Joint intensity-and-point based registration of free-hand B-mode ultrasound and MRI of the carotid artery.
OBJECTIVE
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.
Department of Radiology and Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam 3015 CE, The Netherlands.
Diego D B
DD
Carvalho
Department of Radiology and Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam 3015 CE, The Netherlands.
Stefan
S
Klein
Biomedical Engineering, Erasmus MC, Rotterdam 3015 CE, The Netherlands.
Zeynettin
Z
Akkus
Department of Radiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands and Department of Neurology, Erasmus MC, Rotterdam 3015 CE, The Netherlands.
Anouk C
AC
van Dijk
Department of Radiology and Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam 3015 CE, The Netherlands and Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft 2600 AA, The Netherlands.
Hui
H
Tang
Department of Radiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands.
Mariana
M
Selwaness
Department of Internal Medicine, Division of Pharmacology, Vascular and Metabolic Diseases, Erasmus MC, Rotterdam 3015 CE, The Netherlands and Department of Cardiology, Thoraxcenter, Erasmus MC, Rotterdam 3015 CE, The Netherlands.
Arend F L
AF
Schinkel
Biomedical Engineering, Erasmus MC, Rotterdam 3015 CE, The Netherlands.
Johan G
JG
Bosch
Department of Radiology, Erasmus MC, Rotterdam 3015 CE, The Netherlands.
Aad
A
van der Lugt
Department of Radiology and Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam 3015 CE, The Netherlands and Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft 2600 AA, The Netherlands.
Wiro J
WJ
Niessen
D000465:Algorithms; D050197:Atherosclerosis / Q000000981:diagnostic imaging / Q000473:pathology; D002339:Carotid Arteries / Q000033:anatomy & histology / Q000000981:diagnostic imaging* / Q000473:pathology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D064847:Multimodal Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D014463:Ultrasonography / Q000379:methods*