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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data IV"

共 516 条记录，以下是271-280 订阅

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Classification of cryo electron microscopy images, noisy tomographic images recorded with unknown projection directions, by simultaneously estimating reconstructions and application to an assembly mutant of Cowpea Chlorotic Mottle Virus and portals of the bacteriophage P22

Classification of cryo electron microscopy images, noisy tom...

引用

conference on image reconstruction from incomplete data VI

作者： Lee, Junghoon Zheng, Yili Yin, Zhye Doerschuk, Peter C. Johnson, John E. Johns Hopkins Univ Elect & Comp Engn Baltimore MD 21218 USA Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA Gen Elect Global Res Niskayuna 12309 NY USA Scripps Res Inst Dept Mol Biol La Jolla CA 92037 USA

ISBN: (纸本)9780819482969

Cryo electron microscopy is frequently used on biological specimens that show a mixture of different types of object. Because the electron beam rapidly destroys the specimen, the beam current is minimized which leads to noisy images (SNR substantially less than 1) and only one projection image per object (with an unknown projection direction) is collected. For situations where the objects can reasonably be described as coming from a finite set of classes, an approach based on joint maximum likelihood estimation of the reconstruction of each class and then use of the reconstructions to label the class of each image is described and demonstrated on two challenging problems: an assembly mutant of Cowpea Chlorotic Mottle Virus and portals of the bacteriophage P22.

关键词： statistical image classification statistical inverse problem statistical signal reconstruction tomography cryo electron microscopy virus structure Cowpea Chlorotic Mottle Virus portal of Bacteriophage P22

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Imposing spatio-temporal support in magnetic resonance angiographic imaging

Imposing spatio-temporal support in magnetic resonance angio...

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conference on image reconstruction from incomplete data VI

作者： Bones, Philip J. Vafadar, Bahareh Watts, Richard Wu, Bing Univ Canterbury Dept Elect & Comp Engn Computat Imaging Grp Christchurch New Zealand Univ Canterbury Dept Phys & Astron Christchurch New Zealand Duke Univ Brain Imaging & Analysis Ctr Sch Med Durham NC 27708 USA

ISBN: (纸本)9780819482969

A method to improve time resolution in 3D contrast-enhanced magnetic resonance angiography (CE-MRA) is proposed. A temporal basis based on prior knowledge of the contrast flow dynamics is applied to a sequence of image reconstructions. In CE-MRA a contrast agent (gadolinium) is injected into a peripheral vein and MR data is acquired as the agent arrives in the arteries and then the veins of the region of clinical interest. The acquisition extends over several minutes. Information is effectively measured in 3D k-space (spatial frequency space) one line at-a-time. That line may be along a Cartesian grid line in k-space, a radial line or a spiral trajectory. A complete acquisition comprises many such lines but in order to improve temporal resolution, reconstructions are made from only partial sets of k-space data. By imposing a basis for the temporal changes, based on prior expectation of the smoothness of the changes in contrast concentration with time, it is demonstrated that a significant reduction in artifacts caused by the under-sampling of k-space can be achieved. The basis is formed from a set of gamma variate functions. Results are presented for a simulated set of 2D spiral-sampled CE-MRA data.

关键词： Magnetic resonance imaging data acquisition Arteries Temporal resolution Angiography Signal to noise ratio Gadolinium image quality Veins Magnetism

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Research of the image reconstruction from incomplete Projection data Based on the Homotopy Mapping

Research of the Image Reconstruction from Incomplete Project...

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2nd International Asia conference on Informatics in Control, Automation and Robotics (CAR)

作者： Li Fei Pan Jin-Xiao North Univ China Coll Sci Dept Math Taiyuan Peoples R China North Univ China Natl Key Lab Elect Measurement Technol Taiyuan Peoples R China

ISBN: (纸本)9781424451944

Multi-objective optimization reconstruction algorithm is one of the main methods to solve the limited angle image reconstruction. However we have great difficulty in selecting the right parameter reasonably because there is not only one objective function. So we give a new method to choose the parameter which was called homotopy parameter based on the theory of homotopy in this paper. Finally, the simulations were carried out to verify the proposed strategy, and the simulation results show that this algorithm is superior to the conventional multi-objective optimization methods in many aspects, such as less reconstruction errors, higher smoothness and gray value resolution.

