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.
|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|
|JÃ¸rgensen, 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|
|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|
|Graff, Christian G; Sidky, Emil Y (2015) Compressive sensing in medical imaging. Appl Opt 54:C23-44|
|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:|
|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|
|Sanchez, Adrian A; Sidky, Emil Y; Pan, Xiaochuan (2014) Task-based optimization of dedicated breast CT via Hotelling observer metrics. Med Phys 41:101917|
|Sidky, Emil Y; Kraemer, David N; Roth, Erin G et al. (2014) Analysis of iterative region-of-interest image reconstruction for x-ray computed tomography. J Med Imaging (Bellingham) 1:031007|
|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|
|Sidky, Emil Y; Jorgensen, Jakob S; Pan, Xiaochuan (2013) First-order convex feasibility algorithms for x-ray CT. Med Phys 40:031115|
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