Back Matter (PDF)

Total Page:16

File Type:pdf, Size:1020Kb

Back Matter (PDF) Abbott, J, 910(ab) Ailman, KC, 2067 Arsenault, A, 875(ab) Barness, B, 837(ab) Benard, iF, 1003(ab) Abdel-Dayem, HM, 1396, Al-Mohannadi, 5, 2014, Asahina, M, 917(ab) Barnett, CA, 938(ab) Bender, H, 942(ab), 1002(ab) 2014, 843(ab), 867(ab) 867(ab) Asano, T, 333 Barnhart, CS, 951(ab) Bendriem,B, 2020,843(ab), Abdel-Nabi,HH, 14, Alpert, NM, 908(ab), Ascoop, CAPL, 1727 Barrett, HH, 851(ab), 882(ab), 883(ab), 888(ab), 959(ab), l000(ab) 924(ab) Ashida, K, 246 879(ab), 891(ab), 1002(ab) 943(ab) Abe, K, lOl9(ab) Altehoefer, C, 886(ab) Ashmore, RF, 995(ab) Barrett, iS, 959(ab) Ben-Haim, S, 1486 Abe, S, 10l9(ab) Aithouse, L, 972(ab) Assis, RVC, 995(ab) Barrio, JR. 633, 1472 Benhali, H, 101l(ab) Abel,W, 117 A1-Tikriti, MS, 848(ab), Atkins, HI, 305 Barron, Bi, 968(ab), Ben-Harush, M, 1699 Abi-Dargham, A, 91 1(ab) 945(ab) Atkins,HL, 1717 l047(ab) Benjamin, C, 855(ab), Abraham, V, 832(ab) Altman, K!, 1725 Atkinson,ER, 423 Bartelsmeier,A, 906(ab) 866(ab) Abramovici,J, 1005(ab) Altmann,A, 1982,lOl9(ab) Atkinson,JB, 826(ab) Bartenstein, P, 915(ab) Benjamin, RS, 858(ab), Abrams,Mi, 710,9l0(ab) A1-Zaabi, KS, 978(ab) Aubert, B, 1006(ab) Barthelemy,E, 956(ab) 992(ab) Abrams,P0, 1099 Amano, M, 1008(ab) Austin, MC, 877(ab), Bartlett, B, 1024(ab) Benkert, 0, 887(ab) Aburano, T, 2024, 957(ab), Amarenco,P, 858(ab) 930(ab) Bartolozzi, C, 954(ab) Bennett, DV, 849(ab) 1020(ab) Ambrose,KR, 1028(ab) Avet, P. 1269 Barton, D, 846(ab) Bennett, LR, 827(ab), Acharya, AS, 986(ab) Ambrosetti,P, 1766 Awad,I, 970(ab) Barton,H, 2222 l043(ab) Achong,DM, 256 Aminian, A, 102l(ab) Axworthy, DB, 880(ab), Barzen, 0, 902(ab), 971(ab), Benoliel, i, 1846 Acito, P, 9l5(ab) Amino, 5, 1019(ab) 909(ab), 9l0(ab), 934(ab) 997(ab), 1018(ab) Bensel, K, 835(ab) Ackerman, RF, 1819 Amis, ES, 1042(ab) Basile, F, 866(ab) Benvenuti,C, 471 Ackermann, RF, 908(ab) Ammann, W, 157 Babaian, Ri, lOOl(ab) Basmadjian, GP, 98l(ab) Berding, G, 972(ab) Adachi, I, 1023(ab) Ampil, FL, 1047(ab) Babich, J, 924(ab), 935(ab) Basoglu, T, 906(ab) Berg, 80, 918(ab) Adams,HR, 945(ab) Amstutz, HP, 231 Babich, JW, 2154, 9l0(ab), Bassano, DA, 150, 90l(ab) Berger,HJ, 144 Adams, M, 974(ab) Anderson, Cl, 558, 1685, 996(ab), 1024(ab) Bassingthwaighte,JB, 581 Berger, KL, 1579, 858(ab), Adatepe, MH, 263 837(ab) Bacharach, SL, 1209, 2058, Bataille, 0, 832(ab) l003(ab), 1007(ab), Adelstein, Si, 384 Anderson, JA, l003(ab), 2226,88l(ab), 938(ab) Batchelor, S, 1041(ab) l009(ab) Aden, MD, 1132, 894(ab) lOlO(ab) Badger, C, 960(ab) Bateman,TM, 876(ab), Berger, MR. 1982, 966(ab), Adler, A, 997(ab), 1044(ab) Anderson,PJ, 2090 Badger,CC, 863(ab), 987(ab) 951(ab) l019(ab) Adriaens,Pi, 551, 1032(ab) Anderson, R, 927(ab) Bading,JR, 1373 BattiSti,C, 1859 Bergh, JR. 1039(ab) Affleck, D, 88 l(ab) Anderson, T, 839(ab) Bae, R, 926(ab) Baudhuin, T, 825(ab), Bergmann, SR. 836(ab), Afriyie,M, 973(ab) Anderson, U, 990(ab) Baer,L, 906(ab) 837(ab), 875(ab) 837(ab), 855(ab), 884(ab), Aftab,N, 2154 Andersson, L, 1654, 