关键词： multi-objective optimization limited angle image reconstruction homotopy parameter

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Rethinking image Registration on Customizable Hardware

Rethinking Image Registration on Customizable Hardware

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conference on image reconstruction from incomplete data VI

作者： Bowman, David Tahtali, Murat Lambert, Andrew UNSW ADFA Sch Engn & IT Canberra ACT 2600 Australia

ISBN: (纸本)9780819482969

image registration is one of the most important tasks in image processing and is frequently one of the most computationally intensive. In cases where there is a high likelihood of finding the exact template in the search image, correlation-based methods predominate. Presumably this is because the computational complexity of a correlation operation can be reduced substantially by transforming the task into the frequency domain. Alternative methods such as minimum Sum of Squared Differences (minSSD) are not so tractable and are normally disfavored. This bias is justified when dealing with conventional computer processors since the operations must be conducted in an essentially sequential manner however we demonstrate it is normally unjustified when the processing is undertaken on customizable hardware such as FPGAs where tasks can be temporally and/or spatially parallelized. This is because the gate-based logic of an FPGA is better suited to the tasks of minSSD i.e. signed-addition hardware can be very cheaply implemented in FPGA fabric, and square operations are easily implemented via a look-up table. In contrast, correlation-based methods require extensive use of multiplier hardware which cannot be so cheaply implemented in the device. Even with modern DSP-oriented FPGAs which contain many "hard" multipliers we experience at least an order of magnitude increase in the number of minSSD hardware modules we can implement compared to cross-correlation modules. We demonstrate successful use and comparison of techniques within an FPGA for registration and correction of turbulence degraded images.

关键词： image Registration FPGA Cross Correlation Minimum Sum of Squared Differences

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Metal Artifact Reduction in Computed Tomography by Constrained Optimization

Metal Artifact Reduction in Computed Tomography by Constrain...

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conference on Medical Imaging - Physics of Medical Imaging

作者： Zhang, Xiaomeng Wang, Jing Xing, Lei Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Radiat Oncol Stanford CA 94305 USA

ISBN: (纸本)9780819480231

Computed tomography (CT) streak artifacts caused by metal implants have long been recognized as a problem that limits various applications of CT imaging. An effective and robust algorithm is highly desirable to minimize metal artifacts and achieve clinically acceptable CT images. In this work, the raw projection data is viewed as "incomplete" in the presence of metal shadows. Shape and location of metal objects are automatically identified and used as prior knowledge for accurate segmentation of metal shadows in projection domain. An iterative algorithm based on constrained optimization is then used for the image reconstruction. This algorithm minimizes a quadratic penalized smoothness measure function of the image, subject to the constraint that the estimated projection data is within a specified tolerance of the available metal-shadow-excluded projection data, with image non-negativity enforced. The constrained minimization problem is optimized through the combination of projection onto convex sets (POCS) and steepest gradient descent of the smoothness measure objective. Digital phantom study shows that the proposed constrained optimization algorithm has superior performance in reducing metal artifacts, suppressing noise and improving soft-tissue visibility. Some comparisons are performed with the filtered-back-projection (FBP), FDK, POCS and constrained optimization with total-variation (TV) objective. Although the algorithm is presented in the context of metal artifacts, it can be generated to image reconstruction from incomplete projections caused by limited angular range or low angular sampling rate in both 2D and 3D cases.

关键词： computed tomography (CT) metal artifacts image reconstruction optimization

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FIT3D Toolbox: multiple view geometry and 3D reconstruction for MATLAB

FIT3D Toolbox: multiple view geometry and 3D reconstruction ...

