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26 March 2008 3-D statistical cancer atlas-based targeting of prostate biopsy using ultrasound image guidance
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Abstract
Prostate cancer is a multifocal disease and lesions are not distributed uniformly within the gland. Several biopsy protocols concerning spatially specific targeting have been reported urology literature. Recently a statistical cancer atlas of the prostate was constructed providing voxelwise probabilities of cancers in the prostate. Additionally an optimized set of biopsy sites was computed with 94 - 96% detection accuracy was reported using only 6-7 needles. Here we discuss the warping of this atlas to prostate segmented side-fire ultrasound images of the patient. A shape model was used to speed up registration. The model was trained from over 38 expert segmented subjects off-line. This training yielded as few as 15-20 degrees of freedom that were optimized to warp the atlas surface to the patient's ultrasound image followed by elastic interpolation of the 3-D atlas. As a result the atlas is completely mapped to the patient's prostate anatomy along with optimal predetermined needle locations for biopsy. These do not preclude the use of additional biopsies if desired. A color overlay of the atlas is also displayed on the ultrasound image showing high cancer zones within the prostate. Finally current biopsy locations are saved in the atlas space and may be used to update the atlas based on the pathology report. In addition to the optimal atlas plan, previous biopsy locations and alternate plans can also be stored in the atlas space and warped to the patient with no additional time overhead.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ramkrishnan Narayanan, Dinggang Shen, Christos A. Davatzikos, E. David Crawford, Albaha Barqawi, Priya Werahera, Dinesh Kumar, and Jasjit S. Suri "3-D statistical cancer atlas-based targeting of prostate biopsy using ultrasound image guidance", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69142X (26 March 2008); https://doi.org/10.1117/12.770542
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