Generalized Filtered Backprojection for Digital Breast Tomosynthesis Reconstruction
|
|
- Lesley Todd
- 6 years ago
- Views:
Transcription
1 Generalized Filtered Backprojection for Digital Breast Tomosynthesis Reconstruction Klaus Erhard a, Michael Grass a, Sebastian Hitziger b, Armin Iske b and Tim Nielsen a a Philips Research Europe Hamburg, Germany; b University of Hamburg, Germany ABSTRACT Filtered backprojection (FBP) has been commonly used as an efficient and robust reconstruction technique in tomographic X-ray imaging during the last decades. For standard geometries like circle or helix it is known how to efficiently filter the data. However, for geometries with only few projection views or with a limited angular range, the application of FBP algorithms generally provides poor results. In digital breast tomosynthesis (DBT) these limitations give rise to image artifacts due to the limited angular range and the coarse angular sampling. In this work, a generalized FBP algorithm is presented, which uses the filtered projection data of all acquired views for backprojection along one direction. The proposed method yields a computationally efficient generalized FBP algorithm for DBT, which provides similar image quality as iterative reconstruction techniques while preserving the ability for region of interest reconstructions. To demonstrate the excellent performance of this method, examples are given with a simulated breast phantom and the hardware BR3D phantom. Keywords: Digital Breast Tomosynthesis, FBP, ART, generalized FBP 1. INTRODUCTION Filtered backprojection (FBP) has been commonly used as an efficient and robust reconstruction technique in tomographic X-ray imaging during the last decades. One reason for its efficiency is that it allows for region of interest (ROI) reconstructions and only requires one backprojection per view, which can be parallelized easily. For limited angle tomography acquisitions such as digital breast tomosynthesis (DBT), however, standard FBP reconstruction algorithms provide poor results and give rise to image artifacts due to the limited angular range and the coarse angular sampling. Several modification of the ramp filter have been proposed 1 3 to improve the image quality of FBP applied to limited angular range tomography, but these filter modifications are controlled with free parameters, which have to be optimized and adapted to the particular acquisition geometry. Iterative reconstruction algorithms are often used in DBT since they potentially yield a reconstructed image that is in better accordance with the measured data. Compared to FBP, iterative algorithms are more stable against noise and can handle arbitrary geometries. However, the improvement in image quality and flexibility has to be paid with computational complexity and typically longer computation times. In this work, a generalized FBP algorithm is presented, which uses the filtered projection data of all acquired views for backprojection along one direction in order to compute a high quality image similar to iterative techniques. The proposed method requires the computation of geometry-dependent filter kernels but can still be formulated as a generalized filtered backprojection scheme. Within this generalized FBP formulation, the filtering step incorporates computations across multiple views while only one backprojection per view-angle is needed. Therefore, parallelization of the backprojection step is similar to the standard FBP algorithm. Hence, the proposed method yields an efficient reconstruction algorithm with an accuracy comparable to iterative techniques, which will be demonstrated on both simulated breast tomosynthesis data and measurements of the BR3D phantom on an experimental X-ray system. Further author information: (Send correspondence to K.E.) K.E.: klaus.erhard@philips.com, Telephone:
2 2. METHODS While FBP methods provide fast and accurate reconstruction algorithms for tomographic X-Ray imaging whenever the source trajectory is complete and sufficiently many projections are acquired, 4 they perform worse on limited angle tomographic data where both the trajectory is incomplete and the sampling is coarse. In digital breast tomosynthesis, for example, typical examination protocols acquire only X-ray projections over a limited angular range of Here, a common FBP image reconstruction suffers from severe artefacts such as the loss of the low frequency information value and edge sharpening along the source trajectory. 6, 7 These artefacts can be weakened with the use of iterative reconstruction techniques since these kind of algorithms successively update the reconstructed image in order to reduce the mismatch between measured projection data and the re-projections generated from the current image. 2.1 Motivation of the filter design A similar reconstruction quality as for example with the algebraic reconstruction technique (ART) cannot be achieved with a view-by-view filtering of the projection data in standard FBP reconstruction since the update step of ART implicitly involves information from all projection angles prior to backprojecting from one single view. To mimic this feature of iterative reconstruction algorithms, cross-view projection filtering has to be enabled within the FBP framework. Therefore, a more general filtered backprojection is introduced by x = BF y, (1) with image x, measured projection data filter matrix and the backprojection operator y = (y 1,..., y N ) T, (2) F F 1N F =..... (3) F N1... F NN B = 1 N (B 1,..., B N ). (4) Here, y i denotes the measured data for the source position i, i.e. P x = y with the projection operator P = (P 1,..., P N ) T, (5) defined via the projection operators P i for each view position i. Note, that in contrast to standard FBP algorithms, the off-diagonal filter matrices F ij, i j, acting on the measured data y j and contributing to the filtered view i prior to backprojecting from source position i, are non-zero for the proposed generalized FBP method. Computation of an image x that is consistent with the measured data can be achieved with the knowledge of the generalized inverse P + of the projection operator P via x = P + y. (6) However, direct computation of the generalized inverse is not feasible due to the complexity of P and therefore iterative methods are commonly applied for solving Eq. (6). Comparing Eq. (1) with the following observation P + = B(P B) + shows that filtering with F = (P B) + (7) in the generalized FBP formula (1) yields the same result x as applying the generalized inverse P + to the measured data y.
