Prospective Estimation of Radiation Dose and Image Quality for Optimized CT Performance

X-ray computed tomography (CT) is a non-invasive medical imaging technique that generates cross-sectional images by acquiring attenuation-based projection measurements at multiple angles. Since its first introduction in the 1970s, substantial technical improvements have led to the expanding use of CT in clinical examinations. CT has become an indispensable imaging modality for the diagnosis of a wide array of diseases in both pediatric and adult populations [1, 2]. Currently, approximately 272 million CT examinations are performed annually worldwide, with nearly 85 million of these in the United States alone [3]. Although this trend has decelerated in recent years, CT usage is still expected to increase mainly due to advanced technologies such as multi-energy [4], photon counting [5], and cone-beam CT [6].

Despite the significant clinical benefits, concerns have been raised regarding the population-based radiation dose associated with CT examinations [7]. From 1980 to 2006, the effective dose from medical diagnostic procedures rose six-fold, with CT contributing to almost half of the total dose from medical exposure [8]. For each patient, the risk associated with a single CT examination is likely to be minimal. However, the relatively large population-based radiation level has led to enormous efforts among the community to manage and optimize the CT dose.

As promoted by the international campaigns Image Gently and Image Wisely, exposure to CT radiation should be appropriate and safe [9, 10]. It is thus a responsibility to optimize the amount of radiation dose for CT examinations. The key for dose optimization is to determine the minimum amount of radiation dose that achieves the targeted image quality [11]. Based on such principle, dose optimization would significantly benefit from effective metrics to characterize radiation dose and image quality for a CT exam. Moreover, if accurate predictions of the radiation dose and image quality were possible before the initiation of the exam, it would be feasible to personalize it by adjusting the scanning parameters to achieve a desired level of image quality. The purpose of this thesis is to design and validate models to quantify patient-specific radiation dose prospectively and task-based image quality. The dual aim of the study is to implement the theoretical models into clinical practice by developing an organ-based dose monitoring system and an image-based noise addition software for protocol optimization.

More specifically, Chapter 3 aims to develop an organ dose-prediction method for CT examinations of the body under constant tube current condition. The study effectively modeled the anatomical diversity and complexity using a large number of patient models with representative age, size, and gender distribution. The dependence of organ dose coefficients on patient size and scanner models was further evaluated. Distinct from prior work, these studies use the largest number of patient models to date with representative age, weight percentile, and body mass index (BMI) range.

With effective quantification of organ dose under constant tube current condition, Chapter 4 aims to extend the organ dose prediction system to tube current modulated (TCM) CT examinations. The prediction, applied to chest and abdominopelvic exams, was achieved by combining a convolution-based estimation technique that quantifies the radiation field, a TCM scheme that emulates modulation profiles from major CT vendors, and a library of computational phantoms with representative sizes, ages, and genders. The prospective quantification model is validated by comparing the predicted organ dose with the dose estimated based on Monte Carlo simulations with TCM function explicitly modeled.

Chapter 5 aims to implement the organ dose-estimation framework in clinical practice to develop an organ dose-monitoring program based on a commercial software (Dose Watch, GE Healthcare, Waukesha, WI). In the first phase of the study we focused on body CT examinations, and so the patient’s major body landmark information was extracted from the patient scout image in order to match clinical patients against a computational phantom in the library. The organ dose coefficients were estimated based on CT protocol and patient size as reported in Chapter 3. The exam CTDIvol, DLP, and TCM profiles were extracted and used to quantify the radiation field using the convolution technique proposed in Chapter 4.

With effective methods to predict and monitor organ dose, Chapters 6 aims to develop and validate improved measurement techniques for image quality assessment. Chapter 6 outlines the method that was developed to assess and predict quantum noise in clinical body CT images. Compared with previous phantom-based studies, this study accurately assessed the quantum noise in clinical images and further validated the correspondence between phantom-based measurements and the expected clinical image quality as a function of patient size and scanner attributes.

Chapter 7 aims to develop a practical strategy to generate hybrid CT images and assess the impact of dose reduction on diagnostic confidence for the diagnosis of acute pancreatitis. The general strategy is (1) to simulate synthetic CT images at multiple reduced-dose levels from clinical datasets using an image-based noise addition technique; (2) to develop quantitative and observer-based methods to validate the realism of simulated low-dose images; (3) to perform multi-reader observer studies on the low-dose image series to assess the impact of dose reduction on the diagnostic confidence for multiple diagnostic tasks; and (4) to determine the dose operating point for clinical CT examinations based on the minimum diagnostic performance to achieve protocol optimization.

Chapter 8 concludes the thesis with a summary of accomplished work and a discussion about future research.

Functional Evaluation of Causal Mutations Identified in Human Genetic Studies

Human genetics has been experiencing a wave of genetic discoveries thanks to the development of several technologies, such as genome-wide association studies (GWAS), whole-exome sequencing, and whole genome sequencing. Despite the massive genetic discoveries of new variants associated with human diseases, several key challenges emerge following the genetic discovery. GWAS is known to be good at identifying the locus associated with the patient phenotype. However, the actually causal variants responsible for the phenotype are often elusive. Another challenge in human genetics is that even the causal mutations are already known, the underlying biological effect might remain largely ambiguous. Functional evaluation plays a key role to solve these key challenges in human genetics both to identify causal variants responsible for the phenotype, and to further develop the biological insights from the disease-causing mutations.

