Prostate cancer is the most common cancer after skin cancer and the second leading cause of cancer death in men in the United States. The options for radiotherapy treatment planning of prostate cancer are limited by CT's low soft tissue contract, MRI's distortion of prostate shape, and ultrasound's speckle noise and attenuation-induced imaging artifacts. Robust and automatic prostate segmentation has only achieved limited success in the past decades and remains a challenging task. A novel technique for reconstruction of medical imaging information is proposed in this project. Pairs of pathological useful and anatomically correct backscatter and attenuation fields can be reconstructed from ultrasound images, along with automatic structure segmentation. The excellent soft tissue contract and portability of ultrasound make it a promising modality for accurate determination of the actual prostate boundary, and to be integrated into prostate cancer treatment planning to give adequate target dose while minimizing radiation to surrounding normal tissues.
Four specific aims are proposed.
In aim 1, the method and algorithm based on variational principle will be developed and optimized to compensate for attenuation artifacts, and automatically segment anatomic structures in medical ultrasound images.
In aim 2, image acquisition protocol and system software will be developed to construct 3D ultrasound volumes and accurately register ultrasound images spatially.
In aim 3, the accuracy of the developed system will be quantified and verified in boundary segmentation, localized attenuation artifact correction, and spatial calibration.
In aim 4, the approach will be evaluated clinically and qualitatively by involving prostate cancer patients. Treatment plans will be designed based on the proposed method, and compared on dose coverage in the prostate, bladder and rectum. The significance of reduction in planning margin using the proposed method will be evaluated. This project exploits a greater potential of trans-abdominal ultrasound imaging in prostate cancer treatment planning than is currently being realized in daily verification. The proposed method will improve attenuation artifact correction, reveal hidden/additional clinic-important information, automatically delineate anatomic structures, increase cancer treatment accuracy, and reduce normal tissue toxicity.
Robust and automatic prostate segmentation has only achieved limited success in the past decades and remains a challenging task. This project exploits a greater potential of trans-abdominal ultrasound imaging in prostate cancer treatment planning than is currently being realized in daily verification. The proposed method will improve attenuation artifact correction, reveal hidden/additional clinic-important information, automatically delineate anatomic structures, increase cancer treatment accuracy, and reduce normal tissue toxicity.
