investigator_user investigator user funding collaborators pending menu bell message arrow_up arrow_down filter layers globe marker add arrow close download edit facebook info linkedin minus plus save share search sort twitter remove user-plus user-minus
  • Project leads
  • Collaborators

Computational Tools for Next Generation Volumetric Cone Beam Computed Tomography

Lei Xing

0 Collaborator(s)

Funding source

National Institutes of Health (NIH)
Cone beam CT (CBCT) imaging is becoming an indispensable tool in image guided interventions and many other clinical applications. The data processing method in current CBCT imaging is, however, deficient in multiple aspects, which often leads to severe artifacts and results in high imaging dose to the patients. The poor image quality causes much uncertainty in clinical decision-making and seriously impedes the maximal utilization of the technology. This project is directed at developing CBCT into a clinically accurate and reliable technique for delineation of tumor target, interventional guidance and radiation therapy treatment planning. In response to NIH PAR-09-218, we have assembled a team of investigators comprised of leaders in the fields of radiation oncology, medical physics, image and signal processing, optimization and applied mathematics and established research themes and projects that unify the common interests and expertise of these investigators. We draw on our extensive experience in CBCT imaging and scientific computing to develop the next generation of artifact-free and ultra-low dose CBCT. A number of innovative strategies to dealing with sparse, noisy, and missing data in CBCT imaging will be established. Specific aims are: (1) To achieve ultra-low dose CBCT by utilization of patient-specific prior data and compressed sensing; (2) to develop a divide-and-conquer approach for metal artifact removal in CBCT reconstruction; and (3) to obtain motion artifact-free images by effective use of inter-phase correlation of the projections. Successful completion of this project will provide high quality CBCT images with orders of magnitude less imaging dose. For image guided radiation therapy, the improved image quality will make accurate CBCT-based dose calculation and replanning possible, which will lay the foundation for next generation of adaptive therapy to optimally compensate for the patient setup error and inter- fractional anatomy change. With the reduction in imaging dose, the proposed technique will significantly reduce the risk of radiation-induced secondary cancers and contribute to the safe and efficient use of volumetric X-ray imaging techniques in routine clinical practice.

Related projects