J Med Syst - Unbiased group-wise image registration: applications in brain fiber tract atlas construction and functional connectivity analysis.

Tópicos

{ imag(1057) registr(996) error(939) }
{ group(2977) signific(1463) compar(1072) }
{ network(2748) neural(1063) input(814) }
{ studi(1410) differ(1259) use(1210) }
{ perform(1367) use(1326) method(1137) }
{ structur(1116) can(940) graph(676) }
{ measur(2081) correl(1212) valu(896) }
{ imag(2830) propos(1344) filter(1198) }
{ framework(1458) process(801) describ(734) }
{ search(2224) databas(1162) retriev(909) }
{ import(1318) role(1303) understand(862) }
{ sampl(1606) size(1419) use(1276) }
{ model(3404) distribut(989) bayesian(671) }
{ method(1219) similar(1157) match(930) }
{ imag(2675) segment(2577) method(1081) }
{ error(1145) method(1030) estim(1020) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
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{ time(1939) patient(1703) rate(768) }
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{ analysi(2126) use(1163) compon(1037) }
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{ activ(1452) weight(1219) physic(1104) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We propose an unbiased implicit-reference group-wise (IRG) image registration method and demonstrate its applications in the construction of a brain white matter fiber tract atlas and the analysis of resting-state functional MRI (fMRI) connectivity. Most image registration techniques pair-wise align images to a selected reference image and group analyses are performed in the reference space, which may produce bias. The proposed method jointly estimates transformations, with an elastic deformation model, registering all images to an implicit reference corresponding to the group average. The unbiased registration is applied to build a fiber tract atlas by registering a group of diffusion tensor images. Compared to reference-based registration, the IRG registration improves the fiber track overlap within the group. After applying the method in the fMRI connectivity analysis, results suggest a general improvement in functional connectivity maps at a group level in terms of larger cluster size and higher average t-scores.

Resumo Limpo

propos unbias implicitrefer groupwis irg imag registr method demonstr applic construct brain white matter fiber tract atlas analysi restingst function mri fmri connect imag registr techniqu pairwis align imag select refer imag group analys perform refer space may produc bias propos method joint estim transform elast deform model regist imag implicit refer correspond group averag unbias registr appli build fiber tract atlas regist group diffus tensor imag compar referencebas registr irg registr improv fiber track overlap within group appli method fmri connect analysi result suggest general improv function connect map group level term larger cluster size higher averag tscore

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