3 2.2 Computation of filter kernels The computation of the filter kernels for the generalized filtered backprojection algorithm given by Eq. (1) and Eq. (7) will be demonstrated in the following for a DBT geometry. The acquisition geometry is defined by a stationary detector and an X-ray source moving on a straight line at fixed height above the detector parallel to its columns, see Fig. 1 (a). Therefore, each detector column consisting of M detector elements can be processed separately. However, the computation of the filter F for one particular column still requires the evaluation of the generalized inverse (P B) + of the matrix P B = P 1 B 1... P 1 B N..... P N B 1... P N B N which consists of N N blocks of matrices of the size M M., (8) To analyze the combined operation P i B j, explicit definitions of the projection and backprojection operators (assuming an infinite continuous detector for analytical purposes) are given by D (P i x)(ξ) = 1 ( x ξ + η ) D 0 H (ξ i ξ), η dη, (9) ( ) Hξ ηξi (B i y)(ξ, η) = y. (10) H η Here, ξ is the spatial coordinate along the trajectory direction and η is perpendicular to it (0 η D), ξ i is the ξ-component of the source position for view i. Furthermore, H denotes the constant source to image distance (SID) and D is the compression thickness, see Fig. 1 (a). The value of P i x at the detector position ξ is the average absorption coefficient on a line connecting ξ with the X-ray source position ξ i. The backprojection is obtained by distributing a projection value along the line which connects the corresponding detector coordinate with the X-ray source. The combined operation P i B j can be derived from the definitions (9) and (10). For i = j it is the identity operation: P i B i y = y. For i j is given by: P i B j y(ξ) = H D D ji H D 0 ji y(ξ z) dz, (11) ( ji + z) 2 where ji = ξ j ξ i is the distance between source positions for views i and j. From Eq. (11), it is clear that P i B j y is a convolution of y with a shift invariant kernel which we will abbreviate with ψ ij. It also follows from Eq. (11) that the kernels for P i B j and P j B i are symmetrical, i.e. ψ ij (ξ) = ψ ji ( ξ) for i j. For typical values of M = 2048 and N = 17 in digital breast tomosynthesis the numerical complexity of a direct calculation of (P B) + is O(N 3 M 3 ) and hence too large for practical purposes. To overcome this problem, the operator (P B) acting on the projection data is analyzed in the Fourier domain. Each block matrix P i B j describes a convolution on the projection data with a shift-invariant kernel of limited width depending on the angle between the source positions i and j. Hence, the block matrices P i B j can be described as a diagonal matrix in the frequency domain with diagonal elements given by the frequency components of the Fourier transformed convolution operator P i B j, i.e. ( ) (1) (M) P i B j = diag P i B j,..., Pi B j, (12) and P B can be reordered w.r.t. the frequency components k = 1,..., M, yielding a block-diagonal shape of M blocks of size N N each: ( P B = diag P B (1),..., P B (M)), (13)
4 (a) (b) (c) Figure 1. (a) Acquisition geometry of the simulated DBT system. (b) Circular arc geometry of the experimental X-ray setup. The mapping from the radial source positions onto the virtual line introduces an approximation error of the cone-beam geometry indicated with the dashed and solid fans. (c) Axial slice through simulated breast phantom with anatomical background noise and ellipsoidal lesion. with P B = P 1 B 1. P N B 1... P1 B N PN B N. (14) Now, the calculation of the filter ˆF ) + = ( P B can be performed separately for each frequency component k = 1,..., M, which reduces the complexity from O(N 3 M 3 ) to O(MN 3 ) and enables the practical implementation of the proposed method. The matrices ) ( P B can be calculated analytically for the given acquisition geometry from the Fourier transform ˆψ ij of ψ ij. 2.3 Phantoms For the simulation study a series of breast phantoms have been generated from dynamic contrast-enhanced MRI images, acquired on a Philips MR Intera Achieva 1.5 T scanner at the University of Chicago Hospitals, by segmentation into three tissue compartments representing adipose, glandular and skin tissue and application of a compression model as previously described in. 8 To further refine these software breast phantoms, anatomic noise background structure has been added with a random signal following a power law behavior. To this end, a random white noise signal has been Fourier transformed and multiplied with a power-law function H(f) = B 0.5 /f 1.5 in frequency domain such that the resulting structure noise 9 admits the power spectrum P (f) = B/f 3. An exemplary breast phantom with an additional ellipsoidal lesion and a resolution of 200 µm in each direction is depicted in Fig. 1 (c). Furthermore, the method has been demonstrated using the BR3D phantom (CIRS Inc., Norfolk, VA) as depicted in Fig. 2 (a). The phantom of size 100 mm 180 mm 10 mm comprises five background slabs of 1 cm height, which consist of a mixture of adipose and glandular tissue equivalent material. An additional target slab containing an assortment of micro-calcifications, fibrils and masses has been positioned between the second and the third background slab. The semicircular shaped slabs provide a heterogeneous background pattern and obscure the embedded detail structures on the projection data, see Fig. 2 (b).
