Washington University in St. Louis Washington University Open Scholarship All Theses and Dissertations (ETDs) January 2009 Development and Implementation of Fully 3D Statistical Image Reconstruction Algorithms for Helical CT and Half-Ring PET Insert System Daniel Keesing Washington University in St. Louis Follow this and additional works at: https://openscholarship.wustl.edu/etd Recommended Citation Keesing, Daniel, "Development and Implementation of Fully 3D Statistical Image Reconstruction Algorithms for Helical CT and Half- Ring PET Insert System" (2009). All Theses and Dissertations (ETDs). 427. https://openscholarship.wustl.edu/etd/427 This Dissertation is brought to you for free and open access by Washington University Open Scholarship. It has been accepted for inclusion in All Theses and Dissertations (ETDs) by an authorized administrator of Washington University Open Scholarship. For more information, please contact [email protected]. WASHINGTON UNIVERSITY IN ST. LOUIS School of Engineering and Applied Science Department of Biomedical Engineering Dissertation Examination Committee: Joseph A. O’Sullivan, Chair Yuan-Chuan Tai, Co-Chair Dennis L. Barbour DanielA.Low Lihong Wang Bruce R. Whiting DEVELOPMENT AND IMPLEMENTATION OF FULLY 3D STATISTICAL IMAGE RECONSTRUCTION ALGORITHMS FOR HELICAL CT AND HALF-RING PET INSERT SYSTEM by Daniel Brian Keesing A dissertation presented to the Graduate School of Arts and Sciences of Washington University in partial fulfillment of the requirements for the degree of Doctor of Philosophy August 2009 Saint Louis, Missouri copyright by Daniel Brian Keesing 2009 ABSTRACT OF THE DISSERTATION Development and Implementation of Fully 3D Statistical Image Reconstruction Algorithms for Helical CT and Half-Ring PET Insert System by Daniel Brian Keesing Doctor of Philosophy in Biomedical Engineering Washington University in St. Louis, 2009 Research Advisors: Joseph A. O’Sullivan and Yuan-Chuan Tai X-ray computed tomography (CT) and positron emission tomography (PET) have become widely used imaging modalities for screening, diagnosis, and image-guided treatment planning. Along with the increased clinical use are increased demands for high image quality with reduced ionizing radiation dose to the patient. Despite their significantly high computational cost, statistical iterative reconstruction algorithms are known to reconstruct high-quality images from noisy tomographic datasets. The overall goal of this work is to design statistical reconstruction software for clinical x-ray CT scanners, and for a novel PET system that utilizes high-resolution detec- tors within the field of view of a whole-body PET scanner. The complex choices involved in the development and implementation of image reconstruction algorithms are fundamentally linked to the ways in which the data is acquired, and they require detailed knowledge of the various sources of signal degradation. Both of the imaging modalities investigated in this work have their own set of challenges. However, by ii utilizing an underlying statistical model for the measured data, we are able to use a common framework for this class of tomographic problems. We first present the details of a new fully 3D regularized statistical reconstruction al- gorithm for multislice helical CT. To reduce the computation time, the algorithm was carefully parallelized by identifying and taking advantage of the specific symmetry found in helical CT. Some basic image quality measures were evaluated using mea- sured phantom and clinical datasets, and they indicate that our algorithm achieves comparable or superior performance over the fast analytical methods considered in this work. Next, we present our fully 3D reconstruction efforts for a high-resolution half-ring PET insert. We found that this unusual geometry requires extensive re- development of existing reconstruction methods in PET. We redesigned the major components of the data modeling process and incorporated them into our reconstruc- tion algorithms. The algorithms were tested using simulated Monte Carlo data and phantom data acquired by a PET insert prototype system. Overall, we have developed new, computationally efficient methods to perform fully 3D statistical reconstructions on clinically-sized datasets. iii Acknowledgments It has been a privilege working with my advisors, Jody O’Sullivan and Yuan-Chuan Tai, on their projects for these past several years. I have learned an incredible amount from them and their research groups. I am grateful for the trust they had in me to do much of my work independently. Both of them have always been eager to support me and provide me with excellent opportunities. I would like to thank my thesis committee for taking time out of their busy schedules to see me through this