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Joint intensity‐and‐point based registration of free‐hand B‐mode ultrasound and MRI of the carotid artery
Author(s) -
Carvalho Diego D. B.,
Klein Stefan,
Akkus Zeynettin,
van Dijk Anouk C.,
Tang Hui,
Selwaness Mariana,
Schinkel Arend F. L.,
Bosch Johan G.,
van der Lugt Aad,
Niessen Wiro J.
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4870383
Subject(s) - artificial intelligence , image registration , similarity (geometry) , magnetic resonance imaging , computer vision , computer science , data set , mutual information , ultrasound , joint (building) , intensity (physics) , similarity measure , pattern recognition (psychology) , nuclear medicine , medicine , radiology , image (mathematics) , physics , architectural engineering , quantum mechanics , engineering
Purpose: To introduce a semiautomatic algorithm to perform the registration of free‐hand B‐Mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid artery.Methods: The authors’ approach combines geometrical features and intensity information. The only user interaction consists of placing three seed points in US and MRI. First, the lumen centerlines are used as landmarks for point based registration. Subsequently, in a joint optimization the distance between centerlines and the dissimilarity of the image intensities is minimized. Evaluation is performed in left and right carotids from six healthy volunteers and five patients with atherosclerosis. For the validation, the authors measure the Dice similarity coefficient (DSC) and the mean surface distance (MSD) between carotid lumen segmentations in US and MRI after registration. The effect of several design parameters on the registration accuracy is investigated by an exhaustive search on a training set of five volunteers and three patients. The optimum configuration is validated on the remaining images of one volunteer and two patients.Results: On the training set, the authors achieve an average DSC of 0.74 and a MSD of 0.66 mm on volunteer data. For the patient data, the authors obtain a DSC of 0.77 and a MSD of 0.69 mm. In the independent set composed of patient and volunteer data, the DSC is 0.69 and the MSD is 0.87 mm. The experiments with different design parameters show that nonrigid registration outperforms rigid registration, and that the combination of intensity and point information is superior to approaches that use intensity or points only.Conclusions: The proposed method achieves an accurate registration of US and MRI, and may thus enable multimodal analysis of the carotid plaque.