5 (a) (b) (c) (d) (e) Figure 2. (a) BR3D hardware phantom comprising five background slabs and one target slab positioned between background slab two and three. (b) Central projection through all slabs of the BR3D phantom. (c) - (e) Reconstruction of one slice through a background slab within the BR3D phantom using FBP algorithm with a ramp filter (c), ART with 10 iterations (d), and the proposed generalized FBP algorithm (e), respectively. (a) (b) (c) (d) Figure 3. Axial slice reconstructions with (a) standard FBP, (b) proposed generalized FBP and (c) ART with 10 iterations. (d) shows profiles of the reconstructed attenuation coefficient along the line indicated in (a) - (c). 3. RESULTS Simulated projection data have been generated from breast phantoms for a DBT system with a stationary detector with 100 µm pixel pitch. The tube is moving parallel to the detector columns at constant height of SID = 665 mm above the detector sampling N = 17 projections at equidistant angles between ±16 measured against the detector normal, see Fig. 1 (a). Fig. 3 (a) - (c) show a comparison of an axial slice through the reconstructed volumes for FBP with a ramp filter, the proposed generalized FBP, and ART with 10 iterations. Fig. 3 (d) shows a profile of the attenuation values along the line indicated in the images. Obviously, the FBP algorithm with a standard ramp filter yields a loss of the low spatial frequency information and even negative values occur in the reconstructed attenuation coefficients. On the other hand, the generalized FBP and the iteratively computed ART reconstruct an attenuation function that better fits to the measured data. Both methods exhibit a very similar appearance and the line profiles shown in Fig. 3 (d) almost coincide. For an experimental validation of these simulated results, a BR3D phantom with five background slabs and one target slab has been examined on a laboratory setup (Philips Healthcare, Best, The Netherlands). This system enables a rotational movement of the X-ray tube around a pivot point, which is located 80 mm above a stationary X-ray detector with 50 µm pixel size. The source to iso-center distance was 590 mm and 25 projections have been acquired along a circular arc of 48 at equidistant angular positions with a total mean glandular dose of 2.1 mgy. The deviation of the laboratory geometry from the linear geometry depicted in Fig. 1 (a) results in convolution kernels, which are not shift-invariant. Nevertheless, the generalized FBP algorithm can be applied approximately in this situation by mapping the radial source positions onto a virtual line with constant distance from the stationary detector. This approximation results in a geometric mismatch of the true and virtual geometry as illustrated in Fig. 1 (b).
6 (a) (b) (c) (d) Figure 4. Region of interest through the target slab of the BR3D phantom reconstructed with (a) standard FBP, (b) proposed generalized FBP and (c) ART with 10 iterations. (d) shows profiles of the reconstructed attenuation coefficient along the line indicated in (a) - (c).
