Image Quality, Modeling, and Design for High-Performance Cone-Beam CT of the Head

Diagnosis and treatment of neurological and otolaryngological diseases rely heavily on visualization of fine, subtle anatomical structures in the head. In particular, high-quality head imaging at the point of care mitigates patient risk associated with transport and decreases time to diagnosis for time-sensitive diseases. Cone-beam computed tomography (CBCT) systems have found widespread adoption in diagnostic and image-guided procedures. Such systems exhibit potential for adaptation as point-of-care systems due to relatively low cost, mechanical simplicity, and inherently high spatial resolution, but are generally challenged by low contrast imaging tasks (e.g., visualization of tumors or hemorrhages). This thesis details the development and design of a CBCT imaging system with performance sufficient for high-quality imaging of the head and suitable to deployment at the point of care. The performance of a commercially available head-and-neck CBCT scanner was assessed to determine the potential of such systems for high-quality head imaging. Results indicated low-contrast visualization was challenged by high detector noise and scatter. Photon counting x-ray detectors (PCDs) were identified as a potential technology that could improve the low-contrast visualization, and an imaging performance model was developed to quantify their imaging performance. The model revealed important implications for energy resolution, noise, and spatial resolution as a function of energy threshold and charge sharing rejection. A new CBCT system dedicated to detection of low-contrast contrast intracranial hemorrhage was designed with guidance from an imaging chain model to optimize the system configuration (geometry, detector, x-ray source, etc.). The results indicated flat panel detectors (FPDs) were favorable due to a large field of view, but benefited from detector readout gain adjustments. Dual-gain detector readout was compared with use of bowtie filter in high-gain readout mode to investigate potential improvements to noise performance in FPDs. Finally, technical assessment of the prototype CBCT head scanner (with design based on guidance from the image quality model) indicated performance suitable for translation to clinical studies in the neurosciences critical care unit

IMPROVED IMAGE QUALITY IN CONE-BEAM COMPUTED TOMOGRAPHY FOR IMAGE-GUIDED INTERVENTIONS

In the past few decades, cone-beam computed tomography (CBCT) emerged as a rapidly developing imaging modality that provides single rotation 3D volumetric reconstruction with sub-millimeter spatial resolution. Compared to the conventional multi-detector CT (MDCT), CBCT exhibited a number of characteristics that are well suited to applications in image-guided interventions, including improved mechanical simplicity, higher portability, and lower cost. Although the current generation of CBCT has shown strong promise for high-resolution and high-contrast imaging (e.g., visualization of bone structures and surgical instrumentation), it is often believed that CBCT yields inferior contrast resolution compared to MDCT and is not suitable for soft-tissue imaging. Aiming at expanding the utility of CBCT in image-guided interventions, this dissertation concerns the development of advanced imaging systems and algorithms to tackle the challenges of soft-tissue contrast resolution. The presented material includes work encompassing: (i) a comprehensive simulation platform to generate realistic CBCT projections (e.g., as training data for deep learning approaches); (ii) a new projection domain statistical noise model to improve the noise-resolution tradeoff in model-based iterative reconstruction (MBIR); (iii) a novel method to avoid CBCT metal artifacts by optimization of the source-detector orbit; (iv) an integrated software pipeline to correct various forms of CBCT artifacts (i.e., lag, glare, scatter, beam hardening, patient motion, and truncation); (v) a new 3D reconstruction method that only reconstructs the difference image from the image prior for use in CBCT neuro-angiography; and (vi) a novel method for 3D image reconstruction (DL-Recon) that combines deep learning (DL)-based image synthesis network with physics-based models based on Bayesian estimation of the statical uncertainty of the neural network. Specific clinical challenges were investigated in monitoring patients in the neurological critical care unit (NCCU) and advancing intraoperative soft-tissue imaging capability in image-guided spinal and intracranial neurosurgery. The results show that the methods proposed in this work substantially improved soft-tissue contrast in CBCT. The thesis demonstrates that advanced imaging approaches based on accurate system models, novel artifact reduction methods, and emerging 3D image reconstruction algorithms can effectively tackle current challenges in soft-tissue contrast resolution and expand the application of CBCT in image-guided interventions

Contributions to the improvement of image quality in CBCT and CBμCT and application in the development of a CBμCT system

