Graphical Analysis of MR Feature Space for Measurement of CSF, Gray-Matter, and White-Matter Volumes

Bonar, David; Schaper, Kirt; Anderson, Jon R.; Rottenberg, David A.; Strother, Stephen C.

doi:10.1097/00004728-199305000-00024

Cited by 40 publications

(27 citation statements)

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“…These techniques are strongly dependent on sequences, tuning, and MR scanner performance characteristics with subsequent variable positions of the clusters in a feature space defined by signal intensities (14). The consistency of the Table 2 Phantom Composition Simulated GM Simulated WM Agar (g/liter) 13 10…”

Section: Discussionmentioning

confidence: 99%

Unsupervised, automated segmentation of the normal brain using a multispectral relaxometric magnetic resonance approach

Alfano

Brunetti

Covelli

et al. 1997

Magnetic Resonance in Med

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The purpose of this study was the development and testing of a method for unsupervised, automated brain segmentation. Two spin-echo sequences were used to obtain relaxation rates and proton-density maps from 1.5 T MR studies, with two axial data sets including the entire brain. Fifty normal subjects (age range, 16 to 76 years) were studied. A Three-dimensional (3D) spectrum of the tissue voxels was used for automatic segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) and for calculation of their volumes. Accuracy and reproducibility were tested with a three-compartment phantom simulating GM, WM, and CSF. In the normal subjects, a significant decrease of GM fractional volume and increased CSF volume with age were observed (P < 0.0001), with no significant changes in WM. This multispectral segmentation method permits reproducible, operator-independent volumetric measurements.

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Section: Discussionmentioning

confidence: 99%

Unsupervised, automated segmentation of the normal brain using a multispectral relaxometric magnetic resonance approach

Alfano

Brunetti

Covelli

et al. 1997

Magnetic Resonance in Med

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“…There are two general approaches to brain tissue segmentation, either each voxel is assigned to one specific tissue class (114)(115)(116)(117)(118)(119), or each voxel is assigned a volume fraction of more than one tissue class (120)(121)(122)(123). Allowing more than one tissue class in each voxel is sometimes referred to as fuzzy segmentation, and the resulting voxels with tissue fractions are termed mixels.…”

Section: Brain Tissue Segmentationmentioning

confidence: 99%

“…Most brain tissue segmentation methods are based on conventional contrastweighted MRI (115,116,(118)(119)(120)(121)(122). Although these images provide high anatomical detail, segmentation is complicated by the inhomogeneities and the arbitrary greyscaling of the contrast images.…”

Section: Brain Tissue Segmentationmentioning

confidence: 99%

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Quantitative Magnetic Resonance Imaging of the Brain : Applications for Tissue Segmentation and Multiple Sclerosis

West¹

2014

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Magnetic resonance imaging (MRI) is a sensitive technique for assessing white matter (WM) lesions in multiple sclerosis (MS), but there is a low correlation between MRI findings and clinical disability. Because of this, other pathological changes are of interest, including changes in normal appearing white matter (NAWM) and diffusely abnormal white matter (DAWM). Even so, the mechanisms leading to permanent disability in MS remain unclear. In contrast to conventional MRI, quantitative MRI (qMRI) is aimed at the direct measurement of the physical tissue properties, such as the relaxation times, T1 and T2, as well as the proton density (PD). QMRI is promising for characterising and quantifying changes in MS and for brain tissue segmentation. The present work describes a novel method of qMRI for the human brain (QMAP), and a segmentation method based on this. The developed methods were validated in control subjects and MR phantoms. Furthermore, an application in diseased human brain was demonstrated in MS patients. In all, 50 healthy controls and 35 MS patients were scanned with qMRI in a total of 225 acquisitions. One major finding of this work was that qMRI was able to detect and quantify changes in the MS disease that were not visible using conventional MRI. In particular, it was found that DAWM appears to constitute an intermediate between focal white matter (WM) lesions and NAWM. These changes may be caused by pathological processes that are not entirely attributable to Wallerian degeneration. This study showed that the QMAP method had high accuracy and relatively high precision, within a clinically acceptable time. This work also demonstrated that qMRI could be used for brain tissue segmentation and volume estimation of the whole brain, using pre-defined tissue characteristics. The results showed that brain tissue segmentation had high repeatability, which was somewhat lower when different geometries were acquired or different field strengths used. In particular, small differences were found between 1.5 T and 3.0 T in deep brain structures, the cerebellum and the brain stem. This work leads the way for early clinical applications of qMRI, and the challenge for the years to come is to understand the connection between qMRI properties of the brain and underlying biology.Bildtagning med magnetresonanstomografi (MRT) är en teknik som kan användas för att upptäcka lesioner i vit substans hos patienter med multipel skleros (MS), men sambandet mellan lesioner och klinisk funktionsnedsättning är svagt. På grund av detta har intresset för andra patologiska processer i hjärnan ökat. Exempel är förändringar i vit substans som ser normal ut vid MRT (NAWM) och även så kallad diffus vit vävnad (DAWM). Det är emellertid fortfarande oklart vilka mekanismer i MS som leder till klinisk funktionsnedsättning. Med kvantitativ MRT (qMRT) kan fysiologiska egenskaper i vävnaden, som till exempel relaxationstiderna (T1 och T2) samt protontäthet (PD), mätas. QMRT kan användas för att mäta förändringar i hjärnan hos MS patienter och des...

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“…Clarke et al (16) reviewed numerous classification schemes, including multispectral analysis, for segmenting brain tissues. Many classification methods, including multispectral analysis, use some form of fuzzy classification (5,(8)(9)(10)(11)(12)(13)(14)(15). Fuzzy classification, in contrast to crisp or hard classification, subdivides the contents of a voxel's volume into different tissue classes.…”

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confidence: 99%

Quantification of white matter and gray matter volumes from three‐dimensional magnetic resonance volume studies using fuzzy classifiers

Herndon

Lancaster

Giedd

et al. 1998

Magnetic Resonance Imaging

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We accurately measured white matter (WM) and gray matter (GM) from three-dimensional (3D) volume studies, using a fuzzy classification technique. The new segmentation method is a modification of a recently published method developed for T1 parametric images. 3D MR images were transformed into pseudo forms of T1 parametric images and segmented into WM and GM voxel fraction images with a set of standardized fuzzy classifiers. This segmentation method was validated with synthesized 3D MR images as phantoms. These phantoms were developed from cryosectioned human brain images located in the superior, middle, and inferior regions of the cerebrum. Phantom volume measurements revealed that, generally, the difference between measured and actual volumes was less than 3% for 1.5-mm simulated brain slices. The average cerebral GM/WM ratio calculated from 3D MR studies in four subjects was 1.77, which compared favorably with the estimate of 1.67 derived from anatomical data. Results indicate that this is an accurate and rapid method for quantifying WM and GM from Tl-weighted 3D volume studies.

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Graphical Analysis of MR Feature Space for Measurement of CSF, Gray-Matter, and White-Matter Volumes

Cited by 40 publications

References 0 publications

Unsupervised, automated segmentation of the normal brain using a multispectral relaxometric magnetic resonance approach

Unsupervised, automated segmentation of the normal brain using a multispectral relaxometric magnetic resonance approach

Quantitative Magnetic Resonance Imaging of the Brain : Applications for Tissue Segmentation and Multiple Sclerosis

Quantification of white matter and gray matter volumes from three‐dimensional magnetic resonance volume studies using fuzzy classifiers

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