954(ab) Bagdades, E, 1022(ab) Baudoux, M, 1337 1426(le) Aggarwal, S, 1548 Andreassian, B, 835(ab) Bahrassa, FS, l047(ab) Bauer, A, 865(ab), 993(ab) Berk, MR. 1556 Agid,Y, 1481,10l9(ab) Andre-Fouet, X, 953(ab) Baidoo, KE, 899(ab), Baum,RP, 853(ab),904(ab), Berkovic,SF, 930(ab) Agostini, D, 471 Andrews, D, 964(ab) 921(ab), l032(ab) 9l2(ab) Berkovitz,DM, N25(10) Ahn, CS, 964(ab) Andrich, MP, 852(ab), Bailey, DL, 90l(ab) Baxter,KG, l039(ab), Berkowitz, KA, 512 Ahonen, A, 1255 976(ab) Baird, AE, 877(ab) 1046(ab) Berlangieri,SU, l047(ab) Aigner, TO, 1383, 945(ab), Angelberger, P. 108 Bakir, MA, 2154 Bayhan, H, 464(le), 866(ab) Berlin, JA, 858(ab), 873(ab) 965(ab) Anker, JP, 903(ab) Bakker, WH, 652, 955(ab), Bayouth, JE, 941(ab) Berman,DS, 1509,854(ab), Akamune,A, 893(ab), Ansari, A, 844(ab) 903(ab), 914(ab) Bazin,JP,101l(ab) 876(ab),91l(ab) 933(ab), 972(ab) Ansari, MS, 847(ab) Baldock,C, 104I(ab) Beanlands, R, 684, 826(ab), Bernâ,L, 840(ab), 845(ab), Akansel, G, 21 10, 868(ab) Antar, MA, 928(ab), Baldwin,R, 896(ab) 865(ab), 882(ab) 895(ab), 994(ab) Akiba, A, 1005(ab) 970(ab), 1040(ab), Baldwin, RM, 1836, 1964, Beatty, 50, 953(ab) Bernard, HF, 955(ab) Akiba, H, 958(ab) 1041(ab) 848(ab),911(ab),945(ab) Beauchamp, HT, 900(ab) Bernstein, ID, 863(ab), Akoun, 0, 81 Antich, PP, 974(ab), 990(ab), Balériaux,D, 868(ab) Beaumier, P, 934(ab) 941(ab), 960(ab), 987(ab) Aktolun, C, 464(le) 1003(ab), l010(ab) Ballinger, iR, 970(ab) Beaumier, PL, 394, 880(ab), Berrah, H, 843(ab), 888(ab) Alagona,P, Jr, 1723(le) Antico, VF, 2094 Balon,HR, 1393,898(ab) 909(ab),910(ab),l028(ab) Berridge,MS,885(ab), Alamos, F, 838(ab) Antman, KH, 1594 Balseiro, i, 2059(le) Bebin, M, 971(ab) 984(ab) Alavi, A, 269, 590, 751, Antoine,J-M, 956(ab) Baiter,HS, 1949 Beck,P, 972(ab) Berry, i, 2067 1964, 2036, 848(ab), Antonino,A, 2222 Ban, T, 886(ab) Becker, DP, 867(ab) Bertoni, iM, 1047(ab) 849(ab),857(ab),858(ab), Antonucci, F, 895(ab) Banaszczyk,M, 921(ab) Becker, JA, 1005(ab), Bertrand,A, 906(ab), 896(ab), 897(ab), 898(ab), Aoki,5, 844(ab),966(ab) Bander, N, 913(ab) 1043(ab) 956(ab) 902(ab), 929(ab), 940(ab), Aoshima, S, 6 Bando, M, lOl6(ab) Becker, W, 903(ab), 1428(le) Besnard, i-C, 981(ab) 954(ab),965(ab),992(ab), Aotsuka, A, 917(ab) Bankole, A, 849(ab) Beckers,C, 73 Besozzi, MC, 885(ab), 999(ab), l012(ab), Appelbaum,FR, 863(ab), Banks, WR, 830(ab), Bede,ii, 1914(le) 93l(ab), 983(ab) 1013(ab),1588(le) 94l(ab), 960(ab), 987(ab) 975(ab), 983(ab) Beetz, R, 1021(ab) Besser,GM, 1121, 914(ab) Albarède,P, 409 Appelbaum,JW, 1099 Banzo, I, 955(ab) Behrenbeck, T, 2080 Bessières,M, 906(ab) Albeit, R, 900(ab) Appledorn,R, 956(ab) Barbaras, L, 947(ab) Beierwaltes,WH, 2116 Bettinardi, V, 973(ab) Alderson, P0, 570, 1042(ab) Arai, H, 10l9(ab) Barbarics, E, 1033(ab) Beilin, I, 886(ab) Beuscart, R, 947(ab) Alexander, GE, 940(ab) Araki, N, 1029(ab) Barbaro, NM, 967(ab) Bekdik, C, 319 Bex, M, 832(ab) Alexander, M, 873(ab), Araki,Y, 95l(ab) Barber, HB, 851(ab), Belinka, BA ir, 988(ab) Bhadkamkar, VA, 858(ab), 874(ab) Aras,T, 1027(ab) 879(ab),1002(ab) Bellande, E, 