During the last years cone-beam x-ray CT (CBCT) has been established as a widespread imaging technique and a feasible alternative to conventional CT for dedicated imaging tasks for which the limited flexibility offered by conventional CT advises the development of dedicated designs. CBCT systems are starting to be routinely used in image guided radiotherapy; image guided surgery using C-arms; scan of body parts such as the sinuses, the breast or extremities; and, especially, in preclinical small-animal imaging, often coupled to molecular imaging systems. Despite the research efforts advocated to the advance of CBCT, the challenges introduced by the use of large cone angles and two-dimensional detectors are a field of vigorous research towards the improvement of CBCT image quality. Moreover, systems for small-animal imaging add to the challenges posed by clinical CBCT the need of higher resolution to obtain equivalent image quality in much smaller subjects. This thesis contributes to the progress of CBCT imaging by addressing a variety of issues affecting image quality in CBCT in general and in CBCT for small-animal imaging (CBμCT). As part of this work we have assessed and optimized the performance of CBμCT systems for different imaging tasks. To this end, we have developed a new CBμCT system with variable geometry and all the required software tools for acquisition, calibration and reconstruction. The system served as a tool for the optimization of the imaging process and for the study of image degradation effects in CBμCT, as well as a platform for biological research using small animals. The set of tools for the accurate study of CBCT was completed by developing a fast Monte Carlo simulation engine based on GPUs, specifically devoted to the realistic estimation of scatter and its effects on image quality in arbitrary CBCT configurations, with arbitrary spectra, detector response, and antiscatter grids. This new Monte Carlo engine outperformed current simulation platforms by more than an order of magnitude. Due to the limited options for simulation of spectra in microfocus x-ray sources used in CBμCT, we contributed in this thesis a new spectra generation model based on an empirical model for conventional radiology and mammography sources modified in accordance to experimental data. The new spectral model showed good agreement with experimental exposure and attenuation data for different materials. The developed tools for CBμCT research were used for the study of detector performance in terms of dynamic range. The dynamic range of the detector was characterized together with its effect on image quality. As a result, a new simple method for the extension of the dynamic range of flat-panel detectors was proposed and evaluated. The method is based on a modified acquisition process and a mathematical treatment of the acquired data. Scatter is usually identified as one of the major causes of image quality degradation in CBCT. For this reason the developed Monte Carlo engine was applied to the in-depth study of the effects of scatter for a representative range of CBCT embodiments used in the clinical and preclinical practice. We estimated the amount and spatial distribution of the total scatter fluence and the individual components within. The effect of antiscatter grids in improving image quality and in noise was also evaluated. We found a close relation between scatter and the air gap of the system, in line with previous results in the literature. We also observed a non-negligible contribution of forward-directed scatter that is responsible to a great extent for streak artifacts in CBCT. The spatial distribution of scatter was significantly affected by forward scatter, somewhat challenging the usual assumption that the scatter distribution mostly contains low-frequencies. Antiscatter grids showed to be effective for the reduction of cupping, but they showed a much lower performance when dealing with streaks and a shift toward high frequencies of the scatter distributions. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------A lo largo de los últimos años, el TAC de rayos X de haz cónico (CBCT, de “conebeam” CT) se ha posicionado como una de las técnicas de imagen más ampliamente usadas. El CBCT se ha convertido en una alternativa factible al TAC convencional en tareas de imagen específicas para las que la flexibilidad limitada ofrecida por este hace recomendable el desarrollo de sistemas de imagen dedicados. De esta forma, el CBCT está empezando a usarse de forma rutinaria en varios campos entre los que se incluyen la radioterapia guiada por imagen, la cirugía guiada por imagen usando arcos en C, imagen de partes de la anatomía en las que el TAC convencional no es apropiado, como los senos nasales, las extremidades o la mama, y, especialmente el campo de imagen preclínica con pequeño animal. Los sistemas CBCT usados en este último campo se encuentran habitualmente combinados con sistemas de imagen molecular. A pesar del trabajo de investigación dedicado al avance de la técnica CBCT en los últimos años, los retos introducidos por el uso de haces cónicos y de detectores bidimensionales son un campo candente para la investigación médica, con el objetivo de obtener una calidad de imagen equivalente o superior a la proporcionada por el TAC convencional. En el caso de imagen preclínica, a los retos generados por el uso de CBCT se une la necesidad de una mayor resolución de imagen que permita observar estructuras anatómicas con el mismo nivel de detalle obtenido para humanos. Esta tesis contribuye al progreso del CBCT mediante el estudio de usa serie de efectos que afectan a la calidad de imagen de CBCT en general y en el ámbito preclínico en particular. Como parte de este trabajo, hemos evaluado y optimizado el rendimiento de sistemas CBCT preclínicos en función de la tarea de imagen concreta. Con este fin se ha desarrollado un sistema CBCT para pequeños animales con geometría variable y todas las herramientas necesarias para la adquisición, calibración y reconstrucción de imagen. El sistema sirve como base para la optimización de protocolos de adquisición y para el estudio de fuentes de degradación de imagen además de constituir una plataforma para la investigación biológica en pequeño animal. El conjunto de herramientas para el estudio del CBCT se completó con el desarrollo de una plataforma acelerada de simulación Monte Carlo basada en GPUs, optimizada para la estimación de radiación dispersa en CBCT y sus efectos en la calidad de imagen. La plataforma desarrollada supera el rendimiento de las actuales en más de un orden de magnitud y permite la inclusión de espectros policromáticos de rayos X, de la respuesta realista del detector y de rejillas antiscatter. Debido a las escasas opciones ofrecidas por la literatura para la estimación de espectros de rayos X para fuentes microfoco usadas en imagen preclínica, en esta tesis se incluye el desarrollo de un nuevo modelo de generación de espectros, basado en un modelo existente para fuentes usadas en radiología y mamografía. El modelo fue modificado a partir de datos experimentales. La precisión del modelo presentado se comprobó mediante datos experimentales de exposición y atenuación para varios materiales. Las herramientas desarrolladas se usaron para estudiar el rendimiento de detectores de rayos tipo flat-panel en términos de rango dinámico, explorando los límites impuestos por el mismo en la calidad de imagen. Como resultado se propuso y evaluó un método para la extensión del rango dinámico de este tipo de detectores. El método se basa en la modificación del proceso de adquisición de imagen y en una etapa de postproceso de los datos adquiridos. El simulador Monte Carlo se empleó para el estudio detallado de la naturaleza, distribución espacial y efectos de la radiación dispersa en un rango de sistemas CBCT que cubre el espectro de aplicaciones propuestas en el entorno clínico y preclínico. Durante el estudio se inspeccionó la cantidad y distribución espacial de radiación dispersa y de sus componentes individuales y el efecto causado por la inclusión de rejillas antiscatter en términos de mejora de calidad de imagen y de ruido en la imagen. La distribución de radiación dispersa mostró una acentuada relación con la distancia entre muestra y detector en el equipo, en línea con resultados publicados previamente por otros autores. También se encontró una influencia no despreciable de componentes de radiación dispersa con bajos ángulos de desviación, poniendo en tela de juicio la tradicional asunción que considera que la distribución espacial de la radiación dispersa está formada casi exclusivamente por componentes de muy baja frecuencia. Las rejillas antiscatter demostraron ser efectivas para la reducción del artefacto de cupping, pero su efectividad para tratar artefactos en forma de línea (principalmente formados por radiación dispersa con bajo ángulo de desviación) resultó mucho menor. La inclusión de estas rejillas también enfatiza las componentes de alta frecuencia de la distribución espacial de la radiación dispersa

