Browsing by Author "Segars, Paul"

Virtual imaging trials (VITs) are a growing part of medical imaging research. VITs are a powerful alternative to the current gold-standard for determining or verifying the efficacy of new technology in healthcare: the clinical trial. Prohibitively high expenses, multi-site standardization of protocols, and risks to the health of the trial’s patient population are all challenges associated with the clinical trial; conversely, these challenges highlight the strengths of virtualization, particularly with regard to evaluating medical imaging technologies.Virtual imaging requires a combination of virtual subjects, physics-based imaging simulation platforms, and virtual pathologies. Currently, most computational phantom organs and pathologies are segmented or generated from clinical CT images. With this approach, most computational organs and pathologies are necessarily static, comprising only a single instantaneous representation. Further, this static-anatomy–static-pathology approach does not address the underlying physiological constraints acting on the organs or their pathologies—making some imaging exams (e.g., perfusion, coronary angiography) difficult to simulate robustly. It also does not provide a clear path toward including anatomical and physiological (functional) detail at sub-CT resolution. This project aims to integrate high-resolution, dynamic features into computational human models. The focus is primarily an advanced model known as XCAT. These additions include healthy and progressive-disease anatomy and physiology, micron-level–resolution coronary artery lesions, and an array of pathologies. In particular, we focus on the physiology needed for CT perfusion studies, dynamic lesions, or coronary artery disease (CAD), and means to integrate each of these features into XCAT via custom software. The outcome is further to demonstrate the utility of each of these advances with representative simulated imaging. Chapter 1 presents a method using clinical information and physiological theory to develop a mathematical model that produces the liver vasculature within a given XCAT. The model can be used to simulate contrast perfusion by taking into account contrast position and concentration at an initial time t and the spatial extent of the contrast in the liver vasculature at subsequent times. The mathematical method enables the simulation of hepatic contrast perfusion in the presence or absence of abnormalities (e.g., focal or diffuse disease) for arbitrary imaging protocols, contrast concentrations, and virtual patient body habitus. The vessel growing method further generalizes to vascular models of other organs as it is based on a parameterized approach, allowing for flexible repurposing of the developed tool. Chapter 2 presents a method for using cardiac plaque histology and morphology data acquired at micron-level resolution to generate new, novel plaques informed by a large, original patient cohort. A methodology for curating and validating the anatomical and physiological realism was further applied to the synthesized plaques to ensure realism. This method was integrated with the XCAT heart and coronary artery models to allow simulated imaging of a wide variety of coronary artery plaques in varied orientations and with unique material distribution and composition. Generation of 200 unique plaques has been optimized to take as little as 5 seconds with GPU acceleration. This work enables future studies to optimize current and emerging CT imaging methods used to detect, diagnose, and treat coronary artery disease. Chapter 3 focuses on small-scale modeling of the internal structure of the bones of the chest. The internal structure of the bones appears as a diffuse but recognizable texture under medical imaging and corresponds to a complex physical structure tuned to meet the physical purpose of the bone (e.g., weight-bearing, protective structure, etc.). The project aimed to address the limitations of prior texture-based modelling by creating mathematically based fine bone structures. The method was used to generate realistic bone structures, defined as polygon meshes, with accurate morphological and topological detail for 45 chest bones for each XCAT phantom. This new method defines the spatial extent of the complementary bone–marrow structures that are the root cause of the characteristic image texture 1-4 and provides a transition from using image-informed characteristic power law textures to a ground-truth model with exact morphology—which we additionally paired with the DukeSim CT simulator5 and XCAT phantoms6 to produce radiography and CT images with physics-based bone textures. This work enables CT acquisition parameter optimization studies that can inform clinical image assessment of osteoporosis and bone fractures. Chapter 4 proposes a new model of lesion morphology and insertion and was created with the intent to be informed and validated by—rather than constrained by—imaging data. It additionally includes the new incorporation of biological data, intended to provide dynamic computational lung lesion models for use in CT simulation applications. Each chapter includes a section presenting an example application of the respective tools in virtual medical imaging. Chapter 5 concludes this work with a brief summary of the content and is followed by Appendices A–D. The appendices are organized by topic and contain a visual demonstration of the work in a series of high-resolution, full-page images.