989(ab), lOOl(ab) Alexoff,DL, 853(ab) Araujo,LI, 1669 Barber, R, 983(ab) l034(ab) Bhatia, K, 1994 Aliabadi,P, 643 Archer,CM, 850(ab) Barbet, J, 9l2(ab), 1000(ab) Bellen,J, 1283 Bhargava, KK, 919(ab), Alioth, C, 833(ab) Archer, 5, 423 Barbour,MM, 95l(ab) BelIer,GA, 668, 1732, 986(ab) Alkire, AL, 974(ab) Areeda, J, 876(ab) Bares, R, 100l(ab) 864(ab) Bhattacharva, 5, 938(ab) Allada, V, 977(ab) Argenyi, E, 1917(le) Barkan,AL, 1608,834(ab) Bellina, CR, 1020(ab) Bianchi, R, 1020(ab) Allen,EW,98l(ab), Aric,I, l041(ab) Barker,WW, 1459 Bellinger, MF, 923(ab), Bianco, iA, l586(le) 1034(ab) Arimizu,N, 324 Barkovich, AJ, 967(ab) 977(ab) Biaslo, Y, 854(ab) Allen,LF, 23 Anlkan, 0, 1027(ab) Barlow, CF. 838(ab) Belliveau,iW, 867(ab) Bice,AN, 771,864(ab), Allenberg,JR. 907(ab) Arntz, IE, 936(ab) Barnes, D, 950(ab) Bellon, EM, 1044(ab) 872(ab), 889(ab), Allie, D, 1269 Aronen, Hi, 867(ab) Barnes, WE, 2214 Belzberg,AS, 1468 l005(ab), 125l(le), AuthorIndex•1992 2251 Bidaut 1429(le) Boles Ponto, LL, 1008(ab), Breithardt, G, 915(ab) Burke,TS, 1390 Carson, GC, l047(ab) Bidaut, LM, 868(ab) l027(ab) Breitz, HB, 1099 Burkholder, D, 979(ab) Carson, RE, 2103, 881(ab), Biedler, JL, 1373 Bolzati, C, 850(ab) Bremner, ID, 911(ab) Burn, PR, 998(ab) 945(ab), 10l0(ab) Biegel, J, 878(ab) Bomanji,J, 1121,904(ab), Brennan, KM, 884(ab) Burns,HD, 900(ab) Carstens, MC, 992(ab) Biegon, A, 890(ab) 9l4(ab), 924(ab) Brenner, N, 900(ab) Burrows, L, 949(ab) Cartner, T, 874(ab) Biemer, E, 859(ab) Bond,HW, 885(ab) Bresnahan,DR Jr, 951(ab) Buscombe,JR, 903(ab) Carton, EG, 239 Bierman,R, 435 Boni, G, 1020(ab) Bride, B, 526, 839(ab), Bushe,H, 958(ab) Cartwright, PC, 873(ab) Biersack, H-i, 526, 839(ab), Boniface, G, 853(ab) 844(ab), 9l4(ab), 995(ab) Bushman, Mi, 989(ab), Carvalho, PA, 181, 1516, 844(ab), 914(ab), 972(ab), Bonilla, JJ, 955(ab) Britell,C, l035(ab) l033(ab), 1024(ab) 970(ab) 995(ab), 1028(ab) Bonino, C, 999(ab) Britton,KE, 1121,904(ab), Bushnell,DL, 2214 Casara, D, 1478 Bigard, M-A, 956(ab) Bonnet,AM, 1481,l019(ab) 914(ab), 924(ab) Busnardo,B, 1478 Casciari, JJ, 2202, l010(ab) Biggs, T, 676 Bonow,RO,815, 1209, Broadhurst, P, 938(ab) Bussiére-Lapalus,F, 1846 Casey, ME, 86l(ab), 1430(le) Bigner,DD, 1535,881(ab), 2226, 2057(le), 88 l(ab), Brodack, i, 1025(ab) Butchko, G, 912(ab) Casey, Mi, 829(ab) 934(ab) 938(ab) BrOdaCk,JW, 850(ab), Butler, cs, 964(ab) Cashman, P, 938(ab) Bihi, H, 839(ab) Bonte, F, 992(ab) 937(ab),980(ab) Butler, RE, 1014(ab) Casi, M, 863(ab) Bilchik, T, 269 Bonte, Fi, 826(ab) Brodie,JD, 192,287, 1720, Butler, SP, 1029(ab), Cassel, D, 916(ab), 952(ab) Billings, J, 870(ab), 890(ab), Booker, FS, 850(ab) 847(ab) 1252(le) Castaigne,A, 471, 896(ab) 963(ab),989(ab) Borbely,K, 1931 Broich, K, 870(ab), 898(ab), Buxton,DB,865(ab), Castaneda, AR, 838(ab), Billingsley,JL, 2052 Borchert,R, 830(ab), 972(ab) 945(ab), 1425(le) 930(ab) Biniakiewicz,D, 993(ab) 975(ab),983(ab) Brooks, CE, 950(ab) Bylund, DB, 861(ab) Castelli, L, 892(ab), 918(ab), Binon, J-P, 1337 Bordlee, RP, 1857 Brooks,KM, 921(ab) Byrne, CL, 843(ab) 