7 Fig. 4 compares the reconstruction of the target slab using the ramp-filtered backprojection with the proposed method and an ART reconstruction after 10 iterations. The FBP algorithm reconstructs the attenuation values up to a constant bias, see Fig. 4 (d), hence the level and window settings have been adapted for a harmonized presentation of each reconstruction. Both the ART and the generalized FBP are more stable against noise, which is demonstrated in Fig. 4 (a) - (c). The proposed generalized FBP method provides a high image quality comparable to the ART reconstruction. A shift in the reconstructed attenuation values could be observed with the generalized FBP method, which is contributed to an approximation in the filter kernel computation due to the circular arc geometry. 4. DISCUSSION The proposed method yields a computationally efficient generalized FBP algorithm for digital breast tomosynthesis, which provides similar image quality as iterative reconstruction techniques while preserving the ability for region of interest reconstructions. Both a small number of views and a limited angular range can be handled by the generalized FBP with a decent image quality in contrast to the standard FBP reconstruction, which yields a severe loss of the low frequency information of the object. Moreover, due to the filter computation as the pseudo-inverse operator, the reconstructed image provides an optimal solution in the least-squares sense, which minimizes the error between the measured data and the re-projections of the reconstructed image. The computational performance of the generalized FBP is comparable to standard FBP algorithms. The filtering step requires additional cross-view computations and therefore the backprojection step can only be started after the data acquisition has been completed. The presented comparison of the proposed method with FBP and ART both in a simulation study and with measurements on a laboratory X-ray system demonstrate a comparable image quality and robustness against noise in the projection data as standard iterative reconstruction techniques such as ART. ACKNOWLEDGMENTS The authors would like to thank Lex Alving and Robert Hofsink, Philips Healthcare, Best, The Netherlands, for performing the tomosynthesis acquisition of the BR3D phantom on an experimental X-ray system. REFERENCES [1] Lauritsch, G. and Haerer, W. H., Theoretical framework for filtered back projection in tomosynthesis, Medical Imaging 1998: Image Processing 3338(1), , SPIE (1998). [2] Zhao, B. and Zhao, W., Three-dimensional linear system analysis for breast tomosynthesis., Med Phys 35, (Dec 2008). [3] Ludwig, J., Mertelmeier, T., Kunze, H., and Härer, W., A novel approach for filtered backprojection in tomosynthesis based on filter kernels determined by iterative reconstruction techniques, in [IWDM 08: Proceedings of the 9th international workshop on Digital Mammography], , Springer-Verlag, Berlin,Heidelberg (2008). [4] Kak, A. C. and Slaney, M., [Principles of Computerized Tomographic Imaging], IEEE Press, New York (1987). [5] Dobbins, J. T. and Godfrey, D. J., Digital x-ray tomosynthesis: current state of the art and clinical potential., Phys Med Biol 48, R (Oct 2003). [6] Hamaker, C., Smith, K. T., and Wagner, D. C. S. S. L., The divergent beam x-ray transform, Rocky Mountain Journal of Mathematics 6, (1980). [7] Louis, A. K. and Rieder, A., Incomplete data problems in x-ray computerized tomography, 56, (1989). [8] Erhard, K., Grass, M., and Nielsen, T., A second pass correction method for calcification artifacts in digital breast tomosynthesis, Medical Imaging 2011: Physics of Medical Imaging 7961(1), , SPIE (2011). [9] Burgess, A. E., Jacobson, F. L., and Judy, P. F., Human observer detection experiments with mammograms and power-law noise., Med Phys 28, (Apr 2001).
DEVELOPMENT 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 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 informationSimulation of Mammograms & Tomosynthesis imaging with Cone Beam Breast CT images
Simulation of Mammograms & Tomosynthesis imaging with Cone Beam Breast CT images Tao Han, Chris C. Shaw, Lingyun Chen, Chao-jen Lai, Xinming Liu, Tianpeng Wang Digital Imaging Research Laboratory (DIRL),
More informationBackground 8/2/2011. Development of Breast Models for Use in Simulation of Breast Tomosynthesis and CT Breast Imaging. Stephen J.
