A numerical simulator in VC++ on PC for iterative image reconstruction
|
|
- Laurence Stewart
- 5 years ago
- Views:
Transcription
1 Journal of X-Ray Science and Technology 11 (2003) IOS Press A numerical simulator in VC++ on PC for iterative image reconstruction Xiang Li a,, Ming Jiang c and Ge Wang a,b a Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA xiang-li@uiowa.edu b Department of Radiology, University of Iowa, Iowa City, IA 52242, USA ge-wang@uiowa.edu c School of Mathematics, Beiing Universty, 5 Summer Palace Street, Beiing, China iangm@math.pku.edu.cn Abstract. With the development of computing technology, there is an increasing interest in iterative CT image reconstruction. To study iterative reconstruction algorithms, we have developed a software simulator IterCT in VC++ on PC and made it publicly available on the Internet. In the latest version of the simulator, we have implemented four representative iterative image reconstruction algorithms, which are expectation maximization (EM) method and its ordered-subset version (OSEM), the simultaneous algebraic reconstruction technique (SART) and its ordered-subset version (OSSART). The filtered backproection method is included as the benchmark. Keywords: Computed Tomography (CT), image reconstruction, iterative reconstruction, expectation maximization (EM), simultaneous algebraic reconstruction technique (SART), ordered-subset 1. Introduction With the development of computing technology and X-ray tomography algorithms, it is appealing to shift from filtered backproection towards iterative reconstruction [13,14]. The iterative approach is valuable and powerful in the cases of incomplete and/or noisy proection data. The basic idea of iterative reconstruction is to divide a cross- section or a volume into finite elements (pixels and voxels in two and three dimensions, respectively), model the imaging process as a linear system, and then invert this system iteratively [9,10]. The expectation maximization (EM) algorithm has been successful in emission and transmission tomography. The iterative algorithm for positron emission tomography (PET) was proposed by Shepp and Vardi [3]. It determines an optimal estimate of the underlying emitting source distribution in the sense of maximizing the likelihood. Snyder et al. demonstrated that the likelihood function is equivalent to the I- divergence, and gave a deterministic interpretation to this EM formula [11]. Wang et al. applied the EM formula to several transmission CT problems [1]. The simultaneous algebraic reconstruction technique (SART) was proposed by Andersen and Kak as an improvement to the well-known algebraic Corresponding author: Xiang Li and Ge Wang, CT/MicroCT Lab, C721 GH, University of Iowa Hospital and Clinics, Iowa City, IA 52242, USA. Tel.: ; {Xiang-li, ge-wang}@uiowa.edu /03/$ IOS Press. All rights reserved
2 62 X. Li et al. / A numerical simulator in VC++ on PC for iterative image reconstruction R y -R R x -R Fig. 1. Cross section grid overlapped on an image. reconstruction technique (ART) [5]. Recently, Jiang and Wang proved that the image sequence generated by the SART converges to a weighted least-square solution [7]. The traditional EM and SART algorithms utilize all the proection data simultaneously in each iteration. The proection data can also be sub-grouped into pre-defined ordered subsets (OS) then reconstruction algorithms are applied to every subset in sequence. As a result, the EM and SART algorithms become OSEM [4] and OSSART algorithms, respectively. It was reported that OS versions converge much faster than their simultaneous counterparts [15]. We consider that the above four iterative algorithms, EM, OSEM, SART and OSSART, are representative for several important reasons. The obectives of the EM and SART schemes are the I-divergence and the least square measure, which have been axiomatically ustified in the nonnegative and real spaces, respectively [1,11]. These algorithms respectively maximize the Poisson and Gaussian likelihood with appropriate data. Furthermore, their computational structures are similar to the filtered backproection method, and relatively easy to implement. Although both the EM and SART algorithms are slow in terms of convergence, their OS versions may shorten reconstruction time by an order of magnitude. Finally, theoretical understanding has been gained over the past years regarding the convergence of the EM, OSEM, SART, and OSSART. To study those iterative reconstruction algorithms, we have developed a user-friendly simulator IterCT in Visual C++ on PC and made it publicly available on the Internet. ( iterct) In the following section, we review the four representative iterative reconstruction algorithms. In the third section, we describe the design and implementation of the software. In the last section, we discuss relevant issues and conclude the paper.
3 X. Li et al. / A numerical simulator in VC++ on PC for iterative image reconstruction 63 Updated Image Re-proect Data Measurement Subset Compare Measurement Subset Measurement Subset Measurement Subset Error (Weighted) Backproect Correction 2. Iterative reconstruction algorithms Fig. 2. Use of Ordered Subsets for Iterative Reconstruction. Without loss of generality we focus on the 2D case. A cross section can be divided into N square pixels, represented by an N-dimensional column vector x, as shown in Fig. 1. Each pixel has a constant X-ray linear attenuation coefficient x, =1,...,N. Similarly, proection data can be assembled into an M-dimensional column vector b, with the element being b i,i=1,...,m. Let A =(a i ) be the matrix mapping x to b, where a i measures the contribution of x to b i.wehave the following linear system: Ax = b (1) The CT image reconstruction problem is to find x. The EM algorithm is formulated as follows: ( M ) x k+1 = x k i=1 a i(b i /ˆb i ) M i=1 a, =1,...,N. (2) i The SART formula is expressed as: M x (k+1) = x (k) i=1 a i + M i=1 a i (b i ˆb i ) N =1 a i, =1,...,N. (3) The ordered-subset (OS) technique sub-groups proection into several sets of data. S t,t=1, 2,...,L, where L is the total number of subsets, and applies either the EM or SART algorithms to each of the individual subsets. The intermediate reconstructed image is used as the input to the next subset reconstruction. One iteration is defined as a single pass through all the subsets [4]. Specifically, the OSEM formula is: x (k,t+1) = x (k,t) ( l S t a l (b l /ˆb l ) l S t a l ),t =1,...,L; =1,...,N. (4)
4 64 X. Li et al. / A numerical simulator in VC++ on PC for iterative image reconstruction Fig. 3. Interface. The OSSART formula is: (b x (k,t+1) = x (k,t) l S t a t ˆb l ) l N + =1 a l M l=1 a,t =1,...,L; =1,...,N. (5) l In summary, the generic iterative reconstruction algorithm is described as follows: 0. Initiation: Make a guess on the image to be reconstructed, set the iteration index k =0; 1. Re-proection: Estimate proection data b i based on the current guess x (k). ˆb =< a i,x (k) >, where a i denotes the ith row of A, ˆb i the ith reproection value in Step k, and <, > the inner product; 2. Comparison: Compare real data with re-proected ones. 3. Backproection: Backproect the comparison results over the image space; 4. Modification: Update the current image by incorporating weighted backproection in a pixelspecific fashion; 5. Evaluation: Evaluate the reconstruction error; if the error is not sufficiently small, set k = k+1, and repeat Steps 1 to 4. Figure 2 shows how to apply ordered-subsets for iterative reconstruction.
