Magnetization transfer imaging of multiple sclerosis

1997 ◽  
Vol 18 (6) ◽  
pp. 359-365 ◽  
Author(s):  
G. B. Pike
Keyword(s):  
Multiple Sclerosis ◽  
Magnetization Transfer ◽  
Magnetization Transfer Imaging

Diffusion Tensor and Magnetization Transfer Imaging Correlates of Motor Dysfunction in the Spinal Cord in Multiple Sclerosis (S21.002)

Neurology ◽  
2012 ◽  
Vol 78 (Meeting Abstracts 1) ◽  
pp. S21.002-S21.002 ◽  
Author(s):  
J. Oh ◽  
K. Zackowski ◽  
M. Chen ◽  
S. Newsome ◽  
S. Smith ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Spinal Cord ◽  
Diffusion Tensor ◽  
Magnetization Transfer ◽  
Motor Dysfunction ◽  
Magnetization Transfer Imaging

Magnetization transfer imaging in multiple sclerosis treated with alemtuzumab

2012 ◽  
Vol 19 (2) ◽  
pp. 241-244 ◽  
Author(s):  
T. Button ◽  
D. Altmann ◽  
D. Tozer ◽  
C. Dalton ◽  
K. Hunter ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Magnetization Transfer ◽  
Magnetization Transfer Imaging

Brain MRI correlates of magnetization transfer imaging metrics in patients with multiple sclerosis

1999 ◽  
Vol 166 (1) ◽  
pp. 58-63 ◽  
Author(s):  
Marco Rovaris ◽  
Marco Bozzali ◽  
Mariaemma Rodegher ◽  
Carla Tortorella ◽  
Giancarlo Comi ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Magnetization Transfer ◽  
Brain Mri ◽  
Magnetization Transfer Imaging

A Magnetization Transfer Imaging Study of Normal-Appearing White Matter in Multiple Sclerosis

Neurology ◽  
1995 ◽  
Vol 45 (3) ◽  
pp. 478-482 ◽  
Author(s):  
M. Filippi ◽  
A. Campi ◽  
V. Dousset ◽  
C. Baratti ◽  
V. Martinelli ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Magnetization Transfer ◽  
Imaging Study ◽  
Magnetization Transfer Imaging ◽  
Normal Appearing White Matter

scholarly journals Sensorimotor dysfunction in multiple sclerosis and column-specific magnetization transfer-imaging abnormalities in the spinal cord

Brain ◽  
2009 ◽  
Vol 132 (5) ◽  
pp. 1200-1209 ◽  
Author(s):  
K. M. Zackowski ◽  
S. A. Smith ◽  
D. S. Reich ◽  
E. Gordon-Lipkin ◽  
B. A. Chodkowski ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Spinal Cord ◽  
Magnetization Transfer ◽  
Magnetization Transfer Imaging ◽  
Specific Magnetization

scholarly journals A Novel Classification Method using Effective Neural Network and Quantitative Magnetization Transfer Imaging of Brain White Matter in Relapsing Remitting Multiple Sclerosis

2018 ◽  
Vol 8 (4Dec) ◽  
Author(s):  
M Fooladi ◽  
H Sharini ◽  
S Masjoodi ◽  
E Khodamoradi
Keyword(s):  
Neural Network ◽  
Multiple Sclerosis ◽  
Relaxation Time ◽  
Healthy Subjects ◽  
Magnetization Transfer ◽  
Magnetization Transfer Imaging ◽  
Longitudinal Relaxation Time ◽  
Brain White Matter ◽  
Relapsing Remitting ◽  
Relapsing Remitting Multiple Sclerosis