process. Kurt Thoroughman was my academic advisor in Biomedical Engineering. I would also like to acknowledge my colleagues and labmates (past and present) who have helped make this research possible, namely, Dave Politte, Bruce Whiting, Mike Harrod, Debashish Pal, Geoff San Antonio, Liangjun Xie, Norbert Agbeko, Heyu Wu, Sergey Komarov, and Tae Yong Song. In particular, I am fortunate to have worked closely with Debashish and to have taken part in many interesting discussions with him. John Young and Stefan Siegel at Siemens Medical Solutions in Knoxville have also been of great assistance regarding the half-ring insert PET system. I would most definitely be in a different area of research (or perhaps not even a graduate student at all) if I had not met Boris Hasselblatt and Misha Kilmer at Tufts University. I had done undergraduate research with both of them, and shortly discovered that I had a passion to work in the field of medical imaging. My parents and brother Jeff have provided me with unlimited love and support, even through tough times. They would have preferred to see me more often, but understood the commitments involved in pursuing graduate-level research. The rest of my family, but especially my aunt, have been extremely supportive as well. I have also met some wonderful friends at Wash U, and am very fortunate to be so close to them. Dick Wu in particular has been a truly remarkable friend and housemate these past five years. Finally, I would like to acknowledge my financial support from the National Science Foundation Graduate Research Fellowship Program. This work was also supported in part by NIH grant 5R01CA075371-08 (J. F. Williamson, PI), Susan G. Komen for the Cure grant BCTR0601279 (Y.-C. Tai, PI), and NIH grant 5R33CA110011-04 iv (Y.-C. Tai, PI). Support was additionally provided in part by the National Science Foundation through TeraGrid computational resources at the National Center for Supercomputing Applications (University of Illinois at Urbana-Champaign). Daniel Brian Keesing Washington University in Saint Louis August 2009 v Dedicated to my family, for their love and support every step of the way. vi Contents Abstract ...................................... ii Acknowledgments ................................ iv List of Tables ................................... x List of Figures .................................. xi 1 Introduction .................................. 1 1.1CTDataAcquisition........................... 3 1.2PETDataAcquisition.......................... 5 1.3VirtualPinholePET........................... 7 1.4OrganizationoftheDissertation..................... 10 2 Background .................................. 11 2.1ImageReconstructionOverview..................... 12 2.1.1 Reconstruction from Line Integral Data Model . 12 2.1.2 Reconstruction from Statistical Data Model . 15 2.1.3 Comparison of Analytical and Statistical Methods . 21 2.2SystemModeling............................. 23 2.2.1 CTData.............................. 23 2.2.2 PETData............................. 25 2.3HelicalCTReconstruction........................ 28 2.4IncompleteCTDataReconstruction.................. 31 2.5 Virtual Pinhole PET Systems and their Reconstruction . 33 2.5.1 Full-RingInsert.......................... 34 2.5.2 Half-RingInsert.......................... 34 2.6 Acceleration of Statistical Reconstruction Algorithms . 36 2.6.1 AlgorithmicSpeedup....................... 36 2.6.2 HardwareSpeedup........................ 38 2.7MainContributions............................ 39 3 Reconstruction of Multislice Helical CT Datasets .......... 42 3.1Theory................................... 42 3.1.1 Systemgeometry......................... 42 3.1.2 Statisticaldatamodel...................... 43 3.1.3 Imagereconstructionformulation................ 44 vii 3.2Fully3Dsystemmatrix.......................... 47 3.2.1 Derivation............................. 47 3.2.2 Fast computation using voxel traversal algorithm . 48 3.2.3 Symmetryandstoragedetails.................. 48 3.3Parallelizationscheme.......................... 51 3.3.1 Readinginthedata........................ 52 3.3.2 Forwardandbackprojections................... 52 3.3.3 Summingthepartialbackprojections.............. 54 3.3.4 Imageupdate........................... 54 3.4Experiments................................ 55 3.5Results................................... 57 3.5.1 Resolutionphantom....................... 57 3.5.2 OrderedsubsetsandFDKinitialization............. 58 3.5.3 Clinicaldatasets......................... 59 3.5.4 TimingPerformance....................... 60 3.6Discussion................................. 63 4 Reconstruction of Incomplete CT Datasets .............. 65 4.1Theory................................... 65 4.2Experiments...............................