5 X. Li et al. / A numerical simulator in VC++ on PC for iterative image reconstruction Software implementation 3.1. System architecture and main features Fig. 4. Snap shot of the Help file. The simulator IterCT is developed using Microsoft R Visual C under Windows R The current version assumes the parallel-beam geometry. Figure 3 gives the screen shot of its interface. IterCT consists of five modules: Image, Proection, Reconstruction, Analysis and Help. The first four simulate the cross section image, proection process, image reconstruction and reconstruction error analysis, respectively. The Help module provides a user manual for the usage of IterCT and the literature of iterative algorithms. Figure 4 provides a screen shot for the help file. IterCT allows users to either create a cross section on the fly or open a previously created cross section file. A slice is obtained by cutting through the 3D Shepp- Logan phantom, as shown in Fig. 5. The phantom parameters are described in Table 1 [5]. The image size is up to 512 by 512. Users also have the option to either generate the proection data or open a saved proection data file. When a proection data set is ready users can choose among five reconstruction algorithms (four iterative algorithms and one filtered backproection). Then users can select part of the reconstructed image and perform error analysis or compare the original slice with its reconstruction. For demonstration users can also perform a fast simulation by clicking the appropriate buttons on the left tool bar of the main interface. Figure 6 shows the high-level system architecture.
6 66 X. Li et al. / A numerical simulator in VC++ on PC for iterative image reconstruction (a) (b) (c) (d) Fig. 5. Slicing a 3D Shepp-Logan Phantom. (a): the interface, (b): z = , (c): z = 0.25, (d): z = Obect-oriented design and data structure Through out the design of IterCT we took an obect-oriented approach to gain the advantages as software reusability and functionality extensibility. Some important classes in IterCT are: Ray, Simulator, Phantom and View. The Ray is defined as a straight line through a field of view. It is evenly divided with the step length equaling to the pixel size. A Ray obect has attributes of ray number and proection number that uniquely define the ray path. Each Ray obect has two operations along the ray path: Re-proect to generate an estimated proection and Backproect to distribute a comparative error to pixels. The Simulator class has four methods simulating four steps of the iterative reconstruction. Inside
7 X. Li et al. / A numerical simulator in VC++ on PC for iterative image reconstruction 67 Image Module To provide cross section images Slicing through a 3D Shepp&Logan phantom Read an image file Image slice Proection Module To provide proection data Generate proection data by ray-tracing through the slice Read proection data file Image slice Proection data Analysis Module To analyze reconstruction Difference image Error in a region of interest Error between original and reconstructed slice Reconstructed slice Reconstruction Module To showcase reconstruction algorithms Four iterative and one filtered backproection algorithms Fig. 6. System Architecture diagram. MainView original_image original_proection MessageHandlers() PhantomView CreatePhantomSlice() OpenPhantomFile() SavePhantomFile() DrawPhantom() ProectionView CreateProection() OpenProectionFile() SaveProectionFile() DrawProection() ReconstructionView(s) Simulation(simulator) DrawSimulation() ray:cray proection number ray number ForwardProect (image) BackProect (image) Fig. 7. Classes Model diagram. simulator:csimulator ForwardProection(image, ray) GetBackProectData() BackProection(image, ray) UpdateImage() the simulator it initiates Re-proect function calls to obtain the proection data set. After comparing with real data, the simulator calls the Backproect functions and updates an intermediate reconstructed image. In this common structure, the details of comparison and updating are specialized according to the EM, OSEM, SART and OSSART formulae, respectively. The Phantom class wraps the phantom definition and slice attributes such as image dimension, size and type. It has a SetParameters function. According to specified image types a Phantom class gives different slices. The View classes are containers and function call initiators. They utilize other classes like Ray and Simulator to calculate the proection and reconstruction, and draw the results. There are five types of View classes: MainView, PhantomView, ProectionView, ReconstructionView and AnalysisView. Figure 7 illustrates the class models. Figure 8 shows their relationship Reproection and backproection IterCT uses ray-tracing and bilinear interpolation to simulate reproection. The value at each dividing point along the ray path is the bilinear interpolation from values of its four nearest neighbors that are on
8 68 X. Li et al. / A numerical simulator in VC++ on PC for iterative image reconstruction User input: Messages Analyze reconstruction Create phantom MainView PhantomView Uses Phantom Open phantom Create proection Open proection ProectionView Reconstruction Ray Data AnalysisView ReconstructionView Uses Simulator Fig. 8. Class Collaboration diagram. Table 1 Reconstruction Time Comparison. Initial guess Tested on a desktop PC computer with two 1.2G Hz AMD Athlon CPU, 1 G memory EM OS-EM SART OS-SART Iteration number 16 2 with 8 subsets 16 2 with 8 subsets Reconstruction time 46 s 16 s 46 s 16 s Iteration number with 8 subsets with 8 subsets Reconstruction time 4 min 52s 1 min 24s 4 min 52s 1 min 24 s the image grid. The ray proection value is the ray integral or the sum of values at all dividing points along the ray path. The data to be backproected is reassigned to each dividing point s four neighbors in the same manner. Sum of all weighting factors along th column ( M i=1 a i) is computed as for backproection while factors along ith row N =1 a i is estimated as for reproection. In both cases we assume a unit image vector for reproection and backproection Size and order of subsets Hudson and Larkin discussed some methods for grouping proection data [4]. In our program, rays are first reshuffled randomly then divided into subsets in sequence. More specifically, after random reshuffling, the ray number and proection number are no longer the original ones associated with ray index. Then we group ray[i], i =1,...,T as the first subset; ray[i], i =(T +1),...,(2T 1) as the second subset; and so on. T is the size of subset which equals to the number of proections divided by number of subsets L Error analysis IterCT enables users to select a certain region in the reconstructed image and calculate the mean square error of reconstruction. Users can compare the reconstruction with the original image by viewing the difference image or the mean square error. Mean square error comparison between the original proection data and the reproected data is also displayed.