We adopted various methods to characterize the effects of variants identified in human genetic studies, including patient genetic and phenotypic data, RNA chemistry, molecular biology, virology, and multi-electrode array and primary neuronal culture systems. Chapter 1 is a broader introduction for the motivation and challenges for functional evaluation in human genetic studies, and the background of several genetics discoveries, such as hepatitis C treatment response, in which we performed functional characterization.

Chapter 2 focuses on the characterization of causal variants following the GWAS study for hepatitis C treatment response. We characterized a non-coding SNP (rs4803217) of IL28B (IFNL3) in high linkage disequilibrium (LD) with the discovery SNP identified in the GWAS. In this chapter, we used inter-disciplinary approaches to characterize rs4803217 on RNA structure, disease association, and protein translation.

Chapter 3 describes another avenue of functional characterization following GWAS focusing on the novel transcripts and proteins identified near the IL28B (IFNL3) locus. It has been recently speculated that this novel protein, which was named IFNL4, may affect the HCV treatment response and clearance. In this chapter, we used molecular biology, virology, and patient genetic and phenotypic data to further characterize and understand the biology of IFNL4. The efforts in chapter 2 and 3 provided new insights to the candidate causal variant(s) responsible for the GWAS for HCV treatment response, however, more evidence is still required to make claims for the exact causal roles of these variants for the GWAS association.

Chapter 4 aims to characterize a mutation already known to cause a disease (seizure) in a mouse model. We demonstrate the potential use of multi-electrode array (MEA) system for the functional characterization and drug testing on mutations found in neurological diseases, such as seizure. Functional characterization in neurological diseases is relatively challenging and available systematic tools are relatively limited. This chapter shows an exploratory research and example to establish a system for the broader use for functional characterization and translational opportunities for mutations found in neurological diseases.

Overall, this dissertation spans a range of challenges of functional evaluations in human genetics. It is expected that the functional characterization to understand human mutations will become more central in human genetics, because there are still many biological questions remaining to be answered after the explosion of human genetic discoveries. The recent advance in several technologies, including genome editing and pluripotent stem cells, is also expected to make new tools available for functional studies in human diseases.

Role of Hydrological Process Representation on Erosion, Deposition, and Sediment Yield Estimate

Soil erosion by water is a major driven force causing land degradation. Laboratory experiments, on-site field study, and suspended sediments measurements were major fundamental approaches to study the mechanisms of soil water erosion and to quantify the erosive losses during rain events. The experimental research faces the challenge to extent the result to a wider spatial scale. Soil water erosion modeling provides possible solutions for scaling problems in erosion research, and is of principal importance to better understanding the governing processes of water erosion. However, soil water erosion models were considered to have limited value in practice. Uncertainties in hydrological simulations are among the reasons that hindering the development of water erosion model. Hydrological models gained substantial improvement recently and several water erosion models took advantages of the improvement of hydrological models. It is crucial to know the impact of changes in hydrological processes modeling on soil erosion simulation.

This dissertation work first created an erosion modeling tool (GEOtopSed) that takes advantage of the comprehensive hydrological model (GEOtop). The newly created tool was then tested and evaluated at an experimental watershed. The GEOtopSed model showed its ability to estimate multi-year soil erosion rate with varied hydrological conditions. To investigate the impact of different hydrological representations on soil erosion simulation, a 11-year simulation experiment was conducted for six models with varied configurations. The results were compared at varied temporal and spatial scales to highlight the roles of hydrological feedbacks on erosion. Models with simplified hydrological representations showed agreement with GEOtopSed model on long temporal scale (longer than annual). This result led to an investigation for erosion simulation at different rainfall regimes to check whether models with different hydrological representations have agreement on the soil water erosion responses to the changing climate. Multi-year ensemble simulations with different extreme precipitation scenarios were conducted at seven climate regions. The differences in erosion simulation results showed the influences of hydrological feedbacks which cannot be seen by purely rainfall erosivity method.

[N]pT ensemble and finite-size-scaling study of the critical isostructural transition in the generalized exponential model of index 4.

First-order transitions of system where both lattice site occupancy and lattice spacing fluctuate, such as cluster crystals, cannot be efficiently studied by traditional simulation methods, which necessarily fix one of these two degrees of freedom. The difficulty, however, can be surmounted by the generalized [N]pT ensemble [J. Chem. Phys. 136, 214106 (2012)]. Here we show that histogram reweighting and the [N]pT ensemble can be used to study an isostructural transition between cluster crystals of different occupancy in the generalized exponential model of index 4 (GEM-4). Extending this scheme to finite-size scaling studies also allows us to accurately determine the critical point parameters and to verify that it belongs to the Ising universality class.