Quantitative MRI studies for assessment of multiple sclerosis

Pannizzo, Frank; Stallmeyer, M.J.B.; Friedman, Joshua; Jennis, R. J.; Zabriskie, J. B.; Plank, Christina; Zimmerman, Robert D.; Whalen, Joseph P.; Cahill, Patrick

doi:10.1002/mrm.1910240110

Cited by 37 publications

(8 citation statements)

References 4 publications

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“…Results obtained using this approach have shown that intra-observer variability can be as low as 6% in experienced hands, but that inter-observer variability can be as high as 14-20% for (Paty et al, 1993(Paty et al, , 1994Gonzales et al, 1994). Thresholdbased lesion methods require different degrees of operator assistance and improve variability for volume determinations compared with manual tracing (Pannizzo et al, 1992;Wicks et al, 1992;Grimaud et al, 1996). However, in case of false negatives (missed lesions), false positives or wrongly delineated lesions, these techniques either ignore them or require considerable operator assistance.…”

Section: Quantitative Techniquesmentioning

confidence: 99%

The role of magnetic resonance techniques in understanding and managing multiple sclerosis

Miller¹,

Grossman

Reingold

et al. 1998

Brain

498

342

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Magnetic resonance (MR) techniques have had a major impact in the last 10-15 years in understanding and managing multiple sclerosis. This review summarizes the current uses of MR in multiple sclerosis, based on the proceedings of a recent international workshop, under four headings: (i) technical issues; (ii) role in diagnosis; (iii) natural history studies in understanding the disease; (iv) application in clinical trials. The theory and methodology of relevant technical issues is outlined, in order to provide a framework with which to understand the potential and limitations of MR in addressing biological and clinical questions in multiple sclerosis. The principles underlying signal-to-noise and contrast-to-noise ratio are discussed, along with the techniques and clinical results for conventional and fast spin echo T2-weighted imaging, fluid-attenuated inversion recovery, detection of blood-brain barrier break down and hypointense lesions on T1-weighted images, magnetization transfer, T2 decay-curve analysis, MR spectroscopy, spinal cord imaging, diffusion imaging, and quantification of lesion load and atrophy. MRI has an extremely valuable role in confirming the clinical diagnosis of multiple sclerosis. T2-weighted brain imaging remains the standard diagnostic tool, but in some instances it is usefully complemented with gadolinium enhancement and spinal imaging. The caveat that the diagnosis of multiple sclerosis remains primarily a clinical one cannot be over-emphasized. Serial MRI studies have added much to our understanding of the natural history and pathophysiology of the disease. Blood-brain barrier breakdown is a consistent early feature of new lesion development in relapsing-remitting and secondary progressive multiple, sclerosis, and this usually correlates with active inflammation and myelin breakdown. A number of the acute MR changes are reversible, but chronic persistent abnormalities in a number of MR parameters, such as reduced N-acetyl aspartate, low magnetization transfer ratios, atrophy and T1-hypointensity, suggest the presence of demyelination and/or axonal degeneration in many chronic lesions. The presence and extent of T2-weighted MRI abnormalities at first presentation with a clinically isolated syndrome suggestive of demyelination strongly predicts the risk of developing clinically definite multiple sclerosis in the next few years. In established multiple sclerosis, however, the correlations between T2 abnormalities and disability are modest. This poor relationship partly relates to the discrepancy between lesion site and function in attempting to correlate locomotor disability with brain MRI findings. However, the correlations between brain lesion load and cognitive dysfunction in multiple sclerosis, whilst more evident, are still modest. A more important limitation is the low pathological specificity of abnormalities seen on T2-weighted images. Stronger correlations have been found between disability and new putative MR markers for demyelination and/or axonal degeneration. Serial studies...

