URBANA, IL, United States
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Methods and systems for computed tomography. A subject is imaged with a divergent beam source using a plurality of source positions and a detector array comprising a plurality of detector bins to obtain a representation of the subject including a plurality of image voxels. Contribution of a voxel to a detector bin in a computed forward projection or a detector bin to a voxel in a backprojection is determined by the intensity value assigned to the voxel, or to the detector bin, respectively, multiplied by the product of an area or volume of overlap and an additional weighting factor, and the area or volume of overlap is determined by overlap of the voxel with the area or volume of the image illuminated by a ray-wedge defined by detector bin edge rays.


Brokish J.,Instarecon, Inc. | Keesing D.B.,Instarecon, Inc. | Bresler Y.,Instarecon, Inc. | Bresler Y.,University of Illinois at Urbana - Champaign
Progress in Biomedical Optics and Imaging - Proceedings of SPIE | Year: 2010

This is the first report on a new fast statistical iterative reconstruction algorithm for conebeam with a circular source trajectory, accelerated by InstaRecon's fast O(N3logN) hierarchical cone beam backprojection1 and reprojection algorithms. We report on the results of image quality and run-time comparisons with iterative algorithms based on conventional backprojection and reprojection. We demonstrate that the iterative algorithm introduced here can provide Image quality indistinguishable from an iterative algorithm using conventional BP/RP operators, while providing almost a 10x speedup in reconstruction rates. Combining the 10x algorithmic acceleration with additional hardware acceleration by FPGA, Cell, or GPU implementation, this work indicates the feasibility of iterative reconstruction algorithms for dose reduction and image quality improvement in routine CT practice, at competitive speeds and affordable cost. © 2010 SPIE.


Brokish J.,Instarecon, Inc. | Sack P.,Instarecon, Inc. | Bresler Y.,Instarecon, Inc. | Bresler Y.,University of Illinois at Urbana - Champaign
Progress in Biomedical Optics and Imaging - Proceedings of SPIE | Year: 2010

In this paper, we describe the first implementation and performance of a fast O(N3logN) hierarchical backprojection algorithm for cone beam CT with a circular trajectory1,developed on a modern Graphics Processing Unit (GPU). The resulting tomographic backprojection system for 3D cone beam geometry combines speedup through algorithmic improvements provided by the hierarchical backprojection algorithm with speedup from a massively parallel hardware accelerator. For data parameters typical in diagnostic CT and using a mid-range GPU card, we report reconstruction speeds of up to 360 frames per second, and relative speedup of almost 6× compared to conventional backprojection on the same hardware. The significance of these results is twofold. First, they demonstrate that the reduction in operation counts demonstrated previously for the FHBP algorithm can be translated to a comparable run-time improvement in a massively parallel hardware implementation, while preserving stringent diagnostic image quality. Second, the dramatic speedup and throughput numbers achieved indicate the feasibility of systems based on this technology, which achieve real-time 3D reconstruction for state-of-the art diagnostic CT scanners with small footprint, high-reliability, and affordable cost. © 2010 SPIE.


Komarov S.A.,Washington University in St. Louis | Wu H.,Washington University in St. Louis | Wu H.,Instarecon, Inc. | Keesing D.B.,Washington University in St. Louis | And 4 more authors.
IEEE Transactions on Nuclear Science | Year: 2010

The integration of a high resolution PET insert into a conventional PET system can significantly improve the resolution and the contrast of its images within a reduced imaging field of view. For the rest of the scanner imaging field of view, the insert is a highly attenuating and scattering media. In order to use all available coincidence events (including coincidences between 2 detectors in the original scanner, namely the scanner-scanner coincidences), appropriate scatter and attenuation corrections have to be implemented. In this work, we conducted a series of Monte Carlo simulations to estimate the composition of the scattering background and the importance of the scatter correction. We implemented and tested the Single Scatter Simulation (SSS) algorithm for a hypothetical system and show good agreement between the estimated scatter using SSS and Monte Carlo simulated scatter contribution. We further applied the SSS to estimate scatter contribution from an existing prototype PET insert for a clinical PET/CT scanner. The results demonstrated the applicability of SSS to estimate the scatter contribution within a clinical PET/CT system even when there is a high resolution half ring PET insert device in its imaging field of view. © 2010 IEEE.


