Group-wise feature-based registration of CT and ultrasound images of spine

Abtin Rasoulian; Parvin Mousavi; Mehdi Hedjazi Moghari; Pezhman Foroughi; Purang Abolmaesumi

doi:10.1117/12.844598

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23 February 2010 Group-wise feature-based registration of CT and ultrasound images of spine

Abtin Rasoulian, Parvin Mousavi, Mehdi Hedjazi Moghari, Pezhman Foroughi, Purang Abolmaesumi

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Proceedings Volume 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling; 76250R (2010) https://doi.org/10.1117/12.844598
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

Registration of pre-operative CT and freehand intra-operative ultrasound of lumbar spine could aid surgeons in the spinal needle injection which is a common procedure for pain management. Patients are always in a supine position during the CT scan, and in the prone or sitting position during the intervention. This leads to a difference in the spinal curvature between the two imaging modalities, which means a single rigid registration cannot be used for all of the lumbar vertebrae. In this work, a method for group-wise registration of pre-operative CT and intra-operative freehand 2-D ultrasound images of the lumbar spine is presented. The approach utilizes a pointbased registration technique based on the unscented Kalman filter, taking as input segmented vertebrae surfaces in both CT and ultrasound data. Ultrasound images are automatically segmented using a dynamic programming approach, while the CT images are semi-automatically segmented using thresholding. Since the curvature of the spine is different between the pre-operative and the intra-operative data, the registration approach is designed to simultaneously align individual groups of points segmented from each vertebra in the two imaging modalities. A biomechanical model is used to constrain the vertebrae transformation parameters during the registration and to ensure convergence. The mean target registration error achieved for individual vertebrae on five spine phantoms generated from CT data of patients, is 2.47 mm with standard deviation of 1.14 mm.

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Abtin Rasoulian, Parvin Mousavi, Mehdi Hedjazi Moghari, Pezhman Foroughi, and Purang Abolmaesumi "Group-wise feature-based registration of CT and ultrasound images of spine", Proc. SPIE 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, 76250R (23 February 2010); https://doi.org/10.1117/12.844598

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