically relevant and physically consistent and reliable data analysis standard. Stable and well established analysis algorithms, in both the time and the frequency domain, are already available, as is free commercial software for implementing them. In this study, we propose an automatic algorithm that takes into account anatomical localisation, relative concentrations of white matter, grey matter, cerebrospinal fluid and signal abnormalities and inter-scan patient movement. The endpoint is the collection of a series of covariates that could be implemented in a multivariate analysis of covariance (MANCOVA) of the MRS data, as a tool for dealing with differences that may be ascribed to the anatomical variability of the subjects, to inaccuracies in the localisation of the voxel or slab, or to movement, rather than to the pathology under investigation. The aim was to develop an analysis procedure that can be consistently and reliably applied in the follow up of brain tumour. In this study, we demonstrate that the inclusion of such variables in the data analysis of quantitative MRS is fundamentally important (especially in view of the reduced reduced accuracy typical of MRS measures compared to other MRI techniques), reducing the occurrence of false positives.
KW - Brain tumour
KW - Magnetic resonance spectroscopy
KW - Methodology
KW - Post-processing
UR - http://www.scopus.com/inward/record.url?scp=66549109352&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=66549109352&partnerID=8YFLogxK
M3 - Article
C2 - 19331784
AN - SCOPUS:66549109352
VL - 23
SP - 207
EP - 213
JO - Functional Neurology
JF - Functional Neurology
SN - 0393-5264
IS - 4
ER -