Motivation: In prostate brachytherapy, transrectal ultrasound (TRUS) is used to visualize the anatomy, while implanted
seeds can be seen in C-arm fluoroscopy or CT. Intra-operative dosimetry optimization requires localization of the
implants in TRUS relative to the anatomy. This could be achieved by registration of TRUS images and the implants
reconstructed from fluoroscopy or CT. Methods: TRUS images are filtered, compounded, and registered on the
reconstructed implants by using an intensity-based metric based on a 3D point-to-volume registration scheme. A
phantom was implanted with 48 seeds, imaged with TRUS and CT/X-ray. Ground-truth registration was established
between the two. Seeds were reconstructed from CT/X-ray. Seven TRUS filtering techniques and two image similarity
metrics were analyzed as well. Results: For point-to-volume registration, noise reduction combined with beam profile
filter and mean squares metrics yielded the best result: an average of 0.38 ± 0.19 mm seed localization error relative to
the ground-truth. In human patient data C-arm fluoroscopy images showed 81 radioactive seeds implanted inside the
prostate. A qualitative analysis showed clinically correct agreement between the seeds visible in TRUS and
reconstructed from intra-operative fluoroscopy imaging. The measured registration error compared to the manually
selected seed locations by the clinician was 2.86 ± 1.26 mm. Conclusion: Fully automated seed localization in TRUS
performed excellently on ground-truth phantom, adequate in clinical data and was time efficient having an average
runtime of 90 seconds.
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.
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