Purpose: Radiation doses from computed tomography (CT) examinations have come under public and governmental scrutiny because of several recent misadministrations of radiation across the country. Current CT dosimetry methods in the clinic use standardized cylindrical water phantoms to measure radiation dose across various scanning protocols and different scanner manufacturers. These methods and equipment are too generalized to provide accurate risk assessment for patients of varying ages, genders, and anatomies. The advent of computer models based on real CT imaged anatomy has made patient specific and organ specific dosimetry achievable.

With a population of both pediatric and adult patient models comprised of a wide range of anatomies, Monte Carlo based dose calculations can be cataloged. A patient can receive a prospective dose estimation from a phantom within our population that best exhibits the patient's age and anatomical characteristics. Knowledge of organ size and location is essential to finding a proper match between the patient and the computer model. To this end, very little information is currently available regarding organ size and location across a diverse human population. The purpose of this study was to develop a predictive model to ascertain organ locations and volumes for pediatric and adult patients.

Methods: This study included 51 adults and 40 pediatrics from which Extended NURBS-based Cardiac-Torso (XCAT) phantoms were generated. Large organs were manually segmented from clinical CT data. The remaining organs and other anatomical structures were created by transforming an existing human model template to fit the framework of the segmented structures. The maximum and minimum points of the organs were recorded with respect to the axial distance from the tip of the sacrum. The axial width and midpoint for each organ were then determined. The organ volumes were also calculated. All three organ parameters were plotted as functions of patient age and weight for adults and patient age for pediatrics.

Results: The adult patients showed no statistically significant correlation between organ parameters and age and BMI. There were slight, positive linear trends with organ midpoint (max r2=0.365, mean r2=0.185) and organ volume (max r2=0.510, mean r2=0.183) versus adult patient weight. The height correlations were also positive for midpoint (r2=0.485, mean r2=0.271). Gaussian fits performed on probability density functions of adult organs resulted in r2-values ranging from 0.945 to 0.996. Pediatric patients demonstrated strong cube root relationships with organ midpoints (max r2=0.857, mean r2=0.790) and organ widths (max r2=0.905 , mean r2=0.564) versus age. Pediatric organ volumes showed positive linear relationships versus age (max r2=0.983, mean r2=0.701).

Conclusions: Adult patients exhibited small variations in organ volume and location with respect to weight, but no meaningful correlation existed between these parameters and age. Once adulthood is reached, organ morphology and positioning seems to remain static; however, clear trends are evident between pediatric age and organ volumes and locations. Such information can aid in the selection of an appropriate computer model that has the highest probability of mirroring the anatomy of a patient undergoing a clinical exam. Applications could also extend into comparing PET versus CT determination of organ volume and location.

Objectives: We develop machine-learning based methods to synthesize lung textures within computational phantoms for improved realism in simulating high-resolution patient CT imaging data to evaluate and improve imaging devices and techniques.Methods: We first optimized a previously developed technique designed using a Conditional Generative Adversarial Network (CGAN), Project 1. The optimized model was trained and validated using clinical CT data. Generated texture images were evaluated qualitatively and quantitatively comparing them to the original CT data as well as to results from the previous work. Using what we learned from Project 1, in Project 2, we trained and validated a new generator using high-resolution micro-CT data of the lungs. The new generator was evaluated in a similar fashion. Results: For Project 1, the model was unable to produce results better than the previous work; lung textures were found to be blurry and lacked detail. For Project 2, the trained generator was found capable of simulating variable 3D lung background textures similar to the micro-CT both qualitatively and quantitatively. Conclusion: The CGAN method developed in this work, based on micro-CT data, can greatly improve the realism of computational phantoms by adding high-resolution background textures to the lungs. Such anatomical detail is necessary to evaluate higher-resolution CT imaging methods such as photon-counting CT.