Development of Breast Models for Use in Simulation of Breast Tomosynthesis and CT Breast Imaging Stephen J. Glick* J. Michael O Connor**, Clay Didier**, Mini Das*, * University of Massachusetts Medical
More informationTranslational Computed Tomography: A New Data Acquisition Scheme
2nd International Symposium on NDT in Aerospace 2010 - We.1.A.3 Translational Computed Tomography: A New Data Acquisition Scheme Theobald FUCHS 1, Tobias SCHÖN 2, Randolf HANKE 3 1 Fraunhofer Development
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 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 informationNoise power spectrum and modulation transfer function analysis of breast tomosynthesis imaging
Noise power spectrum and modulation transfer function analysis of breast tomosynthesis imaging Weihua Zhou a, Linlin Cong b, Xin Qian c, Yueh Z. Lee d, Jianping Lu c,e, Otto Zhou c,e, *Ying Chen a,b a
More informationIntroduction to Medical Imaging. Cone-Beam CT. Klaus Mueller. Computer Science Department Stony Brook University
Introduction to Medical Imaging Cone-Beam CT Klaus Mueller Computer Science Department Stony Brook University Introduction Available cone-beam reconstruction methods: exact approximate algebraic Our discussion:
More informationInvestigating Oblique Reconstructions with Super-Resolution in Digital Breast Tomosynthesis
Investigating Oblique Reconstructions with Super-Resolution in Digital Breast Tomosynthesis Raymond J. Acciavatti, Stewart B. Mein, and Andrew D.A. Maidment University of Pennsylvania, Department of Radiology,
More informationAssessment of 3D performance metrics. X-ray based Volumetric imaging systems: Fourier-based imaging metrics. The MTF in CT
Assessment of 3D performance metrics D and 3D Metrics of Performance Towards Quality Index: Volumetric imaging systems X-ray based Volumetric imaging systems: CBCT/CT Tomosynthesis Samuel Richard and Ehsan
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 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 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 informationA comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis
A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis Yiheng Zhang, a Heang-Ping Chan, Berkman Sahiner, Jun Wei, Mitchell M. Goodsitt, Lubomir M. Hadjiiski, Jun
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 informationTowards full-body X-ray images
Towards full-body X-ray images Christoph Luckner 1,2, Thomas Mertelmeier 2, Andreas Maier 1, Ludwig Ritschl 2 1 Pattern Recognition Lab, FAU Erlangen-Nuernberg 2 Siemens Healthcare GmbH, Forchheim christoph.luckner@fau.de
More informationPower Spectrum Analysis of an Anthropomorphic Breast Phantom Compared to Patient Data in 2D Digital Mammography and Breast Tomosynthesis
Power Spectrum Analysis of an Anthropomorphic Breast Phantom Compared to Patient Data in 2D Digital Mammography and Breast Tomosynthesis Lesley Cockmartin 1,*, Predrag R. Bakic 2, Hilde Bosmans 1, Andrew
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 informationEnhancement Image Quality of CT Using Single Slice Spiral Technique
Enhancement Image Quality of CT Using Single Slice Spiral Technique Doaa. N. Al Sheack 1 and Dr.Mohammed H. Ali Al Hayani 2 1 2 Electronic and Communications Engineering Department College of Engineering,
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 informationScaling Calibration in the ATRACT Algorithm
Scaling Calibration in the ATRACT Algorithm Yan Xia 1, Andreas Maier 1, Frank Dennerlein 2, Hannes G. Hofmann 1, Joachim Hornegger 1,3 1 Pattern Recognition Lab (LME), Friedrich-Alexander-University Erlangen-Nuremberg,
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 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 informationDiscrete Estimation of Data Completeness for 3D Scan Trajectories with Detector Offset
Discrete Estimation of Data Completeness for 3D Scan Trajectories with Detector Offset Andreas Maier 1, Patrick Kugler 2, Günter Lauritsch 2, Joachim Hornegger 1 1 Pattern Recognition Lab and SAOT Erlangen,
More informationCT NOISE POWER SPECTRUM FOR FILTERED BACKPROJECTION AND ITERATIVE RECONSTRUCTION
CT NOISE POWER SPECTRUM FOR FILTERED BACKPROJECTION AND ITERATIVE RECONSTRUCTION Frank Dong, PhD, DABR Diagnostic Physicist, Imaging Institute Cleveland Clinic Foundation and Associate Professor of Radiology
More informationMEDICAL EQUIPMENT: COMPUTED TOMOGRAPHY. Prof. Yasser Mostafa Kadah
MEDICAL EQUIPMENT: COMPUTED TOMOGRAPHY Prof. Yasser Mostafa Kadah www.k-space.org Recommended Textbook X-Ray Computed Tomography in Biomedical Engineering, by Robert Cierniak, Springer, 211 Computed Tomography
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 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 informationDigital Scatter Removal in Mammography to enable Patient Dose Reduction
