TY - GEN
T1 - Biomechanically constrained groupwise US to CT registration of the lumbar spine
AU - Gill, Sean
AU - Mousavi, Parvin
AU - Fichtinger, Gabor
AU - Chen, Elvis
AU - Boisvert, Jonathan
AU - Pichora, David
AU - Abolmaesumi, Purang
PY - 2009
Y1 - 2009
N2 - Registration of intraoperative ultrasound (US) with preoperative computed tomography (CT) data for interventional guidance is a subject of immense interest, particularly for percutaneous spinal injections. We propose a biomechanically constrained group-wise registration of US to CT images of the lumbar spine. Each vertebra in CT is treated as a sub-volume and transformed individually. The sub-volumes are then reconstructed into a single volume. The algorithm simulates an US image from the CT data at each iteration of the registration. This simulated US image is used to calculate an intensity based similarity metric with the real US image. A biomechanical model is used to constrain the displacement of the vertebrae relative to one another. Covariance Matrix Adaption - Evolution Strategy (CMA-ES) is utilized as the optimization strategy. Validation is performed on CT and US images from a phantom designed to preserve realistic curvatures of the spine. The technique is able to register initial misalignments of up to 20mm with a success rate of 82%, and those of up to 10mm with a success rate of 98.6%.
AB - Registration of intraoperative ultrasound (US) with preoperative computed tomography (CT) data for interventional guidance is a subject of immense interest, particularly for percutaneous spinal injections. We propose a biomechanically constrained group-wise registration of US to CT images of the lumbar spine. Each vertebra in CT is treated as a sub-volume and transformed individually. The sub-volumes are then reconstructed into a single volume. The algorithm simulates an US image from the CT data at each iteration of the registration. This simulated US image is used to calculate an intensity based similarity metric with the real US image. A biomechanical model is used to constrain the displacement of the vertebrae relative to one another. Covariance Matrix Adaption - Evolution Strategy (CMA-ES) is utilized as the optimization strategy. Validation is performed on CT and US images from a phantom designed to preserve realistic curvatures of the spine. The technique is able to register initial misalignments of up to 20mm with a success rate of 82%, and those of up to 10mm with a success rate of 98.6%.
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U2 - 10.1007/978-3-642-04268-3_99
DO - 10.1007/978-3-642-04268-3_99
M3 - Conference contribution
SN - 3642042678
SN - 9783642042676
VL - 5761 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 803
EP - 810
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009
Y2 - 20 September 2009 through 24 September 2009
ER -