An automated method for registering B-mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid arteries is proposed. The registration uses geometric features, namely, lumen centerlines and lumen segmentations, which are extracted fully automatically from the images after manual annotation of three seed points in US and MRI. The registration procedure starts with alignment of the lumen centerlines using a point-based registration algorithm. The resulting rigid transformation is used to initialize a rigid and subsequent non-rigid registration procedure that jointly aligns centerlines and segmentations by minimizing a weighted sum of the Euclidean distance between centerlines and the dissimilarity between segmentations. The method was evaluated in 28 carotid arteries from eight patients and six healthy volunteers. First, the automated US lumen segmentation method was validated and optimized in a cross-validation experiment. Next, the effect of the weighting parameter of the proposed registration dissimilarity metric and the control point spacing in the non-rigid registration was evaluated. Finally, the proposed registration method was evaluated in comparison to an existing intensity-and-point-based method, a registration using only the centerlines and a registration using manual US lumen segmentations. Registration accuracy was measured in terms of the mean surface distance between manual US segmentations and the registered MRI segmentations. The average mean surface distance was 0.78 ± 0.34 mm for all subjects, 0.65 ± 0.09 mm for healthy volunteers and 0.87 ± 0.42 mm for patients. The results for the complete set were significantly better (Wilcoxon test, p <0.01) than the results for the intensity-and-point-based method and the centerline-based registration method. We conclude that the proposed method can robustly and accurately register US and MR images of the carotid artery, allowing multimodal analysis of the carotid plaque to improve plaque assessment.
|Number of pages||13|
|Journal||Ultrasound in Medicine & Biology|
|Publication status||Published - 1 Jan 2017|
- Automatic segmentation
- Magnetic resonance imaging
- Multimodal registration