Guo H.,Instarecon, Inc. |
Renaut R.A.,Arizona State University
Computerized Medical Imaging and Graphics | Year: 2011
The expectation maximization algorithm is commonly used to reconstruct images obtained from positron emission tomography sinograms. For images with acceptable signal to noise ratios, iterations are terminated prior to convergence. A new quantitative and reproducible stopping rule is designed and validated on simulations using a Monte-Carlo generated transition matrix with a Poisson noise distribution on the sinogram data. Iterations are terminated at the solution which yields the most probable estimate of the emission densities while matching the sinogram data. It is more computationally efficient and more accurate than the standard stopping rule based on the Pearson's χ2 test. © 2010 Elsevier Ltd. Source
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 154.73K | Year: 2012
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 deliveredby x-ray CT. The reduction of this dose has therefore taken on increased importance, as evidenced by the recent NIH Summit on Managing Dose in CT with the mandate of achieving the routine sub-millisievert CT exam. Iterative reconstruction algorithms are akey part in accomplishing this goal, producing high-quality images from low-dose data by incorporating detailed models of the physics and statistics of the data acquisition process. Iterative algorithms based on these system models are beginning to enterthe marketplace, but currently these algorithms suffer from three main limitations: (i) they are a very expensive add-on; (ii) they leave out detailed modeling of the physics, thus limiting the available dose reduction; and (iii) they are 10 - 100 times slower than standard reconstruction, preventing their use as a default for routine scans. The key to fully enabling iterative algorithms is acceleration of the backprojection and reprojection computational bottleneck, which is accomplished through the use ofInstaRecon's fast hierarchical backprojection/reprojection operators. Accelerating the iterative algorithm enables it to run on a less expensive platform, delivering fast reconstruction rates, and opens the door to incorporation of other system modeling,allowing for further image quality improvement and dose reduction. Thus, low-dose imaging and iterative reconstruction can move from a high-end option to the default scanning mode for a wide range of CT scanner hardware. The overall goal of this SBIR project is to accelerate iterative reconstruction rates even further and incorporate additional system models to improve dose and artifact reduction capabilities. The system acceleration will be achieved through algorithmic modifications to the hierarchical operators and the iterative reconstruction loop itself. Additional system modeling wil be introduced at a reduced computational cost through incorporation into the hierarchical operators themselves, providing advanced, accelerated system models. The resultingsystem will be faster than existing iterative reconstruction platforms, run on less expensive hardware, with additional reduction in dose and artifact levels. 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, and significant improvement in diagnostic quality of CT scans of large patients, or of patients with prosthetic implants or cardiac pacemakers. Moreover, this project will help make iterative algorithm-based low-dose imaging a common scanning modality, reducing the burden of CT x-ray exposure for the patient population at large. PUBLIC HEALTH RELEVANCE: This project promises dramatic acceleration ofadvanced image formation algorithms in CT, with improved dose reducing capabilities. The increased reconstruction rates make it possible for low-dose imaging to be brought into routine clinical use. The resulting product will improve the detection of lungcancer and heart disease, enable 3D CT image-guided surgery and accurate radiotherapy for cancer, improve the imaging of large patients and patients with prosthetic implants and cardiac pacemakers, and reduce healthcare costs.
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 730.60K | Year: 2008
DESCRIPTION (provided by applicant): Image reconstruction in x-ray computer tomography (CT) scanners lags far behind the data acquisition. For example a whole-body scan using the latest 64-slice medical CT scanners, with sub-millimeter slice thickness, wh ich takes about 10 seconds to acquire, requires about 20 times as long to reconstruct. This computational lag occurs despite the use of expensive special-purpose computing hardware. Faster image reconstruction is critical for life-threatening trauma cases, and is key to enhancing the use of x-ray CT as a dynamic real-time imaging modality for cardiac imaging, fluoroscopy and interventional applications. Furthermore, faster reconstruction is needed to enable new applications using computationally demanding i terative reconstruction methods that can overcome metal artifacts, improve image quality, and reduce the x-ray dose required to achieve acceptable image quality. Similarly, faster reconstruction is desired in CT security imaging, especially for scanning of checked luggage at airports. To date, acceleration of image reconstruction in CT scanners has been achieved only by scaling the computing hardware. However, because of the ever-increasing speed demands, simply scaling the hardware (parallelizing, upsizing , or using more processors) carries a prohibitive price tag. The objective of this project is to achieve very large speed-ups through the use of more clever image reconstruction algorithms (i.e. more clever mathematics), which were developed and patented a t the University of Illinois and have been licensed to InstaRecon. These algorithms reduce the mathematical operation counts for the reconstruction by factors of 10 to 50 for 512 W 512 pixel images typical in medical applications. We propose to develop, ev aluate, and validate a hardware prototype of an ultra-fast algorithmically-accelerated image reconstruction engine for three- dimensional cone-beam CT scanners. The hardware platform will be a reconfigurable field-programmable object array (FPOA), which of fers an attractive tradeoff between cost, speed, and flexibility. Specific aims of this project are prototypes of (i) an ultra-fast algorithmically accelerated hardware backprojector for the 3D circular imaging scan geometry; (ii) a fast complete software reconstruction algorithm for the 3D helical cone beam geometry, for the so-called long object problem, applicable to diagnostic imaging; and (iii) an ultra-fast algorithmically accelerated complete hardware reconstructor for the 3D helical cone beam long o bject geometry. We aim to provide a speed-up of at least 20W relative to two benchmarks: (i) conventional algorithms implemented on comparable hardware resources, and (ii) current best-in class commercial CT reconstruction rates. These speed-ups can be use d to implement more sophisticated algorithms to produce better image quality and for low-dose imaging. Stringent control of image quality by both objective and subjective measures and experiments will be applied throughout the course of the project to ensu re that the unprecedented speed-up is achieved while maintaining pristine image quality.
Agency: National Science Foundation | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 453.49K | Year: 2008
The SBIR Phase II project aims to develop a software package that enables rapid image reconstruction for X-ray Micro-CT (computerized Tomography) imaging. Over the last few years, Micro-CT has become a very valuable tool in pharmaceutical and basic research. Current Micro-CT scanners have reached a resolution of 1 micrometer and thus allow high resolution in-vivo and ex-vivo three dimensional examination of entire small animals such as mice. Other applications of Micro-CT range from functional imaging to use in material science. Yet, high resolution reconstruction of a single data set can be extremely time intensive, thus limiting the use. If analysis software capable of speeding up image reconstruction by 2 or 3 orders of magnitude can be developed, such software would significantly decrease the time to analyze high-resolution Micro-CT images and would thus increase the utility of this powerful imaging method.
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 734.33K | Year: 2008
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 it erative 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 Un iversity 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-detect or-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 impl ementations. 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 performa nce 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 prosth etic 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 ma king iterative algorithm-based dose and artifact reduction feasible for the first time.