9 X. Li et al. / A numerical simulator in VC++ on PC for iterative image reconstruction 69 (a) (b) (c) (d) (e) (f) (g) (h) (i) () Fig. 9. Simulation results. The Gaussian noise is added such that the mean value is proection measurement E; the standard deviation is E/8. (a): Original Image, (b): Filtered Backproection reconstruction, (c): EM reconstruction, 16 iterations, (d): OS-EM reconstruction, 2 iterations 8 subsets, (e): SART reconstruction, 16 iterations, (f): OS-SART reconstruction, 2 iterations 8 subsets, (g): EM reconstruction, 80 iterations, (h): OS-EM reconstruction, 10 iterations 8 subsets, (i): SART reconstruction, 80 iterations, (): OS-SART reconstruction, 10 iterations 8 subsets Simulation results Figure 9 shows the simulation results when Gaussian noise is added to the proection data. Due to its statistical nature, the iterative algorithm gives better results when the data is noisy. Table 1 documents the speed performance of four iterative algorithms. It shows that the OS methods can greatly improve the reconstruction speed.
10 70 X. Li et al. / A numerical simulator in VC++ on PC for iterative image reconstruction 4. Discussions and conclusion With a user-friendly graphical interface and online help, IterCT can be used both as a research prototype and a teaching tool. Its obect oriented design enables easy expansion of ray geometry and reconstruction algorithms. For example, the fan beam geometry can be added to the package by adding a rebin function to the ray class. More sophisticated phantom definitions can be introduced without maor change to other parts of the software. The scan mode can be extended to spiral scanning and cone-beam geometry. Subset effects on the improvement of performance is another interesting topic. In conclusion, we have developed an iterative CT reconstruction simulator IterCT for desktop computers running Windows R 2000 operating system. The software can be downloaded from the web site iterct. The EM, OS-EM, SART and OS-SART algorithms have been implemented along with the filtered backproection reconstruction. It can be used as a teaching tool as well as the test-bed for iterative reconstruction experiments. Acknowledgement We thank Chris Piker for technical support. This work is supported in part by an NIH grant (R01 DC03590). References [1] G. Wang, D.L. Snyder, J.A. O Sulivan and M.W. Vannier, Iterative deblurring for CT metal artifact reduction, IEEE Trans. Med. Imag. 15 (1996), [2] G. Wang, G. Schweiger and M.W. Vannier, An iterative algorithm for x-ray CT fluoroscopy, IEEE Trans. Med. Imag. 17 (1998), [3] L.A. Shepp and Y. Vardi, Maximum Likelihood Reconstruction for Emission Tomography, IEEE Trans. Med. Imag. MI-1 (1982), [4] H.M. Hudson and R.S. Larkin, Accelerated image reconstruction using ordere subsets of proection data, IEEE Trans. Med. Imag. 13 (1994), [5] A.H. Andersen and A.C. Kak, Simultaneous algebraic reconstruction technique (SART): A superior implementation of the ART algorithm, Ultrasonic Imaging 6 (1984), [6] J. Browne and A.R. De Pierro, A row-action alternative to the EM algorithm for maximizing likelihoods in emission tomography, IEEE Trans. Med. Imag. 15 (1996), [7] M. Jiang and G. Wang, On the convergence of SART, Revision: 1.8, May 14, [8] A.H. Andersen, Algebraic reconstruction in CT from limited views, IEEE Trans. Med. Imag. 8 (1989), [9] A.C. Kak and M. Slaney, Principles of computerized tomographic imaging, Siam, ISBN X, pp ; Chapter 7, Algebraic Reconstruction Algorithms, [10] J.D. Enderle, S.M. Blanchard and J.D. Bronzino, Introduction to biomedical engineering, Academic Press, Chapter 14, Radiation Imaging, [11] D.L. Snyder, T.J. Schulz and J.A. O Sullivan, Deblurring subect to nonnegativity constraints, IEEE Trans. Singal Processing 40 (1992), [12] I. Csiszar, Why least squares and maximum entropy? An axiomatic approach to inference for linear inverse problems, Ann. Statistics 19 (1991), [13] M. Jiang and G. Wang, Development of Iterative Algorithms for Image Reconstruction, Submitted to Journal of X-ray S&T: A Special Issue on Clinical Applications of Modern Imaging Technologies. [14] G. Wang et al., Cone-beam reconstruction for Micro-CT, Invited paper at the 1st IEEE International Symposium on Biomedical Engineering, Washington D.C. July, 7 17, [15] M. Jiang and G. Wang, Parallel Block-Iterative Algorithms for Image Reconstruction, ICMS 2002, International Congress of Mathematical Software.