999(ab) Birnbaum, BA, 960(ab) Borer, iS, 2057(le), 845(ab) Bmoks,RA, 1931 Cattaruzzi, E, 895(ab) Cabahug, C, 1015(ab), BischofDelaloye, A, 1756, Borges-Neto, S, 825(ab) Broquet, PE, 1766 Cavagnaro, CF, 956(ab) 1028(ab) 903(ab), 956(ab) Borkon, AM, 95 l(ab) Brotchi, J, 868(ab) Cave,V, 186,2086,854(ab), Cacheris, W, 974(ab) Bischoff@H, 828(ab), Borm, iiJ, 917(ab) Brouillet, E, 973(ab) 866(ab), 952(ab) Cacini, W, 2190 1045(ab) Bormans, G, 937(ab) Broussard, W, 985(ab) Cerar, A, 587 Brown,DW, 1750 Cahill, N, l9N(le) Bishop, HA, 1040(ab) Bormans, GM, 551 Caillat-Vigneron,N, 913(ab) Ceriani,R,912(ab) Brown,J, 848(ab) Bisson, G, 855(ab), 866(ab) Borst, MM, 2138 Cain,JW, 838(ab) Ceriani, RI, 1530 Bjorn, MJ, 1099 Bosworth, M, 974(ab) Brown,JA, 985(ab) Cerino,M, 951(ab) Calay,R, 1337 Bjornson, B, 1758 Bottino,B, 1028(ab) Brown,JK, 1881, 901(ab) Cerletty, JM, 869(ab) Caldwell, JH, 102, 2144, Bottlaender,M, 883(ab), Brown,MA, 884(ab) Cerqueira, MD, 202, 763, Black,HR, 2045,906(ab) 2202 Black, KL, 867(ab) 973(ab) Brown,MD, 885(ab) 1324,1493,1642,866(ab), Calenoff, E, 934(ab), 936(ab) Botvithck, EH, 1444, Brown,ML, 1613,923(ab) 939(ab) Black,PMcL,844(ab), calikoglu, T, 319 896(ab), 910(ab), 935(ab), Bmwn, MT.
Recommended publications
  • CT Image Reconstruction Basics
    CT Image Reconstruction Basics 1. Introduction CT perfusion imaging requires the reconstruction of a series of time-dependent volumetric datasets. The sequence of CT volumes measures the dynamics of contrast agent both in the vasculature and in the parenchyma. CT image reconstruction refers to the computational process of determining tomographic images from X-ray projection images of the irradiated patient. Image reconstruction is a compute-intensive task and one of the most crucial steps in the CT imaging process. As the basics of X-ray physics were detailed in Chapter 1, we assume that the result of the X-ray image formation is an attenuation image. Each individual pixel on the detector therefore represents a line integral, that is, the accumulation of all X-ray attenuation coefficients along the projection line. Here, the projection line is the connecting line of the X-ray focal spot with the center of the respective detector pixel. 2. From Line Integrals to Voxels In spite of the discrete nature of the projection images, most reconstruction theory uses a continuous framework, in which the reconstruction algorithms are derived mathematically. The problem of discrete sampling is then solved within the final formulation of the reconstruction algorithm. As the discrete sampling is a more technical issue, we will neglect it here for the ease of presentation and refer to the literature that details these steps [1]. Thus, we will remain in a continuous domain in this section. a. Radon Transform Figure 1: Parallel-beam geometry and the generation of a parallel-beam projection ( , ). For ease of understanding, we explain the approach in the 2D (x,y) plane.풑 The풔 휽 source- detector arrangement is rotating around the object (Figure 1).
