Group-wise registration of ultrasound to CT images of human vertebrae

Automatic registration of ultrasound (US) to computed tomography (CT) datasets is a challenge of considerable interest, particularly in orthopaedic and percutaneous interventions. We propose an algorithm for group-wise volume-to-volume 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 dynamically combines simulated US reflections from the vertebrae surfaces and surrounding soft tissue in the reconstructed CT, with scaled CT data to simulate US images of the spine anatomy. The simulated US data is used to register preoperative CT data to intra-operative US images. Covariance Matrix Adaption - Evolution Strategy (CMA-ES) is utilized as the optimization strategy. The registration is tested using a phantom of the lumbar spine (L3-L5). Initial misalignments of up to 8 mm were registered with a mean target registration error of 1.87±0.73 mm for L3, 2.79±0.93 mm for L4, 1.72±0.70 mm for L5, and 2.08±0.55 mm across the entire volume. To select an appropriate optimization strategy, we performed a volumeto- volume registration of US to CT of the lumbar spine, allowing no relative motion between vertebrae. We compare the results of this registration using three optimization strategies: simplex, gradient descent and CMA-ES. CMA-ES was found to converge slower than gradient descent and simplex, but was more robust for rigid volume-to-volume registration for initial misalignments up to 20 mm.