Digital Scatter Removal in Mammography to enable Patient Dose Reduction Mary Cocker Radiation Physics and Protection Oxford University Hospitals NHS Trust Chris Tromans, Mike Brady University of Oxford
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 informationBackground. Outline. Radiographic Tomosynthesis: Image Quality and Artifacts Reduction 1 / GE /
Radiographic Tomosynthesis: Image Quality and Artifacts Reduction Baojun Li, Ph.D Department of Radiology Boston University Medical Center 2012 AAPM Annual Meeting Background Linear Trajectory Tomosynthesis
More informationCover Page. The handle holds various files of this Leiden University dissertation
Cover Page The handle http://hdl.handle.net/188/48289 holds various files of this Leiden University dissertation Author: Plantagie, L. Title: Algebraic filters for filtered backprojection Issue Date: 201-04-13
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 informationAn Acquisition Geometry-Independent Calibration Tool for Industrial Computed Tomography
4th International Symposium on NDT in Aerospace 2012 - Tu.3.A.3 An Acquisition Geometry-Independent Calibration Tool for Industrial Computed Tomography Jonathan HESS *, Patrick KUEHNLEIN *, Steven OECKL
More informationComputer-Tomography II: Image reconstruction and applications
Computer-Tomography II: Image reconstruction and applications Prof. Dr. U. Oelfke DKFZ Heidelberg Department of Medical Physics (E040) Im Neuenheimer Feld 280 69120 Heidelberg, Germany u.oelfke@dkfz.de
More informationFrequency-based method to optimize the number of projections for industrial computed tomography
More Info at Open Access Database www.ndt.net/?id=18739 Frequency-based method to optimize the number of projections for industrial computed tomography Andrea Buratti 1, Soufian Ben Achour 1, Christopher
More informationA Novel Two-step Method for CT Reconstruction
A Novel Two-step Method for CT Reconstruction Michael Felsberg Computer Vision Laboratory, Dept. EE, Linköping University, Sweden mfe@isy.liu.se Abstract. In this paper we address the parallel beam 2D
More informationTESTING OF THE CIRCLE AND LINE ALGORITHM IN THE SETTING OF MICRO-CT
SCA2016-080 1/7 TESTING OF THE CIRCLE AND LINE ALGORITHM IN THE SETTING OF MICRO-CT Alexander Katsevich 1, 2 and Michael Frenkel 1 1 itomography Corp., 2 University of Central Florida (UCF) This paper
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 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 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 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 informationCarbondale, IL USA; University of North Carolina Chapel Hill, NC USA; USA; ABSTRACT
Pre-computed backprojection based penalized-likelihood (PPL) reconstruction with an edge-preserved regularizer for stationary Digital Breast Tomosynthesis Shiyu Xu a, Christy Redmon Inscoe b, Jianping
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 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 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 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 informationUltrasonic Multi-Skip Tomography for Pipe Inspection
18 th World Conference on Non destructive Testing, 16-2 April 212, Durban, South Africa Ultrasonic Multi-Skip Tomography for Pipe Inspection Arno VOLKER 1, Rik VOS 1 Alan HUNTER 1 1 TNO, Stieltjesweg 1,
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 informationGE s Revolution CT MATLAB III: CT. Kathleen Chen March 20, 2018
GE s Revolution CT MATLAB III: CT Kathleen Chen chens18@rpi.edu March 20, 2018 https://www.zmescience.com/medicine/inside-human-body-real-time-gifs-demo-power-ct-scan/ Reminders Make sure you have MATLAB
More informationPlanar tomosynthesis reconstruction in a parallel-beam framework via virtual object reconstruction
Planar tomosynthesis reconstruction in a parallel-beam framework via virtual object reconstruction Brian E. Nett a,shuaileng a and Guang-Hong Chen a,b a Department of Medical Physics, University of Wisconsin-Madison,
More informationStatistical iterative reconstruction using fast optimization transfer algorithm with successively increasing factor in Digital Breast Tomosynthesis
Statistical iterative reconstruction using fast optimization transfer algorithm with successively increasing factor in Digital Breast omosynthesis Shiyu Xu a and Zhenxi Zhang b and Ying Chen a,* a Department
More informationThe n-pi-method for Helical Cone-Beam CT
848 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 19, NO. 9, SEPTEMBER 2000 The n-pi-method for Helical Cone-Beam CT R. Proksa*, Th. Köhler, M. Grass, and J. Timmer Abstract A new class of acquisition schemes
More informationCIVA CT, an advanced simulation platform for NDT