Parallel iterative cone beam CT image reconstruction on a PC cluster
Galley Proof 30/04/2005; 12:41 File: xst127.tex; BOKCTP/ljl p. 1 Journal of X-Ray Science and Technology 13 (2005) 1 10 1 IOS Press Parallel iterative cone beam CT image reconstruction on a PC cluster
More informationA Weighted Least Squares PET Image Reconstruction Method Using Iterative Coordinate Descent Algorithms
A Weighted Least Squares PET Image Reconstruction Method Using Iterative Coordinate Descent Algorithms Hongqing Zhu, Huazhong Shu, Jian Zhou and Limin Luo Department of Biological Science and Medical Engineering,
More informationA Projection Access Scheme for Iterative Reconstruction Based on the Golden Section
A Projection Access Scheme for Iterative Reconstruction Based on the Golden Section Thomas Köhler Philips Research Laboratories Roentgenstrasse - Hamburg Germany Abstract A new access scheme for projections
More informationAlgebraic Iterative Methods for Computed Tomography
Algebraic Iterative Methods for Computed Tomography Per Christian Hansen DTU Compute Department of Applied Mathematics and Computer Science Technical University of Denmark Per Christian Hansen Algebraic
More informationAN ANALYSIS OF ITERATIVE ALGORITHMS FOR IMAGE RECONSTRUCTION FROM SATELLITE EARTH REMOTE SENSING DATA Matthew H Willis Brigham Young University, MERS Laboratory 459 CB, Provo, UT 8462 8-378-4884, FAX:
More informationTomographic Algorithm for Industrial Plasmas
Tomographic Algorithm for Industrial Plasmas More info about this article: http://www.ndt.net/?id=22342 1 Sudhir K. Chaudhary, 1 Kavita Rathore, 2 Sudeep Bhattacharjee, 1 Prabhat Munshi 1 Nuclear Engineering
More informationExpectation Maximization and Total Variation Based Model for Computed Tomography Reconstruction from Undersampled Data
Expectation Maximization and Total Variation Based Model for Computed Tomography Reconstruction from Undersampled Data Ming Yan and Luminita A. Vese Department of Mathematics, University of California,
More informationEM+TV Based Reconstruction for Cone-Beam CT with Reduced Radiation
Appears in ISVC 2011, Part I, LNCS 6938, pp. 1-10, 2011 EM+TV Based Reconstruction for Cone-Beam CT with Reduced Radiation Ming Yan 1, Jianwen Chen 2, Luminita A. Vese 1, John Villasenor 2, Ale Bui 3,
More informationFeldkamp-type image reconstruction from equiangular data
Journal of X-Ray Science and Technology 9 (2001) 113 120 113 IOS Press Feldkamp-type image reconstruction from equiangular data Ben Wang a, Hong Liu b, Shiying Zhao c and Ge Wang d a Department of Elec.
More informationAdaptive algebraic reconstruction technique
Adaptive algebraic reconstruction technique Wenkai Lua) Department of Automation, Key State Lab of Intelligent Technology and System, Tsinghua University, Beijing 10084, People s Republic of China Fang-Fang
More informationGengsheng Lawrence Zeng. Medical Image Reconstruction. A Conceptual Tutorial
Gengsheng Lawrence Zeng Medical Image Reconstruction A Conceptual Tutorial Gengsheng Lawrence Zeng Medical Image Reconstruction A Conceptual Tutorial With 163 Figures Author Prof. Dr. Gengsheng Lawrence
More informationDEVELOPMENT OF CONE BEAM TOMOGRAPHIC RECONSTRUCTION SOFTWARE MODULE
Rajesh et al. : Proceedings of the National Seminar & Exhibition on Non-Destructive Evaluation DEVELOPMENT OF CONE BEAM TOMOGRAPHIC RECONSTRUCTION SOFTWARE MODULE Rajesh V Acharya, Umesh Kumar, Gursharan
More informationTemperature Distribution Measurement Based on ML-EM Method Using Enclosed Acoustic CT System
Sensors & Transducers 2013 by IFSA http://www.sensorsportal.com Temperature Distribution Measurement Based on ML-EM Method Using Enclosed Acoustic CT System Shinji Ohyama, Masato Mukouyama Graduate School
More informationCover Page. The handle holds various files of this Leiden University dissertation
Cover Page The handle http://hdl.handle.net/1887/8289 holds various files of this Leiden University dissertation Author: Plantagie, L. Title: Algebraic filters for filtered backprojection Issue Date: 2017-0-13
More informationUnmatched Projector/Backprojector Pairs in an Iterative Reconstruction Algorithm
548 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 19, NO. 5, MAY 2000 Unmatched Projector/Backprojector Pairs in an Iterative Reconstruction Algorithm Gengsheng L. Zeng*, Member, IEEE, and Grant T. Gullberg,
More informationIndex. aliasing artifacts and noise in CT images, 200 measurement of projection data, nondiffracting
Index Algebraic equations solution by Kaczmarz method, 278 Algebraic reconstruction techniques, 283-84 sequential, 289, 293 simultaneous, 285-92 Algebraic techniques reconstruction algorithms, 275-96 Algorithms
More informationAn object-oriented library for 3D PET reconstruction using parallel computing
An object-oriented library for 3D PET reconstruction using parallel computing Claire Labbé 1, K. Thielemans 2, D. Belluzzo 3, V. Bettinardi 3, M.C. Gilardi 3, D.S. Hague 4, M. Jacobson 5, S. Kaiser 6,
More informationApproximating Algebraic Tomography Methods by Filtered Backprojection: A Local Filter Approach
Fundamenta Informaticae 135 (2014) 1 19 1 DOI 10.3233/FI-2014-1109 IOS Press Approximating Algebraic Tomography Methods by Filtered Backprojection: A Local Filter Approach Linda Plantagie Centrum Wiskunde
More informationAlgebraic Iterative Methods for Computed Tomography
Algebraic Iterative Methods for Computed Tomography Per Christian Hansen DTU Compute Department of Applied Mathematics and Computer Science Technical University of Denmark Per Christian Hansen Algebraic
More informationMedical Image Reconstruction Term II 2012 Topic 6: Tomography
Medical Image Reconstruction Term II 2012 Topic 6: Tomography Professor Yasser Mostafa Kadah Tomography The Greek word tomos means a section, a slice, or a cut. Tomography is the process of imaging a cross
More informationART Exhibit. Per Christian Hansen. Tommy Elfving Touraj Nikazad Hans Henrik B. Sørensen. joint work with, among others
ART Exhibit Per Christian Hansen joint work with, among others Tommy Elfving Touraj Nikazad Hans Henrik B. Sørensen About Me Forward problem Interests: numerical methods for inverse problems and tomography,
More informationSINGLE-PHOTON emission computed tomography
1458 IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 59, NO. 4, AUGUST 2012 SPECT Imaging With Resolution Recovery Andrei V. Bronnikov Abstract Single-photon emission computed tomography (SPECT) is a method
More informationA Fast GPU-Based Approach to Branchless Distance-Driven Projection and Back-Projection in Cone Beam CT
