Image-Guided Interventions Using Cone-Beam CT: Improving Image Quality with Motion Compensation and Task-Based Modeling

Cone-beam CT (CBCT) is an increasingly important modality for intraoperative 3D imaging in interventional radiology (IR). However, CBCT exhibits several factors that diminish image quality — notably, the major challenges of patient motion and detectability of low-contrast structures — which motivate the work undertaken in this thesis. A 3D–2D registration method is presented to compensate for rigid patient motion. The method is fiducial-free, works naturally within standard clinical workflow, and is applicable to image-guided interventions in locally rigid anatomy, such as the head and pelvis. A second method is presented to address the challenge of deformable motion, presenting a 3D autofocus concept that is purely image-based and does not require additional fiducials, tracking hardware, or prior images. The proposed method is intended to improve interventional CBCT in scenarios where patient motion may not be sufficiently managed by immobilization and breath-hold, such as the prostate, liver, and lungs. Furthermore, the work aims to improve the detectability of low-contrast structures by computing source–detector trajectories that are optimal to a particular imaging task. The approach is applicable to CBCT systems with the capability for general source–detector positioning, as with a robotic C-arm. A “task-driven” analytical framework is introduced, various objective functions and optimization methods are described, and the method is investigated via simulation and phantom experiments and translated to task-driven source–detector trajectories on a clinical robotic C-arm to demonstrate the potential for improved image quality in intraoperative CBCT. Overall, the work demonstrates how novel optimization-based imaging techniques can address major challenges to CBCT image quality

PREDICTION AND CONTROL OF IMAGE PROPERTIES IN ADVANCED COMPUTED TOMOGRAPHY

Computed Tomography (CT) is an important technique that is in widespread use for disease diagnosis, monitoring, and interventional procedures. There are many varieties of CT including cone-beam CT (CBCT) that has exceptional high spatial resolution and spectral CT that incorporates energy-dependent measurements for advanced material discrimination. The goal of this research is to quantify image properties using a prospective prediction framework for advanced reconstruction in CBCT and spectral CT systems. These predictors analyze the dependencies of image properties on system configuration, acquisition strategy, and reconstruction regularization design. The prospective estimation of image properties facilitates novel system and acquisition design, adaptive and task-driven imaging, and tuning of regularization for robust and reliable performance. The proposed research quantifies the image properties of model-based iterative reconstruction (MBIR) in CBCT and model-based material decomposition (MBMD) in spectral CT, including spatial resolution, the generalized response to local perturbations, and noise correlation. Predictions are derived with a realistic system model including physical blur, noise correlation, and a poly-energetic model that applies to a variety of spectral CT protocols. Reconstruction methods combining data statistical fidelity and various advanced regularization designs are explored. Prediction accuracy is validated with measured image properties in both simulation and physical experiments. The theoretical understanding is applied to applications with prospective reconstruction regularization design

Task-Driven Trajectory Design for Endovascular Embolization

Computed Tomography (CT) is one of the most useful and widely applied imaging modalities, employed in both diagnostic and treatment planning purposes in the medical field. Circular and spiral acquisition trajectories are traditionally employed and work well in many cases. The advent of technologies such as robotic C-arms in interventional imaging allow for more complex data acquisitions, which enables potential improvements in image quality, increased field of view, and sampling. This capability has particular potential crucial in interventional cases where images may be compromised by complex anatomy or surgical tools. In this work, we present a paradigm that uses custom non-circular orbits and prior patient information along with segmentation and registration techniques to account for surgical tools and/or implants, to improve image quality. The framework leverages the anatomical model to optimize a parameterized source-detector trajectory for a variety of specific imaging tasks. We propose an overall workflow for orbit customization with investigations of the various workflow stages as well as the overall performance of the framework

Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization

We propose a model-based image reconstruction method for photoacoustic tomography(PAT) involving a novel form of regularization and demonstrate its ability to recover good quality images from significantly reduced size datasets. The regularization is constructed to suit the physical structure of typical PAT images. We construct it by combining second-order derivatives and intensity into a non-convex form to exploit a structural property of PAT images that we observe: in PAT images, high intensities and high second-order derivatives are jointly sparse. The specific form of regularization constructed here is a modification of the form proposed for fluorescence image restoration. This regularization is combined with a data fidelity cost, and the required image is obtained as the minimizer of this cost. As this regularization is non-convex, the efficiency of the minimization method is crucial in obtaining artifact-free reconstructions. We develop a custom minimization method for efficiently handling this non-convex minimization problem. Further, as non-convex minimization requires a large number of iterations and the PAT forward model in the data-fidelity term has to be applied in the iterations, we propose a computational structure for efficient implementation of the forward model with reduced memory requirements. We evaluate the proposed method on both simulated and real measured data sets and compare them with a recent reconstruction method that is based on a well-known fast iterative shrinkage threshold algorithm (FISTA).Comment: This manuscript has been published in Journal of Instrumentatio

Artificial Intelligence in Radiation Therapy

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy

Model-Based Iterative Reconstruction in Cone-Beam Computed Tomography: Advanced Models of Imaging Physics and Prior Information

Cone-beam computed tomography (CBCT) represents a rapidly developing imaging modality that provides three-dimensional (3D) volumetric images with sub-millimeter spatial resolution and soft-tissue visibility from a single gantry rotation. CBCT tends to have small footprint, mechanical simplicity, open geometry, and low cost compared to conventional diagnostic CT. Because of these features, CBCT has been used in a variety of specialty diagnostic applications, image-guided radiation therapy (on-board CBCT), and surgical guidance (e.g., C-arm based CBCT). However, the current generation of CBCT systems face major challenges in low-contrast, soft-tissue image quality – for example, in the detection of acute intracranial hemorrhage (ICH), which requires a fairly high level of image uniformity, spatial resolution, and contrast resolution. Moreover, conventional approaches in both diagnostic and image-guided interventions that involve a series of imaging studies fail to leverage the wealth of patient-specific anatomical information available from previous scans. Leveraging the knowledge gained from prior images holds the potential for major gains in image quality and dose reduction. Model-based iterative reconstruction (MBIR) attempts to make more efficient use of the measurement data by incorporating a forward model of physical detection processes. Moreover, MBIR allows incorporation of various forms of prior information into image reconstruction, ranging from image smoothness and sharpness to patient-specific anatomical information. By leveraging such advantages, MBIR has demonstrated improved tradeoffs between image quality and radiation dose in multi-detector CT in the past decade and has recently shown similar promise in CBCT. However, the full potential of MBIR in CBCT is yet to be realized. This dissertation explores the capabilities of MBIR in improving image quality (especially low-contrast, soft-tissue image quality) and reducing radiation dose in CBCT. The presented work encompasses new MBIR methods that: i) modify the noise model in MBIR to compensate for noise amplification from artifact correction; ii) design regularization by explicitly incorporating task-based imaging performance as the objective; iii) mitigate truncation effects in a computationally efficient manner; iv) leverage a wealth of patient-specific anatomical information from a previously acquired image; and v) prospectively estimate the optimal amount of prior image information for accurate admission of specific anatomical changes. Specific clinical challenges are investigated in the detection of acute ICH and surveillance of lung nodules. The results show that MBIR can substantially improve low-contrast, soft-tissue image quality in CBCT and enable dose reduction techniques in sequential imaging studies. The thesis demonstrates that novel MBIR methods hold strong potential to overcome conventional barriers to CBCT image quality and open new clinical applications that would benefit from high-quality 3D imaging

High-Resolution Quantitative Cone-Beam Computed Tomography: Systems, Modeling, and Analysis for Improved Musculoskeletal Imaging

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. 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 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