More Info at Open Access Database www.ndt.net/?id=18774 CIVA CT, an advanced simulation platform for NDT Marius Costin 1, David Tisseur 1, Caroline Vienne 1, Ronan Guillamet 1, Hussein Banjak 1, Navnina
More informationProjection and Reconstruction-Based Noise Filtering Methods in Cone Beam CT
Projection and Reconstruction-Based Noise Filtering Methods in Cone Beam CT Benedikt Lorch 1, Martin Berger 1,2, Joachim Hornegger 1,2, Andreas Maier 1,2 1 Pattern Recognition Lab, FAU Erlangen-Nürnberg
More informationRefraction Corrected Transmission Ultrasound Computed Tomography for Application in Breast Imaging
Refraction Corrected Transmission Ultrasound Computed Tomography for Application in Breast Imaging Joint Research With Trond Varslot Marcel Jackowski Shengying Li and Klaus Mueller Ultrasound Detection
More informationMedical Image Processing: Image Reconstruction and 3D Renderings
Medical Image Processing: Image Reconstruction and 3D Renderings 김보형 서울대학교컴퓨터공학부 Computer Graphics and Image Processing Lab. 2011. 3. 23 1 Computer Graphics & Image Processing Computer Graphics : Create,
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 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 informationDigital breast tomosynthesis: comparison of different methods to calculate patient doses
Digital breast tomosynthesis: comparison of different methods to calculate patient doses Poster No.: C-2220 Congress: ECR 2011 Type: Scientific Paper Authors: A. Jacobs 1, L. Cockmartin 1, D. R. Dance
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 informationEmpirical cupping correction: A first-order raw data precorrection for cone-beam computed tomography
Empirical cupping correction: A first-order raw data precorrection for cone-beam computed tomography Marc Kachelrieß, a Katia Sourbelle, and Willi A. Kalender Institute of Medical Physics, University of
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 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 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 informationHIGH-SPEED THEE-DIMENSIONAL TOMOGRAPHIC IMAGING OF FRAGMENTS AND PRECISE STATISTICS FROM AN AUTOMATED ANALYSIS
23 RD INTERNATIONAL SYMPOSIUM ON BALLISTICS TARRAGONA, SPAIN 16-20 APRIL 2007 HIGH-SPEED THEE-DIMENSIONAL TOMOGRAPHIC IMAGING OF FRAGMENTS AND PRECISE STATISTICS FROM AN AUTOMATED ANALYSIS P. Helberg 1,
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 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 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 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 informationJoint ICTP-TWAS Workshop on Portable X-ray Analytical Instruments for Cultural Heritage. 29 April - 3 May, 2013
2455-5 Joint ICTP-TWAS Workshop on Portable X-ray Analytical Instruments for Cultural Heritage 29 April - 3 May, 2013 Lecture NoteBasic principles of X-ray Computed Tomography Diego Dreossi Elettra, Trieste
More informationReconstruction Methods for Coplanar Translational Laminography Applications
Reconstruction Methods for Coplanar Translational Laminography Applications U. EWERT, K.-U. THIESSENHUSEN, A. DERESCH, C. BELLON, S. HOHENDORF, S. KOLKOORI, N. WROBEL, B. REDMER, M. TSCHAIKNER, BAM, Berlin
More informationAdvanced Reconstruction Techniques Applied to an On-Site CT System
2nd International Symposium on NDT in Aerospace 2010 - We.1.A.4 Advanced Reconstruction Techniques Applied to an On-Site CT System Jonathan HESS, Markus EBERHORN, Markus HOFMANN, Maik LUXA Fraunhofer Development
More informationThe Near Future in Cardiac CT Image Reconstruction
SCCT 2010 The Near Future in Cardiac CT Image Reconstruction Marc Kachelrieß Institute of Medical Physics (IMP) Friedrich-Alexander Alexander-University Erlangen-Nürnberg rnberg www.imp.uni-erlangen.de
More informationCoE4TN4 Image Processing. Chapter 5 Image Restoration and Reconstruction
CoE4TN4 Image Processing Chapter 5 Image Restoration and Reconstruction Image Restoration Similar to image enhancement, the ultimate goal of restoration techniques is to improve an image Restoration: a
More informationRevisit of the Ramp Filter
IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 62, NO. 1, FEBRUARY 2015 131 Revisit of the Ramp Filter Gengsheng L. Zeng, Fellow, IEEE Abstract An important part of the filtered backprojection (FBP) algorithm
More informationPhase-Contrast Imaging and Tomography at 60 kev using a Conventional X-ray Tube
Phase-Contrast Imaging and Tomography at 60 kev using a Conventional X-ray Tube T. Donath* a, F. Pfeiffer a,b, O. Bunk a, W. Groot a, M. Bednarzik a, C. Grünzweig a, E. Hempel c, S. Popescu c, M. Hoheisel
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 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 informationIdentification of Shielding Material Configurations Using NMIS Imaging