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conference on Electro-Optical Remote Sensing, Photonic Technologies, and Applications iv

作者： Esteban, Isaac Dijk, Judith Groen, Frans Univ Amsterdam ISLA NL-1012 WX Amsterdam Netherlands TNO Def Secur & Safety Elect Opt Syst Rijswijk Netherlands

ISBN: (纸本)9780819483539

FIT3D is a Toolbox built for Matlab that aims at unifying and distributing a set of tools that will allow the researcher to obtain a complete 3D model from a set of calibrated images. In this paper we motivate and present the structure of the toolbox in a tutorial and example based approach. Given its flexibility and scope we believe that FIT3D represents an exciting opportunity for researchers that want to apply one particular method with real data without the need for extensive additional programming.

关键词： Cameras 3D modeling Motion estimation Calibration 3D image processing Distortion MATLAB Motion models image processing 3D image reconstruction

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Evaluation and optimization of the maximum-likelihood approach for image reconstruction in digital breast tomosynthesis

Evaluation and optimization of the maximum-likelihood approa...

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conference on Medical Imaging - Physics of Medical Imaging

作者： Jerebko, Anna K. Mertelmeier, Thomas Siemens AG Healthcare D-91050 Erlangen Germany

ISBN: (纸本)9780819480231

Digital Breast Tomosynthesis* (DBT) suffers from incomplete data and poor quantum statistics limited by the total dose absorbed in the breast. Hence, statistical reconstruction assuming the photon statistics to follow a Poisson distribution may have some advantages. This study investigates state-of-art iterative maximum likelihood (ML) statistical reconstruction algorithms for DBT and compares the results with simple backprojection (BP), filtered backprojection (FBP), and iFBP (FBP with filter derived from iterative reconstruction). The gradient-ascent and convex optimization variants of the transmission ML algorithm are evaluated with phantom and clinical data. Convergence speed is very similar for both iterative statistical algorithms and after approximately 5 iterations all significant details are well displayed, although we notice increasing noise. We found empirically that a relaxation factor between 0.25 and 0.5 provides the optimal trade-off between noise and contrast. The ML-convex algorithm gives smoother results than the ML-gradient algorithm. The low-contrast CNR of the ML algorithms is between CNR for simple backprojection (highest) and FBP (lowest). Spatial resolution of iterative statistical and iFBP algorithms is similar to that of FBP but the quantitative density representation better resembles conventional mammograms. The iFBP algorithm provides the benefits of statistical iterative reconstruction techniques and requires much shorter computation time.

关键词： tomosynthesis iterative reconstruction statistical reconstruction filtered backprojection breast imaging mammography

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Comparison of reconstruction algorithms for sparse-array detection photoacoustic tomography

Comparison of reconstruction algorithms for sparse-array det...

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conference on Photons Plus Ultrasound - Imaging and Sensing 2010

作者： Chaudhary, G. Roumeliotis, M. Carson, J. J. L. Anastasio, M. A. IIT Med Imaging Res Ctr Dept Elect & Comp Engn Chicago IL 60616 USA IIT Med Imaging Res Ctr Dept Biomed Engn Chicago IL 60616 USA St Josephs Hlth Care Lawson Hlth Res Inst Imaging Program London ON N6A 4V2 Canada Univ West Ontario Dept Med Biophys London ON N6A 5C1 Canada

ISBN: (纸本)9780819479600

A photoacoustic tomography (PAT) imaging system based on a sparse 2D array of detector elements and an iterative image reconstruction algorithm has been proposed, which opens the possibility for high frame-rate 3D PAT. The efficacy of this PAT implementation is highly influenced by the choice of the reconstruction algorithm. In recent years, a variety of new reconstruction algorithms have been proposed for medical image reconstruction that have been motivated by the emerging theory of compressed sensing. These algorithms have the potential to accurately reconstruct sparse objects from highly incomplete measurement data, and therefore may be highly suited for sparse array PAT. In this context, a sparse object is one that is described by a relatively small number of voxel elements, such as typically arises in blood vessel imaging. In this work, we investigate the use of a gradient projection-based iterative reconstruction algorithm for image reconstruction in sparse-array PAT. The algorithm seeks to minimize an l(1)-norm penalized least-squares cost function. By use of computer-simulation studies, we demonstrate that the gradient projection algorithm may further improve the efficacy of sparse-array PAT.

