Atherosclerosis at the carotid bifurcation can result in cerebral emboli, which in turn can block the blood supply to the brain causing ischemic strokes. Noninvasive imaging tools that better characterize arterial wall, and atherosclerotic plaque structure and composition may help to determine the factors which lead to the development of unstable lesions, and identify patients at risk of plaque disruption and stroke. Carotid magnetic resonance (MR)imaging allows for the characterization of carotid vessel wall and plaque composition, the characterization of normal and pathological arterial wall, the quantification of plaque size, and the detection of plaque integrity. On the other hand, various ultrasound(US) measurements have also been used to quantify atherosclerosis, carotid stenosis, intima-media thickness, total plaque volume, total plaque area, and vessel wall volume. Combining the complementary information provided by 3D MR and US carotid images may lead to a better understanding of the underlying compositional and textural factors that define plaque and wall vulnerability, which may lead to better and more effective stroke prevention strategies and patient management. Combining these images requires nonrigid registration to correct the nonlinear misalignments caused by relative twisting and bending in the neck due to different head positions during the two image acquisition sessions. The high degree of freedom and large number of parameters associated with existing nonrigid image registration methods causes several problems including unnatural plaque morphology alteration, high computational complexity, and low reliability. Thus, a “twisting and bending” model was used with only six parameters to model the normal movement of the neck for nonrigid registration. The registration technique was evaluated using 3D US and MR carotid images at two field strengths, 1.5 and , of the same subject acquired on the same day. The mean registration error between the segmented carotid artery wall boundaries in the target USimage and the registered MRimages was calculated using a distance-based error metric after applying a “twisting and bending” model based nonrigid registration algorithm. An average registration error of was obtained for MR and for MR, when registered with 3D USimages using the nonrigid registration technique presented in this paper. Visual inspection of segmented vessel surfaces also showed a substantial improvement of alignment with this nonrigid registration technique compared to rigid registration.
The authors wish to thank Trevor Wade and Adam Krasinski for carotid artery segmentations. This work has been supported by the Canadian Institutes for Health Research (CIHR), The Richard Ivey Foundation, and Ontario Graduate Scholarship program. A.F. holds a Canada Research Chair and acknowledges The Canada Research Chair Program.
II. MATERIALS AND METHODS
II.A. Nonrigid registration
II.A.1. Rigid transformation
II.A.2. Nonrigid transformation: Twisting and bending model
II.A.3. Registration process
II.A.5. Registration initialization
II.B. Evaluation of accuracy
II.B.1. Image acquisition
II.B.2. Registration error metric
II.B.3. Nonrigid registration accuracy
II.B.4. Comparison of rigid and nonrigid registration results
II.B.5. Registration error as a function of the distance from carotid bifurcation
II.B.6. Comparison of US–MR registration errors at two MR field strengths
III.A. Nonrigid registration accuracy
III.B. Comparison of rigid and nonrigid registration results
III.C. Registration error as a function of the distance from carotid bifurcation
III.D. Comparison of US–MR registration errors at two MR field strengths
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