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Cone-Beam Computed Tomography for Neuro-Angiographic Imaging

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Cone-Beam Computed Tomography for Neuro-Angiographic Imaging CONE-BEAM COMPUTED TOMOGRAPHY FOR NEURO-ANGIOGRAPHIC IMAGING by Michael Mow A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Master of Science in Engineering Baltimore, Maryland May 2017 © 2017 Michael Mow All Rights Reserved Abstract Accurate and timely diagnosis and treatment of head trauma incidents, particularly ischemic stroke, rely on confident visualization of brain vasculature and accurate measurements of flow characteristics. Due to their low cost, high spatial resolution, small footprint, and adaptable geometry, cone-beam computed tomography (CBCT) systems have shown to be valuable for point of care applications that require timely diagnosis. However, image quality in CBCT scanners are potentially limited by slow rotation speed and are therefore challenged in the context of hemodynamic neurovascular imaging, as in CT Angiography (CTA) and CT Perfusion (CTP). This thesis details the advancement of neuro- angiographic imaging in CBCT systems using prior-image based reconstruction methods such as Reconstruction-of-Difference (RoD) to provide accurate visualization of cerebral vasculature despite slow image acquisition and/or sparse projection data. The performance of a novel CBCT system for high quality neuro- angiographic imaging was assessed using RoD, a prior-image-based reconstruction method. A digital simulation framework is presented that involves dynamic vasculature models and an accelerated forward projection method. Results indicate feasibility for CBCT angiography attained with a 5 second scan time, despite a limited number of projections (“sparse” projection data). Preliminary results in CTP imaging demonstrate the potential for perfusion parameter estimation and warrant future research using a 3D printed vessel phantom developed in this work. ii Thesis Committee MSE Advisor and Primary Reader: Jeffrey H. Siewerdsen, Ph.D. Professor, Department of Biomedical Engineering Johns Hopkins University Thesis Committee: Jeffrey H. Siewerdsen, Ph.D. Professor, Department of Biomedical Engineering Johns Hopkins University J. Webster Stayman, Ph.D. Assistant Professor, Department of Biomedical Engineering Johns Hopkins University Wojciech Zbijewski, Ph.D. Instructor, Department of Biomedical Engineering Johns Hopkins University iii Progress means getting nearer to the place you want to be. And if you have taken a wrong turning, then to go forward does not get you any nearer. If you are on the wrong road, progress means doing an about-turn and walking back to the right road; and in that case the man who turns back soonest is the most progressive man. ― C.S. Lewis iv Acknowledgements This dissertation represents not only my work over the past year and a half but is also a testimony to those who have sacrificed and given me the opportunity to be where I am today. My experience at Johns Hopkins University has been nothing short of amazing, and I am thankful for the incredible opportunity to study here. This dissertation is the result of these many remarkable individuals whom I wish to acknowledge. First, I would like to thank my advisor, Dr. Jeffrey Siewerdsen, for his mentorship, support, and patience through my graduate studies. His undeniable passion for science and dedication to the things he loves have been contagious to everyone around him, especially inspiring me to pursue excellence and remain perseverant in all my future endeavors. I am sincerely grateful to consider him a mentor in my life. I would also like to thank the other members of my thesis committee, Dr. Web Stayman and Dr. Wojciech Zbijewski, not only for their time and extreme patience, but for their continual support and guidance through my research. Thanks to all those in the I-STAR and AIAI Lab, especially Dr. Alejandro Sisneiga, Dr. Jennifer Xu, Dr. Hao Dang, and Mr. Pengwei Wu, for being amazing colleagues who have made coming to the lab and working on the Head Scanner Team a most enjoyable experience. I would also like to acknowledge all our collaborators at Johns Hopkins Hospital and Carestream Health for their immense contributions to v this project. I could not be more excited to see all our dedication and work come to fruition in the near future. I would like to thank my fellow leaders and volunteer staff at SSM (Phil Yang, Jen Saunders, Andrew Kim, Richard Meehan, Allen Jiang, Annie Mao, Josiah Yiu, Peter Tanaka, Lani Chung, Priya Parameswaran, Eileen Yu, Blaine Miller, and Jocelyn Chang) and the many others I have been blessed to serve alongside, for being more than family to me and supporting me through continual encouragement and prayer in my college life. Thank you to God for graciously showing me the incredible, undeserved