Automatic quantification of multiple sclerosis lesion volume using stereotaxic space

Zijdenbos, Alex P.; Evans, Alan C.; Riahi, F.; Sled, John G.; Chui, Joe; Kollokian, Vasken

doi:10.1007/bfb0046984

Cited by 85 publications

(60 citation statements)

References 16 publications

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“…To carry out this examination, an automated segmentation system developed at the Montreal Neurological Institute, Montreal, Quebec, was used. [21][22][23][24][25] Because of our earlier brain ventricular data and the smaller total brain and temporal lobe volumes with greater loss of gray matter characteristic of adults with schizophrenia, we hypothesized that there would be commensurate differential changes in other brain tissue, including cortical gray matter, with patients with COS showing a greater and more regionally selective decline than seen for healthy controls.…”

Section: Arch Genmentioning

confidence: 99%

See 1 more Smart Citation

Progressive Cortical Change During Adolescence in Childhood-Onset Schizophrenia

Rapoport

Giedd²,

Blumenthal³

et al. 1999

Arch Gen Psychiatry

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354

187

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Section: Arch Genmentioning

confidence: 99%

“…[21][22][23][24][25]31 First, the images are corrected for regional intensity nonuniformities resulting from magnetic field inhomogeneities inherent in the image acquisition process. Next, the images are transformed to a standardized stereotactic (Talairach) space using a 9-parameter linear process.…”

Section: Commentmentioning

confidence: 99%

Progressive Cortical Change During Adolescence in Childhood-Onset Schizophrenia

Rapoport

Giedd²,

Blumenthal³

et al. 1999

Arch Gen Psychiatry

Self Cite

354

187

View full text Add to dashboard Cite

“…We calculated brain volume using the brain extraction tool [27]. The registered and corrected volumes were classified into white matter, gray matter, cerebrospinal fluid, and background using an advanced neural-net classifier [28]. The classified white matter regions were used in the WMH mask extraction step.…”

Section: T1-weighed Image Pre-processingmentioning

confidence: 99%

Diffusion Tensor Imaging Changes Correlate with Clinical Progression in Vascular Mild Cognitive Impairment and Vascular Dementia of Subcortical Type

Ryoo

Yoon

et al. 2013

Dement Neurocognitive Disord

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Background: Cerebral small vessel disease (SVD) induces vascular cognitive impairment (VCI) such as subcortical vascular dementia (SVaD) and subcortical vascular mild cognitive impairment (svMCI). We compared MRI parameters between SVaD and svMCI and determined which MRI parameters best correlated with cognitive function and disability on cross-sectional and longitudinal analyses within them. Methods: Twenty-four patients with SVaD and twelve with svMCI were recruited. They underwent multimodal MRIs including fluid-attenuated inversion recovery lesion load, lacunar infarct number, and fractional anisotropy (FA) and mean diffusivity (MD) from diffusion tensor imaging (DTI), neuropsychological testing, Sum of Boxes of Clinical Dementia Rating Scale (CDR-SB), Barthel Index, and the Korean version of a Geriatric Depression Scale (GDS-K). Seventeen patients were retested after 20 months for a brain MRI and clinical evaluation. Results: There were significant differences in average MD and peak height of MD histograms within normal-appearing brain tissue (NABT) between SVaD and svMCI patients. In the cross-sectional analysis, average MD within NABT significantly correlated with the composite neuropsychology score (r = -0.80, p < 0.001), the composite executive function score (r = -0.67, p < 0.001), and the CDR-SB (r = 0.54, p = 0.001), and the Barthel Index correlated with peak heights of the MD histograms (r = 0.37, p = 0.03) in NABT. Changes of CDR-SB was associated with changes of average MD within WMH (r = 0.57, p = 0.02), and changes of GDS-K was associated with changes of WMH volume (r = 0.51, p = 0.04) on a longitudinal scale. Conclusions: DTI parameters in NABT correlated with cognitive impairment and disability in VCI associated with SVD. Clinical progression of SVD was associated with some increment of WML volume and ultrastructural changes in WMH.

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“…We have combined preprocessing steps (image intensity non-uniformity correction [26], tube masking), linear registration (ANIMAL in linear mode [27]) and resampling into stereotaxic space, cortical surface extraction (MSD [28,29]), tissue classification (INSECT [30]), automatic sulcal extraction (SEAL [31]) and nonlinear registration (ANIMAL in nonlinear mode [2]) into a processing pipeline.…”

Section: Processing Pipelinementioning

confidence: 99%

Non-linear cerebral registration with sulcal constraints

Collins

Goualher

Evans

1998

Medical Image Computing and Computer-Assisted Intervention — MICCAI’98

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Abstract. In earlier work [1], we demonstrated that cortical registration could be improved on simulated data by using blurred, geometric, imagebased features (Lvv) or explicitly extracted and blurred sulcal traces on simulated data. Here, the technique is modified to incorporate sulcal ribbons in conjunction with a chamfer distance objective function to improve registration in real MRI data as well. Experiments with 10 simulated data sets demonstrate a 56% reduction in residual sulcal registration error (from 3.4 to 1.5mm, on average) when compared to automatic linear registration and an 28% improvement over our previously published non-linear technique (from 2.1 to 1.5ram). The simulation results are confirmed by experiments with real MRI data from young normal subjects, where sulcal misregistration is reduced by 20% (from 5.0mm to 4.0mm) and 11% (from 4.5 to 4.0mm) over the standard linear and nonlinear registration methods, respectively.

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Automatic quantification of multiple sclerosis lesion volume using stereotaxic space

Cited by 85 publications

References 16 publications

Progressive Cortical Change During Adolescence in Childhood-Onset Schizophrenia

Progressive Cortical Change During Adolescence in Childhood-Onset Schizophrenia

Diffusion Tensor Imaging Changes Correlate with Clinical Progression in Vascular Mild Cognitive Impairment and Vascular Dementia of Subcortical Type

Non-linear cerebral registration with sulcal constraints

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