In prostate brachytherapy, a transrectal ultrasound (TRUS) will show the prostate boundary but not all the implanted seeds, while fluoroscopy will show all the seeds clearly but not the boundary. We propose an intensity-based registration between TRUS images and the implant reconstructed from fluoroscopy as a means of achieving accurate intra-operative dosimetry. The TRUS images are first filtered and compounded, and then registered to the fluoroscopy model via mutual information. A training phantom was implanted with 48 seeds and imaged. Various ultrasound filtering techniques were analyzed, and the best results were achieved with the Bayesian combination of adaptive thresholding, phase congruency, and compensation for the non-uniform ultrasound beam profile in the elevation and lateral directions. The average registration error between corresponding seeds relative to the ground truth was 0.78 mm. The effect of false positives and false negatives in ultrasound were investigated by masking true seeds in the fluoroscopy volume or adding false seeds. The registration error remained below 1.01 mm when the false positive rate was 31%, and 0.96 mm when the false negative rate was 31%. This fully automated method delivers excellent registration accuracy and robustness in phantom studies, and promises to demonstrate clinically adequate performance on human data as well.

This paper presents a novel approach and its preliminary laboratory results for the employment of ultrasound (US) imaging in intraoperative guidance of computer-assisted orthopaedic surgeries (CAOS). The goal is to register live intraoperative US images with preoperative surgical planning data using minimal number of images. Preoperatively, a set of 2D US images are acquired with the corresponding positional information of the US probe provided by an optical tracking system. Using calibration parameters, the position of every pixel in the acquired images is transformed into the world coordinate frame to construct a 3D volumetric representation of the targeted anatomy for surgical planning. Intraoperatively., the surgeon takes live US images from the patient with the position of the US probe tracked in real time. A mutual-information-based registration algorithm is then used to find the closest match to the live image in the preoperative US image database. Because the position of the preoperative image inside the US volume is known, we are able to register the preoperative US volume to the live image, thus to the patient. Experiments have shown the registration algorithm. has sub-millimeter accuracy in localizing the best, match between the intraoperative and pre-operative images, demonstrating great potential for orthopaedic surgery applications. This method has some significant advantages over the previously reported US-guided CAOS techniques: it requires no segmentation, and employs only a few intraoperative images to accurately and robustly localize the patient. Preliminary laboratory results on both a Sawbones model of a radius bone and human subjects are presented.