    [Show full text]
  • Manipulation of Object-Based 3D
    THE RECONSTRUCTION AND MANIPULATION OF OBJECT-BASED 3D X-RAY IMAGES by Simant Prakoonwit This thesisis submittedin partial fulfilment of the requirementsfor the Degree of Doctor of Philosophy (Ph. D. ) and the Diploma of Imperial College (D. I. C) Departmentof Electricaland Electronic Engineering hnperial Collegeof Science,Technology and Medicine University of London May 1995 ýy4y. AKSTRAICT Computersand graphic peripherals have come to play a significant part in scientific visualisation.In medical applications, many non-invasivetechniques have beendevelopedfor visualising the inner entities of the human body. A major interest has arisen in representationof the entities by digital objects that can be manipulated and displayedusing methods developed in ComputerGraphics. A novel methodin 3D reconstructionis presentedusing the assumptionthat all the entities of interest can be representedas a set of discrete objects. An object is characterisedby its commoncharacteristic. Each object is reconstructedfrom the projections of its surface curvesin about 10 conventional2D X-ray images taken at suitableprojection angles.Yhe method automatically generates an optimumnumber of the object's surface points that are appropriately distributed Yhe method also determines the object's closed surface ftom these surface points. Each solid reconstructedobject is thenrepresented by a Boundary-representation(B-rep) scheme, which is compatiblewith any standarddisplay and manipulationtechnique, and can be manipulatedseparately. Experimentswere performed on both
    [Show full text]
  • Cone-Beam Reconstruction Using Filtered Backprojection
    Link¨oping Studies in Science and Technology Dissertation No. 672 Cone-Beam Reconstruction Using Filtered Backprojection Henrik Turbell Department of Electrical Engineering Link¨opings universitet, SE-581 83 Link¨oping, Sweden Link¨oping, February 2001 ISBN 91-7219-919-9 ISSN 0345-7524 PrintedinSwedenbyUniTryck,Link¨oping 2001 To my parents Abstract The art of medical computed tomography is constantly evolving and the last years have seen new ground breaking systems with multi-row detectors. These tomo- graphs are able to increase both scanning speed and image quality compared to the single-row systems more commonly found in hospitals today. This thesis deals with three-dimensional image reconstruction algorithms to be used in future gen- erations of tomographs with even more detector rows than found in current multi- row systems. The first practical algorithm for three-dimensional reconstruction from cone- beam projections acquired from a circular source trajectory is the FDK method. We present a novel version of this algorithm that produces images of higher quality. We also formulate a version of the FDK method that performs the backprojection in O(N 3 log N) steps instead of the O(N 4) steps traditionally required. An efficient way to acquire volumetric patient data is to use a helical source trajectory together with a multi-row detector. We present an overview of existing reconstruction algorithms for this geometry. We also present a new family of algo- rithms, the PI methods, which seem to surpass other proposals in simplicity while delivering images of high quality. The detector used in the PI methods is limited to a window that exactly fits the cylindrical section between two consecutive turns of the helical source path.
    [Show full text]
  • Iterative Reconstruction of Cone-Beam Micro-CT Data
    University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 5-2006 Iterative Reconstruction of Cone-Beam Micro-CT Data Thomas Matthew Benson University of Tennessee - Knoxville Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Computer Sciences Commons Recommended Citation Benson, Thomas Matthew, "Iterative Reconstruction of Cone-Beam Micro-CT Data. " PhD diss., University of Tennessee, 2006. https://trace.tennessee.edu/utk_graddiss/1640 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Thomas Matthew Benson entitled "Iterative Reconstruction of Cone-Beam Micro-CT Data." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Computer Science. Jens Gregor, Major Professor We have read this dissertation and recommend its acceptance: Michael Berry, Charles Collins, Michael Thomason, Jonathan Wall Accepted for the Council: Carolyn R. Hodges Vice Provost and Dean of the Graduate School (Original signatures are on file with official studentecor r ds.) To the Graduate Council: I am submitting herewith a dissertation written by Thomas Matthew Benson entitled “Iter- ative Reconstruction of Cone-Beam Micro-CT Data”. I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in par- tial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Computer Science.