A Fast GPU-Based Approach to Branchless Distance-Driven Projection and Back-Projection in Cone Beam CT Daniel Schlifske ab and Henry Medeiros a a Marquette University, 1250 W Wisconsin Ave, Milwaukee,
More informationTomographic Reconstruction
Tomographic Reconstruction 3D Image Processing Torsten Möller Reading Gonzales + Woods, Chapter 5.11 2 Overview Physics History Reconstruction basic idea Radon transform Fourier-Slice theorem (Parallel-beam)
More informationReduction of Metal Artifacts in Computed Tomographies for the Planning and Simulation of Radiation Therapy
Reduction of Metal Artifacts in Computed Tomographies for the Planning and Simulation of Radiation Therapy T. Rohlfing a, D. Zerfowski b, J. Beier a, P. Wust a, N. Hosten a, R. Felix a a Department of
More informationSplitting-Based Statistical X-Ray CT Image Reconstruction with Blind Gain Correction
Splitting-Based Statistical X-Ray CT Image Reconstruction with Blind Gain Correction Hung Nien and Jeffrey A. Fessler Department of Electrical Engineering and Computer Science University of Michigan, Ann
More informationCentral Slice Theorem
Central Slice Theorem Incident X-rays y f(x,y) R x r x Detected p(, x ) The thick line is described by xcos +ysin =R Properties of Fourier Transform F [ f ( x a)] F [ f ( x)] e j 2 a Spatial Domain Spatial
More informationHigh Resolution Iterative CT Reconstruction using Graphics Hardware
High Resolution Iterative CT Reconstruction using Graphics Hardware Benjamin Keck, Hannes G. Hofmann, Holger Scherl, Markus Kowarschik, and Joachim Hornegger Abstract Particular applications of computed
More informationMulti-slice CT Image Reconstruction Jiang Hsieh, Ph.D.
Multi-slice CT Image Reconstruction Jiang Hsieh, Ph.D. Applied Science Laboratory, GE Healthcare Technologies 1 Image Generation Reconstruction of images from projections. textbook reconstruction advanced
More informationContinuous and Discrete Image Reconstruction
25 th SSIP Summer School on Image Processing 17 July 2017, Novi Sad, Serbia Continuous and Discrete Image Reconstruction Péter Balázs Department of Image Processing and Computer Graphics University of
More informationGPU implementation for rapid iterative image reconstruction algorithm
GPU implementation for rapid iterative image reconstruction algorithm and its applications in nuclear medicine Jakub Pietrzak Krzysztof Kacperski Department of Medical Physics, Maria Skłodowska-Curie Memorial
More informationIterative CT Reconstruction Using Curvelet-Based Regularization
Iterative CT Reconstruction Using Curvelet-Based Regularization Haibo Wu 1,2, Andreas Maier 1, Joachim Hornegger 1,2 1 Pattern Recognition Lab (LME), Department of Computer Science, 2 Graduate School in
More informationFASART: An iterative reconstruction algorithm with inter-iteration adaptive NAD filter
Bio-Medical Materials and Engineering 26 (2015) S1409 S1415 DOI 10.3233/BME-151439 IOS Press S1409 FASART: An iterative reconstruction algorithm with inter-iteration adaptive NAD filter Ziying Zhou a,
More informationSpiral ASSR Std p = 1.0. Spiral EPBP Std. 256 slices (0/300) Kachelrieß et al., Med. Phys. 31(6): , 2004
Spiral ASSR Std p = 1.0 Spiral EPBP Std p = 1.0 Kachelrieß et al., Med. Phys. 31(6): 1623-1641, 2004 256 slices (0/300) Advantages of Cone-Beam Spiral CT Image quality nearly independent of pitch Increase
More informationAxial block coordinate descent (ABCD) algorithm for X-ray CT image reconstruction
Axial block coordinate descent (ABCD) algorithm for X-ray CT image reconstruction Jeffrey A. Fessler and Donghwan Kim EECS Department University of Michigan Fully 3D Image Reconstruction Conference July
More informationConsistency in Tomographic Reconstruction by Iterative Methods
Consistency in Tomographic Reconstruction by Iterative Methods by M. Reha Civanlar and H.J. Trussell Center for Communications and Signal Processing Department of Electrical and Computer Engineering North
More informationAssessment of OSEM & FBP Reconstruction Techniques in Single Photon Emission Computed Tomography Using SPECT Phantom as Applied on Bone Scintigraphy
Assessment of OSEM & FBP Reconstruction Techniques in Single Photon Emission Computed Tomography Using SPECT Phantom as Applied on Bone Scintigraphy Physics Department, Faculty of Applied Science,Umm Al-Qura
More informationComparison of different iterative reconstruction algorithms for X-ray volumetric inspection
Comparison of different iterative reconstruction algorithms for X-ray volumetric inspection More info about this article: http://www.ndt.net/?id=22973 Georgios Liaptsis 1,2, Alan L. Clarke 1 and Perumal
More informationIntroduction to Biomedical Imaging
Alejandro Frangi, PhD Computational Imaging Lab Department of Information & Communication Technology Pompeu Fabra University www.cilab.upf.edu X-ray Projection Imaging Computed Tomography Digital X-ray
More informationTHE FAN-BEAM scan for rapid data acquisition has
190 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 2, FEBRUARY 2007 Hilbert Transform Based FBP Algorithm for Fan-Beam CT Full Partial Scans Jiangsheng You*, Member, IEEE, Gengsheng L. Zeng, Senior
More informationProjection Space Maximum A Posterior Method for Low Photon Counts PET Image Reconstruction
Proection Space Maximum A Posterior Method for Low Photon Counts PET Image Reconstruction Liu Zhen Computer Department / Zhe Jiang Wanli University / Ningbo ABSTRACT In this paper, we proposed a new MAP
More informationAn overview of fast convergent ordered-subsets reconstruction methods for emission tomography based on the incremental EM algorithm
An overview of fast convergent ordered-subsets reconstruction methods for emission tomography based on the incremental EM algorithm Ing-Tsung Hsiao a Parmeshwar Khurd b Anand Rangaraan c and Gene Gindi
More informationReconstruction in CT and relation to other imaging modalities
Reconstruction in CT and relation to other imaging modalities Jørgen Arendt Jensen November 1, 2017 Center for Fast Ultrasound Imaging, Build 349 Department of Electrical Engineering Center for Fast Ultrasound
More informationRECENTLY, biomedical imaging applications of
1190 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 24, NO. 9, SEPTEMBER 2005 A General Exact Reconstruction for Cone-Beam CT via Backprojection-Filtration Yangbo Ye*, Shiying Zhao, Hengyong Yu, Ge Wang, Fellow,
More informationSPECT reconstruction
Regional Training Workshop Advanced Image Processing of SPECT Studies Tygerberg Hospital, 19-23 April 2004 SPECT reconstruction Martin Šámal Charles University Prague, Czech Republic samal@cesnet.cz Tomography
