Validation of a hybrid Doppler ultrasound vessel-based registration algorithm for neurosurgery.

PURPOSE: We describe and validate a novel hybrid nonlinear vessel registration algorithm for intra-operative updating of preoperative magnetic resonance (MR) images using Doppler ultrasound (US) images acquired on the dura for the correction of brain-shift and registration inaccuracies. We also introduce an US vessel appearance simulator that generates vessel images similar in appearance to that acquired with US from MR angiography data.
METHODS: Our registration uses the minimum amount of preprocessing to extract vessels from the raw volumetric images. This prevents the removal of important registration information and minimizes the introduction of artifacts that may affect robustness, while reducing the amount of extraneous information in the image to be processed, thus improving the convergence speed of the algorithm. We then completed 3 rounds of validation for our vessel registration method for robustness and accuracy using (i) a large number of synthetic trials generated with our US vessel simulator, (ii) US images acquired from a real physical phantom made from polyvinyl alcohol cryogel, and (iii) real clinical data gathered intra-operatively from 3 patients.
RESULTS: Resulting target registration errors (TRE) of less than 2.5 mm are achieved in more than 90 % of the synthetic trials when the initial TREs are less than 20 mm. TREs of less than 2 mm were achieved when the technique was applied to the physical phantom, and TREs of less than 3 mm were achieved on clinical data.
CONCLUSIONS: These test trials show that the proposed algorithm is not only accurate but also highly robust to noise and missing vessel segments when working with US images acquired in a wide range of real-world conditions.