Identification of Shielding Material Configurations Using NMIS Imaging B. R. Grogan, J. T. Mihalczo, S. M. McConchie, and J. A. Mullens Oak Ridge National Laboratory, P.O. Box 2008, MS-6010, Oak Ridge,
More informationOptimization of CT Simulation Imaging. Ingrid Reiser Dept. of Radiology The University of Chicago
Optimization of CT Simulation Imaging Ingrid Reiser Dept. of Radiology The University of Chicago Optimization of CT imaging Goal: Achieve image quality that allows to perform the task at hand (diagnostic
More informationOn the Characteristics of Helical 3-D X-ray Dark-field Imaging
On the Characteristics of Helical 3-D X-ray Dark-field Imaging Lina Felsner 1, Shiyang Hu 1, Veronika Ludwig 2, Gisela Anton 2, Andreas Maier 1, Christian Riess 1 1 Pattern Recognition Lab, Computer Science,
More informationComparison of Probing Error in Dimensional Measurement by Means of 3D Computed Tomography with Circular and Helical Sampling
nd International Symposium on NDT in Aerospace - We..A. Comparison of Probing Error in Dimensional Measurement by Means of D Computed Tomography with Circular and Helical Sampling Jochen HILLER, Stefan
More informationTOMOGRAPHIC reconstruction problems are found in
4750 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23, NO. 11, NOVEMBER 2014 Improving Filtered Backprojection Reconstruction by Data-Dependent Filtering Abstract Filtered backprojection, one of the most
More informationQuality control phantoms and protocol for a tomography system
Quality control phantoms and protocol for a tomography system Lucía Franco 1 1 CT AIMEN, C/Relva 27A O Porriño Pontevedra, Spain, lfranco@aimen.es Abstract Tomography systems for non-destructive testing
More informationHIGH RESOLUTION COMPUTED TOMOGRAPHY FOR METROLOGY
HIGH RESOLUTION COMPUTED TOMOGRAPHY FOR METROLOGY David K. Lehmann 1, Kathleen Brockdorf 1 and Dirk Neuber 2 1 phoenix x-ray Systems + Services Inc. St. Petersburg, FL, USA 2 phoenix x-ray Systems + Services
More informationSpectral analysis of non-stationary CT noise
Spectral analysis of non-stationary CT noise Kenneth M. Hanson Los Alamos Scientific Laboratory Int. Symposium and Course on Computed Tomography, Las Vegas, April 7-11, 1980 This presentation available
More informationConvolution-Based Truncation Correction for C-Arm CT using Scattered Radiation
Convolution-Based Truncation Correction for C-Arm CT using Scattered Radiation Bastian Bier 1, Chris Schwemmer 1,2, Andreas Maier 1,3, Hannes G. Hofmann 1, Yan Xia 1, Joachim Hornegger 1,2, Tobias Struffert
More informationComparison of Reconstruction Methods for Computed Tomography with Industrial Robots using Automatic Object Position Recognition
19 th World Conference on Non-Destructive Testing 2016 Comparison of Reconstruction Methods for Computed Tomography with Industrial Robots using Automatic Object Position Recognition Philipp KLEIN 1, Frank
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 informationContrast Enhancement with Dual Energy CT for the Assessment of Atherosclerosis
Contrast Enhancement with Dual Energy CT for the Assessment of Atherosclerosis Stefan C. Saur 1, Hatem Alkadhi 2, Luca Regazzoni 1, Simon Eugster 1, Gábor Székely 1, Philippe Cattin 1,3 1 Computer Vision
More informationEvaluation of Back Projection Methods for Breast Tomosynthesis Image Reconstruction
DOI 10.1007/s10278-014-9736-6 Evaluation of Back Projection Methods for Breast Tomosynthesis Image Reconstruction Weihua Zhou & Jianping Lu & Otto Zhou & Ying Chen # Society for Imaging Informatics in
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 informationDigital Image Processing
Digital Image Processing SPECIAL TOPICS CT IMAGES Hamid R. Rabiee Fall 2015 What is an image? 2 Are images only about visual concepts? We ve already seen that there are other kinds of image. In this lecture
More informationBeam Attenuation Grid Based Scatter Correction Algorithm for. Cone Beam Volume CT
11th European Conference on Non-Destructive Testing (ECNDT 2014), October 6-10, 2014, Prague, Czech Republic Beam Attenuation Grid Based Scatter Correction Algorithm for More Info at Open Access Database
More informationSuitability of a new alignment correction method for industrial CT
Suitability of a new alignment correction method for industrial CT Matthias Elter 1, Nicole Maass 1, Peter Koch 2 1 Siemens AG, Healthcare Sector, Erlangen, Germany, e-mail: matthias.elter@siemens.com,
More information4/19/2016. Deborah Thames R.T. (R)(M)(QM) Theory & Technology and advancement in 3D imaging DBT
Deborah Thames R.T. (R)(M)(QM) Theory & Technology and advancement in 3D imaging DBT 1 Three manufacturers approved for Tomo Hologic and GE, and Siemens Why 2D Digital Mammography 2D FFDM it appears to
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 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 information