More informationA Fast Implementation of the Incremental Backprojection Algorithms for Parallel Beam Geometries5
A Fast mplementation of the ncremental Backprojection Algorithms for Parallel Beam Geometries5 Chung-Ming Chent, Zang-Hee Cho*, Cheng-Yi Wang t +Center for Biomedical Engineering, College of Medicine,
More informationNONLINEAR BACK PROJECTION FOR TOMOGRAPHIC IMAGE RECONSTRUCTION
NONLINEAR BACK PROJECTION FOR TOMOGRAPHIC IMAGE RECONSTRUCTION Ken Sauef and Charles A. Bournant *Department of Electrical Engineering, University of Notre Dame Notre Dame, IN 46556, (219) 631-6999 tschoo1
More informationReconstruction from Projections
Reconstruction from Projections M.C. Villa Uriol Computational Imaging Lab email: cruz.villa@upf.edu web: http://www.cilab.upf.edu Based on SPECT reconstruction Martin Šámal Charles University Prague,
More informationImplementation and evaluation of a fully 3D OS-MLEM reconstruction algorithm accounting for the PSF of the PET imaging system
Implementation and evaluation of a fully 3D OS-MLEM reconstruction algorithm accounting for the PSF of the PET imaging system 3 rd October 2008 11 th Topical Seminar on Innovative Particle and Radiation
More informationEvaluation of Spectrum Mismatching using Spectrum Binning Approach for Statistical Polychromatic Reconstruction in CT
Evaluation of Spectrum Mismatching using Spectrum Binning Approach for Statistical Polychromatic Reconstruction in CT Qiao Yang 1,4, Meng Wu 2, Andreas Maier 1,3,4, Joachim Hornegger 1,3,4, Rebecca Fahrig
More informationTomographic reconstruction of shock layer flows
Tomographic reconstruction of shock layer flows A thesis submitted for the degree of Doctor of Philosophy at The Australian National University May 2005 Rado Faletič Department of Physics Faculty of Science
More informationAdvanced Image Reconstruction Methods for Photoacoustic Tomography
Advanced Image Reconstruction Methods for Photoacoustic Tomography Mark A. Anastasio, Kun Wang, and Robert Schoonover Department of Biomedical Engineering Washington University in St. Louis 1 Outline Photoacoustic/thermoacoustic
More informationAcknowledgments and financial disclosure
AAPM 2012 Annual Meeting Digital breast tomosynthesis: basic understanding of physics principles James T. Dobbins III, Ph.D., FAAPM Director, Medical Physics Graduate Program Ravin Advanced Imaging Laboratories
More informationIterative Optimization in Medical Imaging
Iterative Optimization in Medical Imaging Charles Byrne (Charles Byrne@uml.edu) http://faculty.uml.edu/cbyrne/cbyrne.html Department of Mathematical Sciences University of Massachusetts Lowell Lowell,
More informationInterior Reconstruction Using the Truncated Hilbert Transform via Singular Value Decomposition
Interior Reconstruction Using the Truncated Hilbert Transform via Singular Value Decomposition Hengyong Yu 1, Yangbo Ye 2 and Ge Wang 1 1 CT Laboratory, Biomedical Imaging Division, VT-WFU School of Biomedical
More informationREAL-TIME and high-quality reconstruction of cone-beam
Real-Time 3D Cone Beam Reconstruction Dzmitry Stsepankou, Klaus Kornmesser, Jürgen Hesser, Reinhard Männer Abstract The paper presents a comparison of filtered backprojection and iterative approaches (modified
More informationAdapted acquisition trajectory and iterative reconstruction for few-views CT inspection
Adapted acquisition trajectory and iterative reconstruction for few-views CT inspection Caroline Vienne 1, Marius Costin 1 More info about this article: http://www.ndt.net/?id=21917 1 CEA, LIST, Département
More informationFace Hallucination Based on Eigentransformation Learning
Advanced Science and Technology etters, pp.32-37 http://dx.doi.org/10.14257/astl.2016. Face allucination Based on Eigentransformation earning Guohua Zou School of software, East China University of Technology,
More informationImplementation of a backprojection algorithm on CELL
Implementation of a backprojection algorithm on CELL Mario Koerner March 17, 2006 1 Introduction X-ray imaging is one of the most important imaging technologies in medical applications. It allows to look
More informationComputed Tomography. Principles, Design, Artifacts, and Recent Advances. Jiang Hsieh THIRD EDITION. SPIE PRESS Bellingham, Washington USA
Computed Tomography Principles, Design, Artifacts, and Recent Advances THIRD EDITION Jiang Hsieh SPIE PRESS Bellingham, Washington USA Table of Contents Preface Nomenclature and Abbreviations xi xv 1 Introduction
More informationReconstruction in CT and relation to other imaging modalities
Reconstruction in CT and relation to other imaging modalities Jørgen Arendt Jensen November 16, 2015 Center for Fast Ultrasound Imaging, Build 349 Department of Electrical Engineering Center for Fast Ultrasound
More informationLimited view X-ray CT for dimensional analysis
Limited view X-ray CT for dimensional analysis G. A. JONES ( GLENN.JONES@IMPERIAL.AC.UK ) P. HUTHWAITE ( P.HUTHWAITE@IMPERIAL.AC.UK ) NON-DESTRUCTIVE EVALUATION GROUP 1 Outline of talk Industrial X-ray
More informationNoise weighting with an exponent for transmission CT
doi:10.1088/2057-1976/2/4/045004 RECEIVED 13 January 2016 REVISED 4 June 2016 ACCEPTED FOR PUBLICATION 21 June 2016 PUBLISHED 27 July 2016 PAPER Noise weighting with an exponent for transmission CT Gengsheng
More informationTop-level Design and Pilot Analysis of Low-end CT Scanners Based on Linear Scanning for Developing Countries
Top-level Design and Pilot Analysis of Low-end CT Scanners Based on Linear Scanning for Developing Countries Fenglin Liu 1,3, Hengyong Yu 2, Wenxiang Cong 3, Ge Wang 3,* 5 1 1 Engineering Research Center
More information2D Fan Beam Reconstruction 3D Cone Beam Reconstruction
2D Fan Beam Reconstruction 3D Cone Beam Reconstruction Mario Koerner March 17, 2006 1 2D Fan Beam Reconstruction Two-dimensional objects can be reconstructed from projections that were acquired using parallel
More informationNon-Stationary CT Image Noise Spectrum Analysis
Non-Stationary CT Image Noise Spectrum Analysis Michael Balda, Björn J. Heismann,, Joachim Hornegger Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen Siemens Healthcare, Erlangen michael.balda@informatik.uni-erlangen.de
More informationIterative and analytical reconstruction algorithms for varying-focal-length cone-beam
Home Search Collections Journals About Contact us My IOPscience Iterative and analytical reconstruction algorithms for varying-focal-length cone-beam projections This content has been downloaded from IOPscience.
