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RESEARCH PRODUCT
A Non-linear Diffeomorphic Framework for Prostate Multimodal Registration
Joan C. VilanovaJhimli MitraZoltan KatoXavier LladoArnau OliverRobert MartíFabrice MeriaudeauSoumya Ghosesubject
Prostate biopsyPhysics::Medical Physics[INFO.INFO-IM] Computer Science [cs]/Medical ImagingImage registration02 engineering and technology030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicine[INFO.INFO-IM]Computer Science [cs]/Medical Imaging0202 electrical engineering electronic engineering information engineeringmedicineComputer visionThin plate splineMathematicsmedicine.diagnostic_test[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industryHigh-definition videoNonlinear systemSpline (mathematics)Hausdorff distanceComputer Science::GraphicsComputer Science::Computer Vision and Pattern Recognition020201 artificial intelligence & image processingDiffeomorphismArtificial intelligencebusinessdescription
International audience; This paper presents a novel method for non-rigid registration of prostate multimodal images based on a nonlinear framework. The parametric estimation of the non-linear diffeomorphism between the 2D fixed and moving images has its basis in solving a set of non-linear equations of thin-plate splines. The regularized bending energy of the thin-plate splines along with the localization error of established correspondences is jointly minimized with the fixed and transformed image difference; where, the transformed image is represented by the set of non-linear equations defined over the moving image. The traditional thin-plate splines with established correspondences may provide good registration of the anatomical targets inside the prostate but may fail to provide improved contour registration. On the contrary, the proposed framework maintains the accuracy of registration in terms of overlap due to the non-linear thinplate spline functions while also producing smooth deformations of the anatomical structures inside the prostate as a result of established corrspondences. The registration accuracies of the proposed method are evaluated in 20 pairs of prostate midgland ultrasound and magnetic resonance images in terms of Dice similarity coefficient with an average of 0.982 ± 0.004, average 95% Hausdorff distance of 1.54 ± 0.46 mm and mean target registration and target localization errors of 1.90±1.27 mm and 0.15 ± 0.12 mm respectively.
year | journal | country | edition | language |
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2011-12-06 |