Literature DB >> 16806979

MRI time series modeling of MS lesion development.

Dominik S Meier1, Charles R G Guttmann.   

Abstract

A mathematical model was applied to new lesion formation in multiple sclerosis, as apparent on frequent T2-weighted MRI. The pathophysiologically motivated two-process model comprises two opposing nonlinear self-limiting processes, intended to represent degenerative and reparatory processes, respectively, investigating T2 activity from a dynamic/temporal rather than a spatial/static perspective. Parametric maps were obtained from the model to characterize the MRI dynamics of lesion development, answering the questions of how long new T2 lesion activity persists, how much residual damage/hyperintensity remains and how the T2 dynamics compare to those of contrast-enhancing MRI indicating active inflammation. 997 MRI examinations were analyzed, acquired weekly to monthly from 45 patients over a 1-year period. The model was applied to all pixels within 332 new lesions, capturing the time profiles with excellent fidelity (r = 0.89 +/- 0.03 average correlation between model and image data). From this modeling perspective, the observed dynamics in new T2 lesions are in agreement with two opposing processes of longitudinal intensity change, such as inflammation and degeneration versus resorbtion and repair. On average, about one third of a new lesion consisted of transient signal change with little or no residual hyperintensity and activity of 10 weeks or less. Global lesion burden as MRI surrogate of disease activity may therefore be confounded by large amounts of transient hyperintensity. T2 activity also persisted significantly beyond the period of contrast enhancement, thereby defining MRI sensitivity toward a subacute phase of lesion development beyond blood-brain barrier patency. Concentric patterns of dynamic properties within a lesion were observed, consistent with concentric histological appearance of resulting MS plaques.

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Year:  2006        PMID: 16806979     DOI: 10.1016/j.neuroimage.2006.04.181

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  17 in total

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2.  Experimental design and sample size considerations in longitudinal magnetic resonance imaging-based biomarker detection for multiple sclerosis.

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4.  Demyelination patterns in a mathematical model of multiple sclerosis.

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5.  Seasonal prevalence of MS disease activity.

Authors:  D S Meier; K E Balashov; B Healy; H L Weiner; C R G Guttmann
Journal:  Neurology       Date:  2010-08-31       Impact factor: 9.910

6.  MR imaging intensity modeling of damage and repair in multiple sclerosis: relationship of short-term lesion recovery to progression and disability.

Authors:  D S Meier; H L Weiner; C R G Guttmann
Journal:  AJNR Am J Neuroradiol       Date:  2007 Nov-Dec       Impact factor: 3.825

7.  Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine.

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Review 8.  Time-series modeling of multiple sclerosis disease activity: a promising window on disease progression and repair potential?

Authors:  Dominik S Meier; Howard L Weiner; Charles R G Guttmann
Journal:  Neurotherapeutics       Date:  2007-07       Impact factor: 7.620

9.  An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions.

Authors:  J D Dworkin; K A Linn; I Oguz; G M Fleishman; R Bakshi; G Nair; P A Calabresi; R G Henry; J Oh; N Papinutto; D Pelletier; W Rooney; W Stern; N L Sicotte; D S Reich; R T Shinohara
Journal:  AJNR Am J Neuroradiol       Date:  2018-02-22       Impact factor: 3.825

10.  Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation.

Authors:  Ipek Oguz; Aaron Carass; Dzung L Pham; Snehashis Roy; Nagesh Subbana; Peter A Calabresi; Paul A Yushkevich; Russell T Shinohara; Jerry L Prince
Journal:  Brainlesion       Date:  2018-02-17
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