More informationWorkshop on Quantitative SPECT and PET Brain Studies January, 2013 PUCRS, Porto Alegre, Brasil Corrections in SPECT and PET
Workshop on Quantitative SPECT and PET Brain Studies 14-16 January, 2013 PUCRS, Porto Alegre, Brasil Corrections in SPECT and PET Físico João Alfredo Borges, Me. Corrections in SPECT and PET SPECT and
More informationNIH Public Access Author Manuscript Med Phys. Author manuscript; available in PMC 2009 March 13.
NIH Public Access Author Manuscript Published in final edited form as: Med Phys. 2008 February ; 35(2): 660 663. Prior image constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic
More informationSome Advanced Topics in Linear Programming
Some Advanced Topics in Linear Programming Matthew J. Saltzman July 2, 995 Connections with Algebra and Geometry In this section, we will explore how some of the ideas in linear programming, duality theory,
More informationAttenuation map reconstruction from TOF PET data
Attenuation map reconstruction from TOF PET data Qingsong Yang, Wenxiang Cong, Ge Wang* Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 80, USA *Ge Wang (ge-wang@ieee.org)
More informationParallelism of iterative CT reconstruction based on local reconstruction algorithm
J Supercomput (2009) 48: 1 14 DOI 10.1007/s11227-008-0198-9 Parallelism of iterative CT reconstruction based on local reconstruction algorithm Junjun Deng Hengyong Yu Jun Ni Lihe Wang Ge Wang Published
More information3/27/2012 WHY SPECT / CT? SPECT / CT Basic Principles. Advantages of SPECT. Advantages of CT. Dr John C. Dickson, Principal Physicist UCLH
3/27/212 Advantages of SPECT SPECT / CT Basic Principles Dr John C. Dickson, Principal Physicist UCLH Institute of Nuclear Medicine, University College London Hospitals and University College London john.dickson@uclh.nhs.uk
More informationSimulation of Multipoint Ultrasonic Flowmeter
Simulation of Multipoint Ultrasonic Flowmeter Jakub Filipský 1,*, Jiří Nožička 2 1,2 CTU in Prague, Faculty of Mechanical Engineering, Department of Fluid Mechanics and Thermodynamics, Technická 4, 166
More informationOptimal threshold selection for tomogram segmentation by reprojection of the reconstructed image
Optimal threshold selection for tomogram segmentation by reprojection of the reconstructed image K.J. Batenburg 1 and J. Sijbers 1 University of Antwerp, Vision Lab, Universiteitsplein 1, B-2610 Wilrijk,
More informationMaterial for Chapter 6: Basic Principles of Tomography M I A Integral Equations in Visual Computing Material
Material for Chapter : Integral Equations in Visual Computing Material Basic Principles of Tomography c 00 Bernhard Burgeth 0 Source: Images Figure : Radon Transform: ttenuation http://en.wikimedia.org/wiki/image:radon_transform.png
More informationTHE methods of maximum likelihood (ML) and maximum
IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 55, NO. 3, JUNE 2008 953 On Iterative Bayes Algorithms for Emission Tomography Jun Ma Abstract In this paper we formulate a new approach to medical image reconstruction
More informationQ.Clear. Steve Ross, Ph.D.
Steve Ross, Ph.D. Accurate quantitation (SUV - Standardized Uptake Value) is becoming more important as clinicians seek to utilize PET imaging for more than just diagnosing and staging disease, but also
More informationx' = c 1 x + c 2 y + c 3 xy + c 4 y' = c 5 x + c 6 y + c 7 xy + c 8
1. Explain about gray level interpolation. The distortion correction equations yield non integer values for x' and y'. Because the distorted image g is digital, its pixel values are defined only at integer
More informationAdaptive region of interest method for analytical micro-ct reconstruction
Journal of X-Ray Science and Technology 19 (2011) 23 33 23 DOI 10.3233/XST-2010-0274 IOS Press Adaptive region of interest method for analytical micro-ct reconstruction Wanneng Yang, Xiaochun Xu, Kun Bi,
More informationFAST MULTIRESOLUTION PHOTON-LIMITED IMAGE RECONSTRUCTION
FAST MULTIRESOLUTION PHOTON-LIMITED IMAGE RECONSTRUCTION Rebecca Willett and Robert Nowak March 18, 2004 Abstract The techniques described in this paper allow multiscale photon-limited image reconstruction
More informationAn Accelerated Convergent Ordered Subsets Algorithm for Emission Tomography
submitted to Physics in Medicine and Biology An Accelerated Convergent Ordered Subsets Algorithm for Emission Tomography Ing-Tsung Hsiao, Anand Rangaraan, Parmeshwar Khurd, Gene Gindi School of Medical
More informationImprovement of Efficiency and Flexibility in Multi-slice Helical CT
J. Shanghai Jiaotong Univ. (Sci.), 2008, 13(4): 408 412 DOI: 10.1007/s12204-008-0408-x Improvement of Efficiency and Flexibility in Multi-slice Helical CT SUN Wen-wu 1 ( ), CHEN Si-ping 2 ( ), ZHUANG Tian-ge
More informationImage Acquisition Systems
Image Acquisition Systems Goals and Terminology Conventional Radiography Axial Tomography Computer Axial Tomography (CAT) Magnetic Resonance Imaging (MRI) PET, SPECT Ultrasound Microscopy Imaging ITCS
More informationAPPLICATION OF RADON TRANSFORM IN CT IMAGE MATCHING Yufang Cai, Kuan Shen, Jue Wang ICT Research Center of Chongqing University, Chongqing, P.R.