    [Show full text]
  • Advanced Industrial X-Ray Computed Tomography for Defect Detection and Characterisation of Composite Structures
    ADVANCED INDUSTRIAL X-RAY COMPUTED TOMOGRAPHY FOR DEFECT DETECTION AND CHARACTERISATION OF COMPOSITE STRUCTURES A thesis submitted to The University of Manchester for the degree of Doctor of Engineering in the Faculty of Engineering and Physical Sciences. 2010 MATHEW AMOS SCHOOL OF MATERIALS Contents Abbreviations 7 Abstract 8 Declaration and Copyright 9 Acknowledgements 10 Chapter 1 1 Introduction 11 1.1 Motivation of Research ……………………………………………….............. 11 1.2 Aims and Objectives ……………………………………………….................. 14 1.3 X-ray Computer Tomography……………………………………................... 14 1.3.1 Commercial Application of X-ray Computed Tomography………. 14 1.3.2 Current Limitations of Industrial X-ray Computed Tomography…………………………………………………………... 15 1.4 Organisation of Thesis…………………………………………………………. 17 Chapter 2 2 Literature Review 18 2.1 Non-Destructive-Testing………………………………………………............. 18 2.1.1 Radiography…………………………………………………………... 19 2.1.1.1 X-ray Physics……………………………………….......... 19 2.1.1.2 X-ray Detectors…………………………………………… 20 2.1.1.3 Application to Fibre-Reinforced-Plastic Composites……………………………………………….. 23 2.1.2 Ultrasonic Testing…………………………………………………….. 24 2.1.1.1 Ultrasonic Inspection Methods………………………….. 26 2.1.1.1 Application to Fibre-Reinforced-Plastic Composites……………………………………………….. 29 2.1.3 Infrared Thermography………………………………………............ 30 2.1.3.1 Application to Fibre-Reinforced-Plastic Composites……………………………………………….. 31 2.1.4 X-ray Computer Tomography……………………………………….. 32 2.1.4.1 Application to Fibre-Reinforced-Plastic Composites……………………………………………….. 34 2 Contents 2.1.5 Summary of NDT Capabilities with respect to FRP Composites……………………………………………………........... 34 2.2 X-ray Computer Tomography…………………………………………………. 36 2.2.1 A Brief History of X-ray CT………………………………………….. 36 2.2.2 X-ray CT Scanning Geometries…………………………………….. 37 2.2.3 CT Reconstruction……………………………………………........... 42 2.2.3.1 General Principles……………………………………….
    [Show full text]
  • Precise and Automated Tomographic Reconstruction with a Limited Number of Projections
    Precise and Automated Tomographic Reconstruction with a Limited Number of Projections Zur Erlangung des akademischen Grades eines DOKTOR-INGENIEURS an der Fakultät für Elektrotechnik und Informationstechnik des Karlsruher Instituts für Technologie (KIT) genehmigte Dissertation von M.Eng. Xiaoli Yang geb. in Shandong Province, China Hauptreferent: Prof. Dr. rer. nat. Marc Weber Korreferent: Prof. Dr. rer. nat. Olaf Dössel Tag der mündlichen Prüfung: 12.01.2016 KIT – University of the State of Baden-Württemberg and National Research Center of the Helmholtz Association www.kit.edu This document is licensed under the Creative Commons Attribution – Share Alike 3.0 DE License (CC BY-SA 3.0 DE): http://creativecommons.org/licenses/by-sa/3.0/de/ Ich versichere wahrheitsgemäß, die Dissertation bis auf die dort angegebene Hilfe selb- ständig angefertigt, alle benutzten Hilfsmittel vollständig und genau angegeben und alles kenntlich gemacht zu haben, was aus Arbeiten anderer und eigenen Veröffentlichungen unverändert oder mit Änderungen entnommen wurde. Karlsruhe, den Xiaoli Yang Abstract X-ray computed tomography (CT) is a popular, non-invasive technique capable of pro- ducing high spatial resolution images. It is generally utilized to provide structural infor- mation of objects to examine. In multiple applications, such as biology, material, and medical science, more and more attention has been paid to tomographic imaging with a limited amount of projections. In this thesis, the tomographic imaging with a limited amount of projections is important in adapting a rapid imaging process or limiting the X- ray radiation exposure to a low level. However, fewer projections normally imply poorer quality of image reconstruction with artifacts by the traditional filtered back-projection (FBP) method, hampering the image analysis and interpretation.
