Generating spectral dental panoramic images from single energy computed tomography volumes

Purpose: To implement a framework generating synthetic spectral panoramic images from single energy CT volumes. Using the framework output to compare the synthetic images against experimental spectral panoramic images for cross-verification. Methods: A simulation framework for generating synthetic spectral panoramic images from CT volumes is described. A cone beam CT scan of an anthropomorphic head phantom is used as input. An experimental spectral panoramic image of the same phantom is acquired. Results: The output of the framework of an anthropomorphic head phantom is compared against an experimental spectral panoramic image of the same phantom. The synthetic and experimental spectral panoramic images resemble each other considerably, especially the bone features. In the soft tissue images, there are some deviations, which are a result of the differences between the experimental and synthetic processing pipelines. Conclusions: It is demonstrated that generating synthetic spectral panoramic images from single energy CT volumes is possible. The synthetic images have many similarities with the experimental results, increasing the confidence in the correctness of the information contained within experimental spectral panoramic images and indicating that the synthetic images could be useful in further research

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

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

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