The broad objective of the project is to investigate, develop, and evaluate approaches for accurate and efficient acquisition and volume reconstruction in helical cone-beam computed tomography (CT), with an emphasis on targeted imaging of regions of interest (ROIs) within a subject. Despite the tremendous progress made in the last decade, it is perhaps fair to say that the development of optimal algorithms in helical cone-beam CT remains open in that a number of important problems, such as development of minimum-data approaches and consideration of physical factors, have not yet been adequately addressed. During the past four years, our project on multi-slice helical CT has been funded by an NIH R01 grant. Our effort on the project has been successful and productive and, more importantly, has laid down a solid foundation for us to move onto the next phase of research on helical cone-beam CT. Recently, we have made, we believe, a fundamental breakthrough in image-reconstruction theory for helical cone-beam CT, which has paved the way for developing innovative algorithms for exact ROI-image reconstruction for scanning configurations previously thought to produce incomplete data. We expect that the new algorithms will yield accurate volume reconstruction, will result in substantial reductions in patient dose, and will extend the volume coverage of the patient. The project has significant implications for applications of helical conebeam CT to clinical and animal imaging.
The specific aims of the project are: (1) to develop innovative algorithms for image reconstruction in helical cone-beam CT, (2) to develop novel algorithms for ROI-image reconstruction in helical cone-beam CT, (3) to investigate the physical properties of the proposed algorithms in discrete forms, and (4) to evaluate the proposed algorithms in computer-simulation and real-data studies. In the past several years, we have made successful strides in understanding and advancing image reconstruction in multi-slice helical CT and in helical cone-beam CT. We believe that our expertise and insights, which have been developed and accumulated in these studies, have placed us in a unique and strong position to perform and accomplish the proposed research on helical cone-beam CT and its clinical applications successfully and in a timely manner.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB000225-08
Application #
7280464
Study Section
Special Emphasis Panel (ZRG1-SBIB-L (91))
Program Officer
Haller, John W
Project Start
2000-07-01
Project End
2010-08-31
Budget Start
2007-09-01
Budget End
2010-08-31
Support Year
8
Fiscal Year
2007
Total Cost
$454,883
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
Pearson, Erik; Pan, Xiaochuan; Pelizzari, Charles (2016) Dynamic intensity-weighted region of interest imaging for conebeam CT. J Xray Sci Technol 24:361-77
Zhang, Zheng; Han, Xiao; Pearson, Erik et al. (2016) Artifact reduction in short-scan CBCT by use of optimization-based reconstruction. Phys Med Biol 61:3387-406
Han, Xiao; Pearson, Erik; Pelizzari, Charles et al. (2015) Algorithm-enabled exploration of image-quality potential of cone-beam CT in image-guided radiation therapy. Phys Med Biol 60:4601-33
Jørgensen, Jakob S; Sidky, Emil Y; Hansen, Per Christian et al. (2015) EMPIRICAL AVERAGE-CASE RELATION BETWEEN UNDERSAMPLING AND SPARSITY IN X-RAY CT. Inverse Probl Imaging (Springfield) 9:431-446
Graff, Christian G; Sidky, Emil Y (2015) Compressive sensing in medical imaging. Appl Opt 54:C23-44
Rose, Sean; Andersen, Martin S; Sidky, Emil Y et al. (2015) Noise properties of CT images reconstructed by use of constrained total-variation, data-discrepancy minimization. Med Phys 42:2690-8
Sidky, Emil Y; Chartrand, Rick; Boone, John M et al. (2014) Constrained TpV Minimization for Enhanced Exploitation of Gradient Sparsity: Application to CT Image Reconstruction. IEEE J Transl Eng Health Med 2:
Sanchez, Adrian A; Sidky, Emil Y; Pan, Xiaochuan (2014) Region of interest based Hotelling observer for computed tomography with comparison to alternative methods. J Med Imaging (Bellingham) 1:031010
Sanchez, Adrian A; Sidky, Emil Y; Pan, Xiaochuan (2014) Task-based optimization of dedicated breast CT via Hotelling observer metrics. Med Phys 41:101917
Bian, Junguo; Yang, Kai; Boone, John M et al. (2014) Investigation of iterative image reconstruction in low-dose breast CT. Phys Med Biol 59:2659-85

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