IEEE Trans Image Process - Bi-modal Non-rigid Registration of Brain MRI Data with Deconvolution of Joint Statistics.

Tópicos

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Resumo

A brain MRI protocol typically includes several imaging contrasts that can provide complementary information by highlighting different tissue properties. The acquired datasets often need to be co-registered or placed in a standard anatomic space before any further processing. Current registration methods particularly for multicontrast data are computationally very intensive, their resolution is lower than that of the images, and their distance metric and its optimization can be limiting. In this work a novel and effective non-rigid registration method is proposed that is based on the restoration of the joint statistics of pairs of such images. The registration is performed with the deconvolution of the joint statistics with an adaptive Wiener filter. The deconvolved statistics are forced back to the spatial domain to estimate a preliminary registration. The spatial transformation is also regularized with Gaussian spatial smoothing. The registration method has been compared with the B-Splines method implemented in 3DSlicer and with the SyN method implemented in the ANTs toolkit. The validation has been performed with a simulated Shepp-Logan phantom, a BrainWeb phantom, the real data of the NIREP database, and real multi-contrast datasets of healthy volunteers. The proposed method has shown improved comparative accuracy as well as analytical efficiency.

Resumo Limpo

brain mri protocol typic includ sever imag contrast can provid complementari inform highlight differ tissu properti acquir dataset often need coregist place standard anatom space process current registr method particular multicontrast data comput intens resolut lower imag distanc metric optim can limit work novel effect nonrigid registr method propos base restor joint statist pair imag registr perform deconvolut joint statist adapt wiener filter deconvolv statist forc back spatial domain estim preliminari registr spatial transform also regular gaussian spatial smooth registr method compar bspline method implement dslicer syn method implement ant toolkit valid perform simul shepplogan phantom brainweb phantom real data nirep databas real multicontrast dataset healthi volunt propos method shown improv compar accuraci well analyt effici

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