Development and Evaluation of a Stationary Head Computed Tomography Scanner

X-Ray Computed Tomography (CT) is a widely used 3D imaging technique, proving indispensable in the diagnosis of medical conditions and pathologies. However, virtually all of today’s state-of-the-art CT systems rely on a rotating gantry to acquire projections spanning up to 360 degrees around the head and/or body. By replacing the rotating source and detector with a stationary array of x-ray sources and line detectors, a CT scanner could be potentially constructed with a smaller footprint and faster scanning speed. The subject of this dissertation is the design, construction, and evaluation of a stationary head CT (s-HCT) scanner capable of diagnosis of stroke and head trauma patients in limited resource areas such as forward operating bases. By bringing the diagnostic CT scanning capability to the patient, survival rates could potentially be greatly improved through quicker delivery of appropriate treatments. The scanner is made possible by recent advances in technologies related to CT, including x-ray sensor technology, iterative reconstruction methods, and distributed x-ray sources. Recently, carbon nanotube (CNT) x-ray source arrays have been utilized in a number of medical and security applications. The unique electronic scanning ability afforded by these systems can removes the need for a rotating gantry, producing a stationary system which potentially is more mechanically robust and could provide diagnostic CT images in a smaller footprint, with little to no loss in image quality.The use of 3 linear x-ray source arrays naturally results in a triangular shape, representing a radical departure from a traditional (circular) source ring. The final construction of the prototype proves that circular objects can still be reconstructed accurately even though the geometry of the system is triangular. Furthermore, the prototype has been able to acquire all of the projection data in scan times comparable to those of commercial scanners (< 1min), indicating the CNT x-ray and s-HCT technologies are developed enough for clinical trials. As part of an initial evaluation, several objects are imaged in a phantom imaging study, with results demonstrating the temporal and spatial resolution, as well as the accuracy and noise associated with the 3D reconstruction output.Doctor of Philosoph