    [Show full text]
  • Image Quality of Digital Breast Tomosynthesis: Optimization in Image Acquisition and Reconstruction
    Image Quality of Digital Breast Tomosynthesis: Optimization in Image Acquisition and Reconstruction by Gang Wu A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Medical Biophysics University of Toronto c Copyright 2014 by Gang Wu Abstract Image Quality of Digital Breast Tomosynthesis: Optimization in Image Acquisition and Reconstruction Gang Wu Doctor of Philosophy Graduate Department of Medical Biophysics University of Toronto 2014 Breast cancer continues to be the most frequently diagnosed cancer in Canadian women. Currently, mammography is the clinically accepted best modality for breast cancer detection and the regular use of screening has been shown to contribute to reduced mortality. However, mammography suffers from several drawbacks which limit its sensitivity and specificity. As a potential solution, digital breast to- mosynthesis (DBT) uses a limited number (typically 10{20) of low-dose x-ray projections to produce a three-dimensional tomographic representation of the breast. The reconstruction of DBT images is challenged by such incomplete sampling. The purpose of this thesis is to evaluate the effect of image acquisition parameters on image quality of DBT for various reconstruction techniques and to optimize these, with three specific goals: A) Develop a better power spectrum estimator for detectability calcu- lation as a task-based image quality index; B) Develop a paired-view algorithm for artifact removal in DBT reconstruction; and C) Increase dose efficiency in DBT by reducing random noise. A better power spectrum estimator was developed using a multitaper technique, which yields reduced bias and variance in estimation compared to the conventional moving average method.
    [Show full text]
  • Tomography Algorithms Development of Computed
    International Journal of Biomedical Imaging Development of Computed Tomography Algorithms Guest Editors: Hengyong Yu, Patrick J. La Riviere, and Xiangyang Tang International Journal of Biomedical Imaging Development of Computed Tomography Algorithms International Journal of Biomedical Imaging Development of Computed Tomography Algorithms Guest Editors: Hengyong Yu, Patrick J. La Riviere, and Xiangyang Tang Copyright © 2006 Hindawi Publishing Corporation. All rights reserved. This is a special issue published in volume 2006 of “International Journal of Biomedical Imaging.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Editor-in-Chief Ge Wang, Virginia Polytechnic Institute and State University, USA Associate Editors Haim Azhari, Israel Ming Jiang, China Jie Tian, China Kyongtae Bae, USA Marc Kachelrieß, Germany Michael Vannier, USA Richard Bayford, UK Seung Wook Lee, South Korea Yue Wang, USA Freek Beekman, The Netherlands Alfred Karl Louis, Germany Guowei Wei, USA Subhasis Chaudhuri, India Erik Meijering, The Netherlands David L. Wilson, USA Jyh-Cheng Chen, Taiwan Vasilis Ntziachristos, USA Sun Guk Yu, South Korea Anne Clough, USA Scott Pohlman, USA Habib Zaidi, Switzerland Carl Crawford, USA Erik Ritman, USA Yantian Zhang, USA Min Gu, Australia Jay Rubinstein, USA Yibin Zheng, USA Eric Hoffman, USA Pete Santago, USA Tiange Zhuang, China Jiang Hsieh, USA Lizhi Sun,
    [Show full text]
  • Tomographic Reconstruction of Transparent Objects By
    Tomographic Reconstruction of Transparent Objects by Borislav Danielov Trifonov B.Sc, The University of South Florida, 2002 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of Science The Faculty of Graduate Studies (Computer Science) The University of British Columbia December, 2006 © Borislav Danielov Trifonov 2006 Abstract This thesis presents an optical acquisition setup and application of tomo• graphic reconstruction to recover the shape of transparent objects. Although various optical scanning methods have been used to recover the shape of objects, they are normally intended for opaque objects, and there are diffi• culties in applying them to transparent ones. An alternative is to use X-ray computed tomography, but this requires a specialized setup, and computer graphics laboratories are not expected to have such equipment. Addition• ally, our setup avoids other problems of optical scanning, such as caused by occlusions, and is able to recover the internal geometry of the objects. Table of Contents Abstract 11 Table of Contents m List of Figures v Acknowledgements vl 1 Introduction 1 1.1 Objectives 3 1.2 Basic assumptions 3 1.3 Overview 5 2 Related Work 6 2.1 Visible light scanning 6 2.2 X-ray computed tomography 7 2.3 Visual hull and voxel coloring 8 2.4 Optical tomography 8 3 Acquisition 10 3.1 Physical setup 10 3.2 Minimizing refraction H 3.3 Calibration 13 iii V Table of Contents 3.4 Acquisition 14 4 Reconstruction 1? 4.1 SART 17 4.2 Projection 18 4.3 Backprojection 19 4.4 Implementation 20 5 Results 22 6 Conclusions and Future Work 31 Bibliography 33 List of Figures 3.1 The acquisition setup 10 3.2 Front and rear calibration images 13 3.3 Ray distribution and reconstruction region from calibration.
