The objective of this program is to improve cone-beam computed tomography (CBCT) imaging performed on an interventional C-arm for guiding catheter-directed embolization of liver tumor in particular, and multiple other interventional procedures. The current standard of care for hepatic catheter directed therapy uses an interventional C-arm and injected contrast agent to visualize tumor and its feeding vessels. C-arm CBCT can be used to understand the lesion anatomy, allowing 3D visualization of the tumor and feeding vessels. However, current C-arm CBCT is far from being optimized for the application due to a number of important limitations concerning exam time, radiation dose, image quality, and intrusiveness to workflow. These limitations can be addressed through the development of innovative CBCT scans and optimization-based reconstructions. In the project, we focus on tackling the practical limitations of the C-arm CBCT in image- guided liver-tumor catheter delivery through the development of innovative scans and optimization-based reconstructions, and demonstrating their translational potential to clinical tasks of image-guided liver-tumor treatment.
The specific aims of the project include (1) to develop optimization-based reconstruction for improving current C-arm imaging, (2) to develop scans and optimization-based reconstruction for further improving C-arm imaging, and (3) to verify, characterize, and evaluate optimization-based reconstructions. The clinical and technical significances of the project are high, because it can directly impact significantly intervention and treatment of liver-cancer and other forms of cancer, particularly, in the lung and kidney, and because it can lead to the development of advanced C-arm CBCT technology for addressing technical challenges such as sparse-view, truncation, and limited-angular-range problems of extremely high practical merit. The project has a high level of innovation, because it includes numerous creative ideas and methods for enhancing workflow and for reducing contrast volume, radiation dose, and patient discomfort.

Public Health Relevance

The objective of the project is to investigate, develop, and evaluate innovative reconstruction algorithms and imaging configurations in advanced C-arm cone-beam computed tomography (CBCT) for providing guidance to embolization of liver cancer. The research can lead to not only improved embolization accuracy and streamlined workflow but also potentially new protocols for new applications with reduced contrast-agent volume and washout, and radiation dose to the patient and staff. The project can impact image-guided surgery and radiation therapy by further improving substantially advanced C-arm CBCT, and can provide important insights into streamlining other tomographic imaging modalities that are used in, or are under development for, clinical and pre-clinical studies.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA182264-02
Application #
8860154
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Farahani, Keyvan
Project Start
2014-07-01
Project End
2018-06-30
Budget Start
2015-07-01
Budget End
2016-06-30
Support Year
2
Fiscal Year
2015
Total Cost
Indirect Cost
Name
University of Chicago
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
Xia, Dan; Chang, Yu-Bing; Manak, Joe et al. (2018) Reduction of Angularly-Varying-Data Truncation in C-Arm CBCT Imaging. Sens Imaging 19:
Zhang, Zheng; Rose, Sean; Ye, Jinghan et al. (2018) Optimization-Based Image Reconstruction From Low-Count, List-Mode TOF-PET Data. IEEE Trans Biomed Eng 65:936-946
Chen, Buxin; Zhang, Zheng; Xia, Dan et al. (2018) Algorithm-enabled partial-angular-scan configurations for dual-energy CT. Med Phys 45:1857-1870
Aggrawal, Hari Om; Andersen, Martin S; Rose, Sean et al. (2018) A Convex Reconstruction Model for X-ray Tomographic Imaging with Uncertain Flat-fields. IEEE Trans Comput Imaging 4:17-31
Qiao, Zhiwei; Redler, Gage; Qian, Yuhua et al. (2018) Investigation of the preconditioner-parameter in the preconditioned Chambolle-Pock algorithm applied to optimization-based image reconstruction. J Xray Sci Technol 26:435-448
Rose, Sean D; Sanchez, Adrian A; Sidky, Emil Y et al. (2017) Investigating simulation-based metrics for characterizing linear iterative reconstruction in digital breast tomosynthesis. Med Phys 44:e279-e296
Schmidt, Taly Gilat; Barber, Rina Foygel; Sidky, Emil Y (2017) A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data. IEEE Trans Med Imaging 36:1808-1819
Chen, Buxin; Zhang, Zheng; Sidky, Emil Y et al. (2017) Image reconstruction and scan configurations enabled by optimization-based algorithms in multispectral CT. Phys Med Biol 62:8763-8793
Foygel Barber, Rina; Sidky, Emil Y; Gilat Schmidt, Taly et al. (2016) An algorithm for constrained one-step inversion of spectral CT data. Phys Med Biol 61:3784-818
Xia, Dan; Langan, David A; Solomon, Stephen B et al. (2016) Optimization-based image reconstruction with artifact reduction in C-arm CBCT. Phys Med Biol 61:7300-7333

Showing the most recent 10 out of 25 publications