HIGH-RESOLUTION QUANTITATIVE CONE-BEAM COMPUTED TOMOGRAPHY: SYSTEMS, MODELING, AND ANALYSIS FOR IMPROVED MUSCULOSKELETAL IMAGING by Qian Cao A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland November 2020 © 2020 Qian Cao All rights reserved Abstract This dissertation applies accurate models of imaging physics, new high-resolution imaging hardware, and novel image analysis techniques to benefit quantitative applications of x-ray CT in in vivo assessment of bone health. Imaging physics can be used to account for nonidealities in image formation and to optimize imaging systems; improved spatial resolution enables characterization of fine anatomical structures; novel image analysis techniques provide more consistent quantitative biomarkers. We pursue three Aims: 1. Characterization of macroscopic joint space morphology, 2. Estimation of bone mineral density (BMD), and 3. Visualization of bone microstructure. This work contributes to the development of extremity cone-beam CT (CBCT), a compact system for musculoskeletal (MSK) imaging. Joint space morphology is characterized by a model which draws an analogy between the bones of a joint and the plates of a capacitor. Virtual electric field lines connecting the two surfaces of the joint are computed as a surrogate measure of joint space width, creating a rich, non-degenerate, adaptive map of the joint space. We showed that by using such maps, a classifier can outperform radiologist measurements at identifying osteoarthritic patients in a set of CBCT scans. Quantitative BMD accuracy is achieved by combining a polyenergetic model-based iterative reconstruction (MBIR) method with fast Monte Carlo (MC) scatter estimation. On a benchtop system emulating extremity CBCT, we validated BMD accuracy and ii reproducibility via a series of phantom studies involving inserts of known mineral concentrations and a cadaver specimen. High-resolution imaging is achieved using a complementary metal-oxide semiconductor (CMOS)-based x-ray detector featuring small pixel size and low readout noise. A cascaded systems model was used to performed task-based optimization to determine optimal detector scintillator thickness in nominal extremity CBCT imaging conditions. We validated the performance of a prototype scanner incorporating our optimization result. Strong correlation was found between bone microstructure metrics obtained from the prototype scanner and µCT gold standard for trabecular bone samples from a cadaver ulna. Additionally, we devised a multiresolution reconstruction scheme allowing fast MBIR to be applied to large, high-resolution projection data. To model the full scanned volume in the reconstruction forward model, regions outside a finely sampled region-of- interest (ROI) are downsampled, reducing runtime and cutting memory requirements while maintaining image quality in the ROI. iii Dissertation Committee Wojciech Zbijewski, Ph.D. (advisor) Assistant Professor, Department of Biomedical Engineering Johns Hopkins University Jeffrey H. Siewerdsen, Ph.D. (co-advisor) Professor, Department of Biomedical Engineering Johns Hopkins University J. Webster Stayman, Ph.D. Associate Professor, Department of Biomedical Engineering Johns Hopkins University Jerry L. Prince Professor, Department of Electrical and Computer Engineering Johns Hopkins University iv Acknowledgements I am deeply indebted to many years of phenomenal mentorship received at Hopkins. It is especially awe-inspiring to experience the inflationary expansion of the I-STAR universe and witness the ever-growing constellation of research projects. The faculty of I- STAR and AIAI are all brilliant engineer-scientists as well as educators. It is nothing short of a privilege to work in such a collaborative and stimulating environment. First, I would like to thank my advisor, Wojciech Zbijewski for leading me to many scenic views on this journey, and for being patient and nurturing along the way. I would like to thank my co- advisor, Jeffrey Siewerdsen, for setting high standards in research and communication, and for his highly contagious enthusiasm on all things medical imaging. I would also like to thank Webster Stayman for many enlightening discussions on modeling, image reconstruction and other interesting ideas. I would like to thank Jerry Prince for serving on my thesis committee. Additionally, my projects were made possible by many clinical collaborators of the lab: Drs. Shadpour Demehri, Greg Osgood, and Gaurav Thawait. Prototyping of the extremity CBCT is made possible by a team of industry partners at Carestream, including Drs. John Yorkston, Xiaohui Wang, Weidong Huang, Bill Snyder, and many others. The work was also supported by grants NIH 1R01-EB-018896 and NIH 1R21-AR-062293. I would like to thank the Howard Hughes Medical Institute (HHMI) for supporting me through 3 years of my PhD. v I would also like to thank my friends and colleagues. Many have helped me deepen my understanding of the field: Jennifer Xu and Grace Gang, for a wealth of insights into cascaded systems analysis; Ali Uneri, for his speedy GPU projectors and tips on coding in CUDA; Alex Sisniega, for his Monte Carlo scatter simulation and motion correction libraries; Hao Dang and Steven Tilley II, who I have often turned to for questions about reconstruction; Sureerat (Ja) Reaugamornrat, who have helped me with image processing and registration. I need to thank: Niral Sheth, for staying late with me in the lab; Michael Brehler and Sarah Capostagno, for taking me on their morning runs; Tharindu De Silva, for the best Sri Lankan cuisine. I have also benefitted greatly from the company of Pengwei Wu, Wenying Wang, Esme Zhang, Runze Han, Shalini Subramanian, Matt Tivnan, Stephen Liu, Michael Ketcha, as well as visiting scholars Matthew Jacobson, Hao Zhang, Joseph Gorres, Amir Pourmorteza, Ashwin Mathews, Adam Wang, and Shiyu Xu. vi Dedication: For my parents. vii Table of Contents Abstract ............................................................................................................................... ii Dissertation Committee ..................................................................................................... iv Acknowledgements ..............................................................................................................v List of Tables ................................................................................................................... xiii List of Figures .................................................................................................................. xiv List of Abbreviations ....................................................................................................... xix Chapter I Cone-Beam Computed Tomography: Systems and Models for Quantitative MSK Imaging.......................................................................................................................1 I.A Quantitative Biomarkers for MSK Imaging ......................................................1 I.B Dedicated CBCT Systems for MSK Imaging ....................................................2 I.B.1 Clinical Realizations ...........................................................................2 I.B.2 X-ray Production and Interaction ........................................................4 I.C Detector Characterization and Modeling ...........................................................8 I.C.1 Indirect X-ray Detectors .....................................................................8 I.C.2 Detector Characterization and Task-Based Assessment of Performance ...............................................................................................11 I.C.2.1 Statistical Description of X-ray Detectors .........................11 I.C.2.2 Task-based Figures of Merit ..............................................15 I.C.2.3 Cascaded Systems Modeling .............................................16 I.D Tomographic Reconstruction ...........................................................................20 I.D.1 Tomographic Reconstruction as an Inverse Problem .......................20 I.D.2 Analytical Reconstruction ................................................................21 viii I.D.3 Model-Based Iterative Reconstruction .............................................23 I.D.3.1 Data Fidelity ..................................................................................23 I.D.3.2 Regularization ....................................................................25 I.D.4 Application of MBIR in Quantitative Imaging .................................26 I.E Quantitative Image Analysis ............................................................................28 I.F Thesis Outline ...................................................................................................33 Chapter II An Electrostatics-Inspired Model for Quantification and Analysis of Joint Macrostructure ...................................................................................................................35 II.A Introduction ....................................................................................................35 II.B Methods ..........................................................................................................37 II.B.1 The Electrostatic Model for Joint Space Analysis ...........................37 II.B.2 Application to Knee Joint Morphology ...........................................41