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Section: Quantitative Techniquesmentioning

confidence: 99%

The role of magnetic resonance techniques in understanding and managing multiple sclerosis

Miller¹,

Grossman

Reingold

et al. 1998

Brain

498

342

View full text Add to dashboard Cite

show abstract

“…Nevertheless, the shape of histograms can be used for tissue discrimination. An early example is the work of Pannizzo et al ( 1992 ), who analyzed MR histograms to characterize multiple sclerosis lesions. This characterization of such lesions was improved by Mitchell et al ( 1994 ), who performed a combined analysis of two data-sets with different weighting.…”

Section: Discussionmentioning

confidence: 99%

Age-Dependent Changes in the Histogram of Apparent Diffusion Coefficients Values in Magnetic Resonance Imaging

Klose¹,

Batra²,

Nägele³

2013

Front. Aging Neurosci.

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The aim of this study was to develop a fast method for estimating whether a brain volume loss is within the normal range for the respective age of the patient. A readout-segmented diffusion-weighted echo-planar imaging sequence was performed as part of the routine examination at a 3-T scanner. Data without (b0-image) and with diffusion weighting (1000 s/mm2) from 492 patients were examined (in the age from 3 to 89 years). One hundred and seventy-three data-sets had to be excluded due to brain lesions or to pathological enlarged cerebrospinal fluid spaces. In the remaining 319 data-sets, apparent diffusion coefficients (ADCs) values were calculated for all pixels exceeding a combined threshold in the diffusion-weighted data and in the non-diffusion-weighted data. The first part of the histogram represents pixels containing mostly brain tissue. The percentage of number of pixels in this part of the ADC histograms was evaluated for all patients and was correlated with the age of the patients. In all the areas examined, a monotone change of relative pixel numbers with the age of the patients was found. The reduction of the contribution of pixels containing mostly brain tissue accelerated with age and was found to be 0.18%/year in the age of 20, 0.34%/year in the age of 50, and 0.50%/year in the age of 80. The observed decrease of the relative number of pixels from the brain tissue with increasing age corresponds to previously published results based on more time-consuming 3-D measurements. The presented technique uses a conventional clinical sequence and might be helpful in deciding whether an observed brain volume loss in a patient is within the normal range for the age of the patient.

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“…Pannizzo et al detected the intracranial boundary in axial MRI slices by tracing a horizontal line outwards from the center of the image (Pannizzo et al 1992). In each direction, the point at which the voxel intensity on the line drops below a reference threshold is considered as a point in the intracranial boundary.…”

Section: Conventional Segmentation Algorithmsmentioning

confidence: 99%

Automatic Segmentation of Multi-Spectral MR Brain Images Using a Neuro-Fuzzy Algorithm

Lee¹,

Ham²,

Kim³

et al. 2000

Neuro-Fuzzy Pattern Recognition

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This paper proposes an efficient segmentation algorithm for magnetic resonance (MR) images of the brain using a neuro-fuzzy algorithm. We apply this algorithm to various MR i mages, acquired from multiple MR scanners at different times, with varying slice thicknesses and fields of view. The proposed algorithm requires a priori knowledge concerning MR images of the brain. For example, MR images of the brain are symmetric, and white matter is contiguous along both sides of ventricular regions. For effective segmentation of MR images, a hybrid method incorporating both clustering and supervised classification algorithms is employed. In order to accept variations in feature values, we employ a bell-shaped fuzzy membership function and input these fuzzified values to a back-propagation (BP) network in the final segmentation step. Simulations with various MR images show that the proposed algorithm effectively segments MR images of the brain containing ambiguous boundaries.

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Quantitative MRI studies for assessment of multiple sclerosis

Cited by 37 publications

References 4 publications

The role of magnetic resonance techniques in understanding and managing multiple sclerosis

The role of magnetic resonance techniques in understanding and managing multiple sclerosis

Age-Dependent Changes in the Histogram of Apparent Diffusion Coefficients Values in Magnetic Resonance Imaging

Automatic Segmentation of Multi-Spectral MR Brain Images Using a Neuro-Fuzzy Algorithm

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