Keesing D.B.,Washington University in St. Louis | Keesing D.B.,Instarecon, Inc. | Mathews A.,Washington University in St. Louis | Komarov S.,Washington University in St. Louis | And 4 more authors.
Physics in Medicine and Biology | Year: 2012

Virtual-pinhole PET (VP-PET) imaging is a new technology in which one or more high-resolution detector modules are integrated into a conventional PET scanner with lower resolution detectors. It can locally enhance the spatial resolution and contrast recovery near the add-on detectors, and depending on the configuration, may also increase the sensitivity of the system. This novel scanner geometry makes the reconstruction problem more challenging compared to the reconstruction of data from a stand-alone PET scanner, as new techniques are needed to model and account for the non-standard acquisition. In this paper, we present a general framework for fully 3D modeling of an arbitrary VP-PET insert system. The model components are incorporated into a statistical reconstruction algorithm to estimate an image from the multi-resolution data. For validation, we apply the proposed model and reconstruction approach to one of our custom-built VP-PET systemsa half-ring insert device integrated into a clinical PET/CT scanner. Details regarding the most important implementation issues are provided. We show that the proposed data model is consistent with the measured data, and that our approach can lead to reconstructions with improved spatial resolution and lesion detectability. © 2012 Institute of Physics and Engineering in Medicine.


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.


Grant
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.


Grant
Agency: Department of Homeland Security | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 99.92K | Year: 2014

Accurate, realtime detection of explosives is a demanding application. High detection accuracy with a low false positive rate is desired. Noise and artifacts in reconstructed images, especially in the presence of metal, degrade the ability of detection algorithms to identify object's shape, volume, and composition. Model-based iterative reconstruction (MBIR) has been demonstrated to improve image quality over conventional direct reconstruction techniques - improving image resolution while suppressing noise and artifacts. The drawback is the significant increase of computation required for image formation, leading to an algorithm that is infeasible: either the reconstruction is too slow, or the hardware required for the desired throughput is too expensive. We will first establish a baseline iterative algorithm matching published state of the art image quality improvements. We will then reduce its computational demands 60 fold via algorithmic speedup. Cornerstone to this effort are the InstaRecon fast hierarchical operators, which reduce the computational complexity of the main computational burden of MBIR. Additional sources of algorithmic acceleration include variable splitting techniques for improved convergence rate, and approximate gradients. We will assess the combination of these algorithmic accelerations with hardware acceleration such as GPUs in the final technical report. Computation is a limiting factor in bringing iterative reconstruction to the market. Only so much hardware acceleration can be used without making cost a prohibitive factor. The algorithmic acceleration proposed here is an essential component of an iterative reconstruction system that can run at the required throughput on a modest hardware platform, making commercial deployment economically feasible.


PubMed | Instarecon, Inc.
Type: Journal Article | Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society | 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 Pearsons (2) test.


Instarecon, Inc. | Entity website

September 1st, 2014 - InstaRecon Awared Phase I DHS Grant InstaRecon awarded a Phase I SBIR contract from the Department of Homeland Security for Algorithmically Accelerated Iterative Reconstruction for Fast and Cost-Effective CT-based Explosive Detection Equipment. July 1st, 2014 - InstaRecon Software Becomes Standard for Bruker MicroCT InstaRecons ultra-fast cone beam reconstruction software, the InstaRecon(R) CBR Premium(TM), is now included as the standard reconstruction engine on all high resolution micro CT scanners from Bruker microCT ...

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