    [Show full text]
  • Suppression of Cone-Beam Artefacts with Direct Iterative Reconstruction Computed Tomography Trajectories (DIRECTT)
    Journal of Imaging Article Suppression of Cone-Beam Artefacts with Direct Iterative Reconstruction Computed Tomography Trajectories (DIRECTT) Sotirios Magkos 1,* , Andreas Kupsch 1 and Giovanni Bruno 1,2 1 Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, 12205 Berlin, Germany; [email protected] (A.K.); [email protected] (G.B.) 2 Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany * Correspondence: [email protected]; Tel.: +49-30-81044463 Abstract: The reconstruction of cone-beam computed tomography data using filtered back-projection algorithms unavoidably results in severe artefacts. We describe how the Direct Iterative Reconstruc- tion of Computed Tomography Trajectories (DIRECTT) algorithm can be combined with a model of the artefacts for the reconstruction of such data. The implementation of DIRECTT results in reconstructed volumes of superior quality compared to the conventional algorithms. Keywords: iteration method; signal processing; X-ray imaging; computed tomography 1. Introduction Citation: Magkos, S.; Kupsch, A.; Computed tomography (CT) systems for non-destructive testing and material analysis Bruno, G. Suppression of Cone-Beam generally use a cone beam on a sample that rotates in a circular orbit [1], with cylindrical Artefacts with Direct Iterative samples being among the most common [1–4]. The exact reconstruction of data acquired Reconstruction Computed during such a measurement is not possible because the geometry does not satisfy Tuy’s Tomography Trajectories (DIRECTT). sufficiency condition [5]. This is demonstrated in Figure1 with the reconstruction of a J. Imaging 2021, 7, 147. https:// concrete rod by the commonly used algorithm developed by Feldkamp, Davis and Kress doi.org/10.3390/jimaging7080147 (FDK) [6].
    [Show full text]
  • Brain Single-Photon Emission CT Physics Principles
    Brain Single-Photon Emission CT Physics PHYSICS REVIEW Principles R. Accorsi SUMMARY: The basic principles of scintigraphy are reviewed and extended to 3D imaging. Single- photon emission computed tomography (SPECT) is a sensitive and specific 3D technique to monitor in vivo functional processes in both clinical and preclinical studies. SPECT/CT systems are becoming increasingly common and can provide accurately registered anatomic information as well. In general, SPECT is affected by low photon-collection efficiency, but in brain imaging, not all of the large FOV of clinical gamma cameras is needed: The use of fan- and cone-beam collimation trades off the unused FOV for increased sensitivity and resolution. The design of dedicated cameras aims at increased angular coverage and resolution by minimizing the distance from the patient. The corrections needed for quantitative imaging are challenging but can take advantage of the relative spatial uniformity of attenuation and scatter. Preclinical systems can provide submillimeter resolution in small animal brain imaging with workable sensitivity. rain single-photon emission computed tomography hole collimator (eg, to improve resolution, sensitivity, or B(SPECT [also abbreviated as SPET]) is a noninvasive tech- both). nique, which provides 3D functional information with high To overcome these limitations, very similar to CT, SPECT sensitivity and specificity. As with all nuclear medicine imag- uses computer algorithms to combine views from different ing procedures, it is based on the injection of tracer amounts angles to produce fully 3D images. SPECT has been pursued (nano- to picomoles, ie, much lower than the milli- to micro- since the early 60s,2 and brain scanning was one of its first molar levels that elicit a pharmacologic response) of a mole- applications3,4; during its infancy, SPECT was the only nonin- cule labeled with a radioactive isotope.
    [Show full text]
  • Information Brochure
    Information Brochure for submission of Expressions of Interest by Private Organizations/Institutions for installation of a PET-C T Scan Unit in the premises of Nilratan Sarkar Medical College & Hospital, 138A, A. J C BOSE Road, Kolkata-700 014 under Public Private Partnership. 2009-10 I. Background : In our State the Nilratan Sarkar Medical College and Hospital has the largest bed- capacity in its hospital. It is one of the largest hospitals in the eastern region of the country. It has vibrant units of Oncology, Neurology and Psychiatry, Cardiology and Pediatric. For sometime past it has been felt necessary that the pathological changes, long before those would be revealed, should be detected by some sort instrument/machine which would indicate future structural changes in tissues through structural imaging modalities like CT or MRI Scans. A study is also required which demonstrates more about the cellular level metabolic status of a disease than other types of imaging modalities. Hence, a decision has been taken that one PET-C T Scan machine should be installed in the said N R S Medical College for detecting the cellular level metabolic status of a disease including pathological changes going to occur. 2. The word PET means Positron Emission Tomography. It has been integrated with Computed Tomography (CT). Thus, Positron Emission Tomography-Computed Tomography (PET CT) has now emerged not only as important research tools but also as a very significant diagnostic imaging system in clinical medicine for evaluation of many diseases based on morphologic, physiologic, molecular and genetic markers of disease. The use of multimodality imaging systems and smart specific imaging agents will provide accurate diagnosis, treatment evaluation, surveillance and prognosis in individual patient.
    [Show full text]