Background: Quantitative Magnetization Transfer Imaging (QMTI) is often used to quantify the myelin content in multiple sclerosis (MS) lesions and normal appearing brain tissues. Also, automated classifiers such as artificial neural networks (ANNs) can significantly improve the identification and classification processes of MS clinical datasets.Objective: We classified patients with relapsing-remitting multiple sclerosis (RRMS) from healthy subjects using QMTI and T1 longitudinal relaxation time data of brain white matter and the performance of three ANN-based classifiers have been investigated.   Materials and Methods: Conventional magnetic resonance imaging (MRI) and quantitative magnetization transfer scans were obtained from RRMS patients (n=30) and age-matched healthy subjects (n=30). After image pre-processing and brain tissue segmentation, QMTI parameters including magnetization transfer ratio (MTR), magnetization transfer rate (Ksat), T1 relaxation time under MT saturation pulse (T1sat) and T1 longitudinal relaxation time were calculated as parametric maps. Three ANN algorithms, including multilayer perceptron (MLP), radial basis function (RBF) and ensemble neural network based on Akaike information criterion (ENN-AIC) input features were extracted in the form of QMTI and T1 mean values. The ANNs quantitative performance is measured by the standard evaluation of confusion matrix criteria.Results: The results indicate that ENN-AIC-based classification method has achieved 90% accuracy, 92% sensitivity and 86% precision compared to other ANN classification models such as RBF and MLP. NPV, FPR and FDR values of the proposed ENN-AIC model were found to be 0.933, 0.125 and 0.133, respectively. A graphical representation of how to track actual data by the predictive values derived from each of the three algorithms, was also presented. It has been demonstrated that ENN-AIC as an effective neural network improves the quality of classification results compared to MLP and RBF.Conclusion: The efficiency and robustness of ENN classifier will greatly enhance with the use of AIC-based combination weights assignment. In addition, this research  provides a new direction to classify a large amount of quantitative MRI data that can help the physician in a correct MS diagnosis.

Contribution of cervical cord MRI and brain magnetization transfer imaging to the assessment of individual patients with multiple sclerosis: a preliminary study

2002 ◽  
Vol 8 (1_suppl) ◽  
pp. 52-58 ◽  
Author(s):  
M. Rovaris ◽  
M. Holtmannspötter ◽  
MA Rocca ◽  
G. Iannucci ◽  
M. Codella ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Lupus Erythematosus ◽  
Magnetization Transfer ◽  
False Negative ◽  
Brain Magnetic Resonance Imaging ◽  
Cervical Cord ◽  
Magnetization Transfer Ratio ◽  
Magnetization Transfer Imaging ◽  
Mri Scans ◽  
The Brain

This study was performed to assess how established diagnostic criteria for brain magnetic resonance imaging (MRI) interpretation in cases of suspected multiple sclerosis (MS) (Barkhofs criteria) would perform in the distinction of MS from other diseases and whether other MR techniques (cervical cord imaging and brain magnetization transfer imaging [MTI]), might help in the diagnostic work-up of these patients. We retrospectively identified 64 MS and 59 non-MS patients. The latter group included patients with systemic immune-mediated disorders (SID; n=44) and migraine (n=15). All patients had undergone MRI scans of the brain (dual echo and MTI) and of the cervical cord (fast short-tau inversion recovery). The number and location of brain T2-hyperintense lesions and the number and size of cervical cord lesions were assessed. Brain images were also postprocessed to quantify the total lesion volumes (TLV) and to create histograms of magnetization transfer ratio (MTR) values from the whole of the brain tissue. Barkhofs criteria correctly classified 108/123 patients, thus showing an accuracy of 87.8%. "False negative" MS patients were 13, while 2 patients with systemic lupus erythematosus (SLE) were considered as "false positives". Using brain T2 TLV, nine of the"false negative" patients were correctly classified. Correct classification of 10 MS patients and both the SLE patients was possible based upon the presence or absence of one cervical cord lesion. Two MS patients with negative Barkhofs criteria and no cervical cord lesions were correctly classified based on their brain MTR values. Overall, only one MS patient could not be correctly classified by any of the assessed MR quantities. These preliminary data support a more extensive use of cervical cord MRI and brain MTI to differentiate between MS and other disorders in case of inconclusive findings on T2-weighted MRI scans of the brain. Multiple Sclerosis (2002) 8, 52-58

scholarly journals Quantitative magnetization transfer imaging in postmortem multiple sclerosis brain

2007 ◽  
Vol 26 (1) ◽  
pp. 41-51 ◽  
Author(s):  
Klaus Schmierer ◽  
Daniel J. Tozer ◽  
Francesco Scaravilli ◽  
Daniel R. Altmann ◽  
Gareth J. Barker ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Magnetization Transfer ◽  
Magnetization Transfer Imaging
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