APPLICATION OF RADON TRANSFORM IN CT IMAGE MATCHING Yufang Cai, Kuan Shen, Jue Wang ICT Research Center of Chongqing University, Chongqing, P.R.China Abstract: When Industrial Computerized Tomography (CT)
More informationOn the Efficiency of Iterative Ordered Subset Reconstruction Algorithms for Acceleration on GPUs
On the Efficiency of Iterative Ordered Subset Reconstruction Algorithms for Acceleration on GPUs Fang Xu 1, Klaus Mueller 1, Mel Jones 2, Bettina Keszthelyi 2, John Sedat 2, David Agard 2 1 Center for
More informationModern CT system generations Measurement of attenuation
CT reconstruction repetition & hints Reconstruction in CT and hints to the assignments Jørgen Arendt Jensen October 4, 16 Center for Fast Ultrasound Imaging, Build 349 Department of Electrical Engineering
More informationReconstruction in CT and hints to the assignments
Reconstruction in CT and hints to the assignments Jørgen Arendt Jensen October 24, 2016 Center for Fast Ultrasound Imaging, Build 349 Department of Electrical Engineering Center for Fast Ultrasound Imaging
More informationAn approximate cone beam reconstruction algorithm for gantry-tilted CT
An approximate cone beam reconstruction algorithm for gantry-tilted CT Ming Yan a, Cishen Zhang ab, Hongzhu Liang a a School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore;
More informationBlob-Representation of Multidimensional Objects and Surfaces
Blob-Representation of Multidimensional Objects and Surfaces Edgar Garduño and Gabor T. Herman Department of Computer Science The Graduate Center City University of New York Presentation Outline Reconstruction
More informationConstructing System Matrices for SPECT Simulations and Reconstructions
Constructing System Matrices for SPECT Simulations and Reconstructions Nirantha Balagopal April 28th, 2017 M.S. Report The University of Arizona College of Optical Sciences 1 Acknowledgement I would like
More informationDUE to beam polychromacity in CT and the energy dependence
1 Empirical Water Precorrection for Cone-Beam Computed Tomography Katia Sourbelle, Marc Kachelrieß, Member, IEEE, and Willi A. Kalender Abstract We propose an algorithm to correct for the cupping artifact
More informationRecognition and Measurement of Small Defects in ICT Testing
19 th World Conference on Non-Destructive Testing 2016 Recognition and Measurement of Small Defects in ICT Testing Guo ZHIMIN, Ni PEIJUN, Zhang WEIGUO, Qi ZICHENG Inner Mongolia Metallic Materials Research
More informationADVANCED IMAGE PROCESSING METHODS FOR ULTRASONIC NDE RESEARCH C. H. Chen, University of Massachusetts Dartmouth, N.
ADVANCED IMAGE PROCESSING METHODS FOR ULTRASONIC NDE RESEARCH C. H. Chen, University of Massachusetts Dartmouth, N. Dartmouth, MA USA Abstract: The significant progress in ultrasonic NDE systems has now
More informationReview of PET Physics. Timothy Turkington, Ph.D. Radiology and Medical Physics Duke University Durham, North Carolina, USA
Review of PET Physics Timothy Turkington, Ph.D. Radiology and Medical Physics Duke University Durham, North Carolina, USA Chart of Nuclides Z (protons) N (number of neutrons) Nuclear Data Evaluation Lab.
More informationSimplified statistical image reconstruction algorithm for polyenergetic X-ray CT. y i Poisson I i (E)e R } where, b i
implified statistical image reconstruction algorithm for polyenergetic X-ray C omesh rivastava, tudent member, IEEE, and Jeffrey A. Fessler, enior member, IEEE Abstract In X-ray computed tomography (C),
More informationOptimization of Cone Beam CT Reconstruction Algorithm Based on CUDA
Sensors & Transducers 2013 by IFSA http://www.sensorsportal.com Optimization of Cone Beam CT Reconstruction Algorithm Based on CUDA 1 Wang LI-Fang, 2 Zhang Shu-Hai 1 School of Electronics and Computer
More informationThe Design and Implementation of COSEM, an Iterative Algorithm for Fully 3-D Listmode Data
IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 20, NO. 7, JULY 2001 633 The Design and Implementation of COSEM, an Iterative Algorithm for Fully 3-D Listmode Data Ron Levkovitz, Dmitry Falikman*, Michael Zibulevsky,
More informationSpiral CT. Protocol Optimization & Quality Assurance. Ge Wang, Ph.D. Department of Radiology University of Iowa Iowa City, Iowa 52242, USA
Spiral CT Protocol Optimization & Quality Assurance Ge Wang, Ph.D. Department of Radiology University of Iowa Iowa City, Iowa 52242, USA Spiral CT Protocol Optimization & Quality Assurance Protocol optimization
More informationRapid Emission Tomography Reconstruction
Volume Graphics (2003) I. Fujishiro, K. Mueller, A. Kaufman (Editors) Rapid Emission Tomography Reconstruction Ken Chidlow Torsten Möller Department of Computer Science, Simon Fraser University, Burnaby,
More information