关键词： Photoacoustic tomography sparse-array photoacoustic tomography image reconstruction

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Elimination of the twist distortion in ivUS and CAG image fusion based on the Frenet-Serret formulas

Elimination of the twist distortion in IVUS and CAG image fu...

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conference on Optics in Health Care and Biomedical Optics iv

作者： Li, Hongxiao Zhang, Zhaoxia Chen, Xiaodong Yu, Daoyin Optoelect Instruments & Med Image Proc Lab Tianjin 300072 Peoples R China

ISBN: (纸本)9780819483751

Cardiovascular disease can be diagnosed at higher accuracy by the fusion of ivUS (Intravascular Ultrasound) and CAG (Coronary Angiography) data. In the ivUS images acquisition process, pull-back path of the ultrasonic probe will twist due to intravascular blood flow and friction with vascular wall, which causes image distortion in the fusion of such ivUS images. In this paper, a new method used for reducing the twist between adjacent frames of ivUS is presented. First, we establish a rough perspective projection imaging model from the crossing information of two almost perpendicular projective angiography images. Then we use a discrete approximation of the Frenet-Serret formulas to calculate ivUS frames' relative twist by sequential triangulation method and correct the twist. Finally, coronary lumen data extracted from the corrected ivUS images are added to 3D transducer paths which are reconstructed by the model mentioned above. reconstruction of the coronary artery which contains the lumen information of ivUS removes blind spots in CAG and provides a full view of artery which is absent in ivUS images.

关键词： coronary angiography intravascular ultrasound fusion twist distortion Frenet-Serret formulas

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Two Satellite image Sets for the Training and Validation of image Processing Systems for Defense Applications

Two Satellite Image Sets for the Training and Validation of ...

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conference on Evolutionary and Bio-Inspired Computation - Theory and Applications iv

作者： Peterson, Michael R. Aldridge, Shawn Herzog, Britny Moore, Frank Univ Hawaii Dept Comp Sci & Engn Hilo HI 96720 USA Univ Alaska Anchorage Dept Comp Sci Anchorage AK USA

ISBN: (纸本)9780819481689

Many image processing algorithms utilize the discrete wavelet transform (DWT) to provide efficient compression and near-perfect reconstruction of image data. Defense applications often require the transmission of data at high levels of compression over noisy channels. In recent years, evolutionary algorithms (EAs) have been utilized to optimize image transform filters that outperform standard wavelets for bandwidth-constrained compression of satellite images. The optimization of these filters requires the use of training images appropriately chosen for the image processing system's intended applications. This paper presents two robust sets of fifty images each intended for the training and validation of satellite and unmanned aerial vehicle (UAV) reconnaissance image processing algorithms. Each set consists of a diverse range of subjects consisting of cities, airports, military bases, and landmarks representative of the types of images that may be captured during reconnaissance missions. Optimized algorithms may be "overtrained" for a specific problem instance and thus exhibit poor performance over a general set of data. To reduce the risk of overtraining an image filter, we evaluate the suitability of each image as a training image. After evolving filters using each image, we assess the average compression performance of each filter across the entire set of images. We thus identify a small subset of images from each set that provide strong performance as training images for the image transform optimization problem. These images will also provide a suitable platform for the development of other algorithms for defense applications. The images are available upon request from the contact author.

关键词： image processing wavelets evolutionary computation unmanned aerial vehicles satellite imagery

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