Relative Merits of 3D Visualization for the Detection of Subtle Lung Nodules

A new imaging modality called bi-plane correlation imaging (BCI) was examined to determine the merits of using BCI with stereoscopic visualization to detect subtle lung nodules. In the first aim of this project, the optimal geometry for conventional projection imaging applications was assessed using a theoretical model to develop generic results for MTF, NNPS, eDQE. The theoretical model was tested with a clinical system using two magnifications and two anthropomorphic chest phantoms to assess the modalities of single view CXR and stereo/BCI. Results indicated that magnification can potentially improve the signal and noise performance of digital images. Results also demonstrated that a cross over point occurs in the spatial frequency above and below which the effects of magnification differ indicating that there are task dependent tradeoffs associated with magnification. Results indicated that magnification can potentially improve the detection performance primarily due to the air gap which reduced scatter by 30-40%. For both anthropomorphic phantoms, at iso-dose, eDQE(0) for stereo/BCI was ~100 times higher than that for CXR. Magnification at iso-dose improved eDQE(0) by ~10 times for BCI. Increasing the dose did not improve results. The findings indicated that stereo/BCI with magnification may improve detection of subtle lung nodules compared to single view CXR. With quantitative results in place, a pilot clinical trial was constructed. Human subject data was acquired with a BCI acquisition system. Subjects were imaged in the PA position as well as two oblique angles. Realistic simulated lesions were added to a subset of subjects determined to be nodule free. A BCI CAD algorithm was also applied. In randomized readings, radiologists read the cases according to viewing protocol. For the radiologist trainees, the AUC of lesion detection was seen to improve by 2.8% (p &lt; 0.05) for stereoscopic viewing after monoscopic viewing compared to monoscopic viewing only. A 13% decrease in false positives was observed. Stereo/BCI as an adjunct modality was beneficial. However, the full potential of stereo/BCI as a replacement modality for single view chest x-ray may be realized with improved observer training, clinically relevant stereoscopic displays, and more challenging detection tasks.Doctor of Philosoph

Characterization of multiphase flows integrating X-ray imaging and virtual reality

Multiphase flows are used in a wide variety of industries, from energy production to pharmaceutical manufacturing. However, because of the complexity of the flows and difficulty measuring them, it is challenging to characterize the phenomena inside a multiphase flow. To help overcome this challenge, researchers have used numerous types of noninvasive measurement techniques to record the phenomena that occur inside the flow. One technique that has shown much success is X-ray imaging. While capable of high spatial resolutions, X-ray imaging generally has poor temporal resolution. This research improves the characterization of multiphase flows in three ways. First, an X-ray image intensifier is modified to use a high-speed camera to push the temporal limits of what is possible with current tube source X-ray imaging technology. Using this system, sample flows were imaged at 1000 frames per second without a reduction in spatial resolution. Next, the sensitivity of X-ray computed tomography (CT) measurements to changes in acquisition parameters is analyzed. While in theory CT measurements should be stable over a range of acquisition parameters, previous research has indicated otherwise. The analysis of this sensitivity shows that, while raw CT values are strongly affected by changes to acquisition parameters, if proper calibration techniques are used, acquisition parameters do not significantly influence the results for multiphase flow imaging. Finally, two algorithms are analyzed for their suitability to reconstruct an approximate tomographic slice from only two X-ray projections. These algorithms increase the spatial error in the measurement, as compared to traditional CT; however, they allow for very high temporal resolutions for 3D imaging. The only limit on the speed of this measurement technique is the image intensifier-camera setup, which was shown to be capable of imaging at a rate of at least 1000 FPS. While advances in measurement techniques for multiphase flows are one part of improving multiphase flow characterization, the challenge extends beyond measurement techniques. For improved measurement techniques to be useful, the data must be accessible to scientists in a way that maximizes the comprehension of the phenomena. To this end, this work also presents a system for using the Microsoft Kinect sensor to provide natural, non-contact interaction with multiphase flow data. Furthermore, this system is constructed so that it is trivial to add natural, non-contact interaction to immersive visualization applications. Therefore, multiple visualization applications can be built that are optimized to specific types of data, but all leverage the same natural interaction. Finally, the research is concluded by proposing a system that integrates the improved X-ray measurements, with the Kinect interaction system, and a CAVE automatic virtual environment (CAVE) to present scientists with the multiphase flow measurements in an intuitive and inherently three-dimensional manner

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

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

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