Research Project
7623952
DESCRIPTION (provided by applicant): With the increased use of x-ray CT, the development of a market for CT screening exams, and the imaging of younger patients, there is a growing concern about the public health risk caused by the radiation dose delivered by x-ray CT. The reduction of this dose has therefore taken on increased importance. The objective of this work is to develop a novel computationally-based approach to the reduction of patient x-ray dose in diagnostic CT scanners. The approach will use iterative algorithms for the image formation, which can produce high-quality images from low-dose data by incorporating detailed models of the physics and statistics of the data acquisition process. To date, such iterative algorithms have been little used in practice owing to their high computational complexity. This problem will be solved by using revolutionary fast algorithms for the backprojection and reprojection steps in the iterative algorithm. These fast algorithms were developed and patented at the University of Illinois by members of the project research team and collaborators, and further developed at InstaRecon, Inc. Using this technology, speed-up factors of 10x - 50x have been achieved in software prototypes. Phase I developed fast statistical and physics-based iterative algorithms for reduced-dose and reduced artifact high-precision tomography for the 2D fan-beam imaging geometry. In Phase II, the methodology and algorithms will be extended to the dominant imaging geometries in modern multi-detector-row diagnostic scanners: helical multislice, conebeam with a circular source trajectory, and helical conebeam. The success of Phase I suggests an acceleration of the iterative algorithm by a factor of 10 or better compared to previous, conventional implementations. This large algorithmic acceleration will be further augmented by implementing the algorithms on a parallel computing platform. The resulting prototype reconstruction system will match the throughput of current CT scanners, while providing dose and artifact reduction. Significant attention will be devoted to thorough testing and quantitative characterization of the speed and image quality of the new dose reduction technology. Benefits of the new technology will include superior low-dose performance in dose-critical applications such as pediatric, screening for lung cancer or heart disease, and interventional imaging. Additionally, it will offer significant improvement in diagnostic quality of CT scans of large patients and of patients with prosthetic implants or cardiac pacemakers. The algorithmic speedup allows for the system to run on a modest hardware platform, making this technology attractive for adoption by scanner manufacturers. This project promises to revolutionize CT as we know it, by making iterative algorithm-based dose and artifact reduction feasible for the first time.
