High-quality computed tomography using advanced model-based iterative reconstruction

Computed Tomography (CT) is an essential technology for the treatment, diagnosis, and study of disease, providing detailed three-dimensional images of patient anatomy. While CT image quality and resolution has improved in recent years, many clinical tasks require visualization and study of structures beyond current system capabilities. Model-Based Iterative Reconstruction (MBIR) techniques offer improved image quality over traditional methods by incorporating more accurate models of the imaging physics. In this work, we seek to improve image quality by including high-fidelity models of CT physics in a MBIR framework. Specifically, we measure and model spectral effects, scintillator blur, focal-spot blur, and gantry motion blur, paying particular attention to shift-variant blur properties and noise correlations. We derive a novel MBIR framework that is capable of modeling a wide range of physical effects, and use this framework with the physical models to reconstruct data from various systems. Physical models of varying degrees of accuracy are compared with each other and more traditional techniques. Image quality is assessed with a variety of metrics, including bias, noise, and edge-response, as well as task specific metrics such as segmentation quality and material density accuracy. These results show that improving the model accuracy generally improves image quality, as the measured data is used more efficiently. For example, modeling focal-spot blur, scintillator blur, and noise iicorrelations enables more accurate trabecular bone visualization and trabecular thickness calculation as compared to methods that ignore blur or model blur but ignore noise correlations. Additionally, MBIR with advanced modeling typically outperforms traditional methods, either with more accurate reconstructions or by including physical effects that cannot otherwise be modeled, such as shift-variant focal-spot blur. This work provides a means to produce high-quality and high-resolution CT reconstructions for a wide variety of systems with different hardware and geometries, providing new tradeoffs in system design, enabling new applications in CT, and ultimately improving patient care

Computational Imaging Approach to Recovery of Target Coordinates Using Orbital Sensor Data

This dissertation addresses the components necessary for simulation of an image-based recovery of the position of a target using orbital image sensors. Each component is considered in detail, focusing on the effect that design choices and system parameters have on the accuracy of the position estimate. Changes in sensor resolution, varying amounts of blur, differences in image noise level, selection of algorithms used for each component, and lag introduced by excessive processing time all contribute to the accuracy of the result regarding recovery of target coordinates using orbital sensor data. Using physical targets and sensors in this scenario would be cost-prohibitive in the exploratory setting posed, therefore a simulated target path is generated using Bezier curves which approximate representative paths followed by the targets of interest. Orbital trajectories for the sensors are designed on an elliptical model representative of the motion of physical orbital sensors. Images from each sensor are simulated based on the position and orientation of the sensor, the position of the target, and the imaging parameters selected for the experiment (resolution, noise level, blur level, etc.). Post-processing of the simulated imagery seeks to reduce noise and blur and increase resolution. The only information available for calculating the target position by a fully implemented system are the sensor position and orientation vectors and the images from each sensor. From these data we develop a reliable method of recovering the target position and analyze the impact on near-realtime processing. We also discuss the influence of adjustments to system components on overall capabilities and address the potential system size, weight, and power requirements from realistic implementation approaches

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

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

Sensory information processing (1 July 1976 - 31 March 1977)

technical reportThe student of human visual perception is often overwhelmed by the vast amount of data that has been accumulated from experiments performed within the last century or so. It is often difficult to understand why a certain experiment has been performed.Results from similar experiments sometimes seem to conflict. Further confusion results when the student encounters raging controversies, the resolution of which would seem to minimally advance our knowledge of how we see. The reason for all this trouble stems from the fact that s suitable superstructure providing organization and support of this accumulation of data does not exist, i.e. an adequate theory of perception is not in hand