love and new life I have found in Jesus Christ. Finally, with the utmost gratitude, I’d like to thank my amazing parents (Kerry and Susan Mow), my grandparents (Henry Mow and Chong Sik Sun), and my siblings (Jessica, Stephanie, Ryan, Evan, and Natalie) for their never-ending care, support, and sacrifice, allowing me to grow in character as an individual and be where I am today. Thank you and love to you all! Michael Mow May 5, 2017 vi Table of Contents Abstract ii Thesis Committee iii Acknowledgements v List of Tables ix List of Figures ix Chapter 1. Introduction 1 1.1 X-Ray and Computed Tomography Systems 1 1.1.1 X-Ray Imaging Physics 2 1.1.2 Computed Tomography 4 1.1.3 Cone-Beam CT Systems 4 1.2 CT Reconstruction Methods 5 1.2.1 Analytical Reconstruction: Filtered Backprojection 5 1.2.2 Model Based Iterative Reconstruction: Penalized Likelihood 6 1.3 Clinical Background and Motivation 8 1.3.1 Traumatic Brain Injury and Stroke 8 1.3.2 Ischemic Stroke 9 1.3.3 CT Angiography 9 1.3.4 CT Perfusion 10 1.4 Outline and Overview of Dissertation 10 Chapter 2. 3D Neurovascular Imaging with Cone-Beam CT 12 2.1 Introduction 12 2.2 CBCT System for High-Quality Imaging of the Head 13 2.3 Challenges with Cone-Beam CT Systems 14 2.4 Prior-Image Based Method: Reconstruction of Difference 17 2.5 Digital Simulation for CBCT Angiographic Imaging 20 2.5.1 Digital Brain Phantom Design 20 2.5.2 Vessel Enhancement Curves 22 vii 2.5.3 Accelerated Forward Projection Simulation 22 2.6 Experimental Methods 24 2.6.1 Reconstruction Parameter Optimization 25 2.6.2 Accuracy of Reconstructions: Severity of Occlusion 26 2.6.3 Sparsity versus Data Consistency 28 2.6.4 Fixed Volume of Iodine Contrast Agent 29 2.7 Results 30 2.7.1 Accelerated Forward Projection Simulation 30 2.7.2 Reconstruction Parameter Optimization 31 2.7.3 Accuracy of Reconstructions: Severity of Occlusion 37 2.7.4 “Sparsity” versus “Data Consistency” 41 2.7.5 Fixed Volume of Contrast Agent 44 2.8 Conclusions and Future Work 46 Chapter 3. Brain Perfusion Imaging with Cone-Beam CT 48 3.1 Introduction 48 3.2 Brain Perfusion Model and Image Simulation 49 3.2.1 Model of Time Attenuation Curves 50 3.2.2 Calculation of Perfusion Parameters 52 3.3 Experimental Methods: Simulation Studies 53 3.4 Results: Perfusion Imaging via Model-Based Image Reconstruction 54 3.5 Physical Phantom for Perfusion Imaging 58 3.6 Conclusions and Future Work 61 Chapter 4. Summary and Conclusions 62 4.1 Summary of Key Developments and Findings 62 4.1.1 Development of a Digital Simulation Framework 63 4.1.2 Feasibility of CBCT Angiographic Imaging 63 4.1.3 Preliminary Results on CBCT Perfusion Imaging 64 4.2 Looking Forward: Future Direction 65 Bibliography 66 viii List of Tables 2.1 Comparison of scan times and number of projections for various 15 rotation speeds of CBCT systems over a 180+fan rotation angle and fixed frame rate of 15 frames/second. 2.2 Brain phantom attenuation map. 21 2.3 Summary of acquisition parameters and system geometry for CT 24 Angiography simulation. 2.4 Comparison of run times of “brute force” versus the accelerated 31 forward projection methods. The accelerated forward projection method provides a 10-30x increase in computational speed. 2.5 Comparison of accuracy, false positive rate, and false negative rate for 38 PL and RoD reconstructions with and without occlusions for 70 projections. 3.1 Summary of acquisition parameters and system geometry for CBCT 49 perfusion imaging. 3.2 Ground truth perfusion parameters, including cerebral blood flow 51 (CBF), cerebral blood volume (CBV), and mean transit time (MTT) in healthy and infarct tissue regions. List of Figures 2.1 Cone-beam CT head scanner prototype for high-quality imaging of the 14 head under development at Johns Hopkins Hospital. 2.2 Illustration of the tradeoff between data consistency and sparsity for a 16 typical arterial enhancement curve. The pink rectangle represents a long scan time of 17.5 seconds resulting in a fully sampled dataset with low data consistency. The green rectangle represents a short scan time of 3.5 seconds resulting in a sparse dataset with high data consistency. 2.3 Flowchart of digital simulation framework for CT angiography. 20 2.4 Digital brain phantom design. a-b) Sagittal and axial slice of the 3D 21 brain phantom. c-d) 3D renderings of the vasculature mask. ix 2.5 Vasculature tree with (a) no occlusion, (b) a partial occlusion, and (c) 26 a full occlusion. 2.6 Enhancement curves with fixed 휇푚푎푥 of 70HU and variable FWHM 28 ranging from 5 to 20 seconds. 2.7 Enhancement curves with fixed volume of contrast (area under the 30 curve) and various FWHM set equal to half of the scan time. 2.8 RMSE as a function of cutoff frequency of the Hann apodization filter 32 for FBP reconstruction. Optimal cutoff frequency for each number of projection case is indicated by the square.
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