Analysis of diffusion tensor measurements of the human cervical spinal cord based on semiautomatic segmentation of the white and gray matter

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F18%3A00106924" target="_blank" >RIV/00216224:14110/18:00106924 - isvavai.cz</a>

Alternative codes found

RIV/65269705:_____/18:00068912

Result on the web

<a href="http://dx.doi.org/10.1002/jmri.26166" target="_blank" >http://dx.doi.org/10.1002/jmri.26166</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/jmri.26166" target="_blank" >10.1002/jmri.26166</a>

Result language

angličtina

Original language name

Analysis of diffusion tensor measurements of the human cervical spinal cord based on semiautomatic segmentation of the white and gray matter

Original language description

BackgroundPurposeSegmentation of the gray and white matter (GM, WM) of the human spinal cord in MRI images as well as the analysis of spinal cord diffusivity are challenging. When appropriately segmented, diffusion tensor imaging (DTI) of the spinal cord might be beneficial in the diagnosis and prognosis of several diseases. To evaluate the applicability of a semiautomatic algorithm provided by ITK-SNAP in classification mode (CLASS) for segmenting cervical spinal cord GM, WM in MRI images and analyzing DTI parameters. Study TypeSubjectsProspective. Twenty healthy volunteers. SequencesAssessment1.5T, turbo spin echo, fast field echo, single-shot echo planar imaging. Three raters segmented the tissues by manual, CLASS, and atlas-based methods (Spinal Cord Toolbox, SCT) on T-2-weighted and DTI images. Masks were quantified by similarity and distance metrics, then analyzed for repeatability and mutual comparability. Masks created over T-2 images were registered into diffusion space and fractional anisotropy (FA) values were statistically evaluated for dependency on method, rater, or tissue. Statistical TestsResultst-test, analysis of variance (ANOVA), coefficient of variation, Dice coefficient, Hausdorff distance. CLASS segmentation reached better agreement with manual segmentation than did SCT (P&lt;0.001). Intra- and interobserver repeatability of SCT was better for GM and WM (both P&lt;0.001) but comparable with CLASS in entire spinal cord segmentation (P=0.17 and P=0.07, respectively). While FA values of whole spinal cord were not influenced by choice of segmentation method, both semiautomatic methods yielded lower FA values (P&lt;0.005) for GM than did the manual technique (mean differences 0.02 and 0.04 for SCT and CLASS, respectively). Repeatability of FA values for all methods was sufficient, with mostly less than 2% variance.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30224 - Radiology, nuclear medicine and medical imaging

Project

<a href="/en/project/NV15-32133A" target="_blank" >NV15-32133A: Predicting conversion of clinically isolated syndrome to multiple sclerosis using advanced magnetic resonance imaging techniques</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Magnetic Resonance Imaging

ISSN

1053-1807

e-ISSN

1522-2586

Volume of the periodical

48

Issue of the periodical within the volume

5

Country of publishing house

US - UNITED STATES

Number of pages

11

Pages from-to

1217-1227

UT code for WoS article

000448081300006

EID of the result in the Scopus database

2-s2.0-85055211023