In the project, we propose to develop, optimize, and translate advanced, exact algorithms for image reconstruction in helical cone-beam computed tomography (CBCT). Approximate reconstruction algorithms have been used for clinical helical CBCT, and they have been optimized, and work well, for many clinical cases. However, on a daily basis, radiologists also encounter CBCT images containing clinically significant artifacts, attaining levels that interfere with accurate diagnosis. The recently developed exact algorithms have the potential to eliminate or mitigate the artifacts while also reducing radiation dose. In order to translate these algorithms into clinic applications, many technical hurdles must be overcome. At this point, we believe that tackling these technical issues is actually more important than pursuing further purely theoretical algorithm development. Working through these technical problems of clinical significance will also provide a paradigm and roadmap that can be followed by research effort in the future if and when major new hardware developments are introduced. The proposed research is intended to fill in important gaps toward translating the theoretically exact algorithms to improving image quality, reducing imaging dose, and enabling new imaging capabilities in helical CBCT by tackling the issues. To achieve the overall objective of translating the newly developed exact algorithms to practical helical CBCT, we have designed four specific aims: (1) to develop and implement reconstruction algorithms tailored to practical helical CBCT;(2) to develop and optimize exact reconstruction algorithms for quantitative CT;(3) to test and evaluate the algorithms by using numerical and physical phantom data;and (4) to test and evaluate the algorithms in model- and human-observer studies. The first two aims focus on (a) the development, optimization, and translation of the exact algorithms that can significantly reduce the image artifacts in current clinical application of helical CBCT and (b) the investigation and exploitation of data redundancy for reducing the imaging dose and image artifacts in helical CBCT. The last two aims are designed for thorough evaluation of the algorithm performance in practical applications. A key motivation for the evaluation studies is to provide guidance for the tasks in Aims 1 and 2. We believe that the project is of high scientific and clinical significance in that it can expedite the translation of exact algorithms for improving image quality and reducing imaging dose. It can also produce new insights of significant implications for developing new CBCT imaging capability. The project has multiple facets of high innovation for addressing critical issues in the algorithms'translation to clinical applications and for investigating and using data redundancy for improving the image quality and lowering the imaging dose.
The objective of the project is to investigate, develop, and translate innovative reconstruction algorithms and imaging configurations for helical cone-beam computed tomography (CBCT). The research can lead not only to an improved diagnostic accuracy for many of the current clinical protocols and but also to a host of potentially new protocols for new applications. The project can also reveal new insights of significant implications for further improving advanced CBCT and other tomographic imaging modalities that are used in, or are under development for, clinical and pre-clinical studies.
|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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