Collimation of a D-D Neutron Generator for Clinical Implementation of Neutron Stimulated Emission Computed Tomography: a Monte Carlo Study

This work is an investigation into collimator designs for a deuterium-deuterium (DD) neutron generator for an inexpensive and compact neutron imaging system that can be implemented in a hospital. The envisioned application is for a spectroscopic imaging technique called neutron stimulated emission computed tomography (NSECT).

Previous NSECT studies have been performed using a Van-de-Graaff accelerator at the Triangle Universities Nuclear Laboratory (TUNL) in Duke University. This facility has provided invaluable research into the development of NSECT. To transition the current imaging method into a clinically feasible system, there is a need for a high-intensity fast neutron source that can produce collimated beams. The DD neutron generator from Adelphi Technologies Inc. is being explored as a possible candidate to provide the uncollimated neutrons. This DD generator is a compact source that produces 2.5 MeV fast neutrons with intensities of 1012 n/s (4π). The neutron energy is sufficient to excite most isotopes of interest in the body with the exception of carbon and oxygen. However, a special collimator is needed to collimate the 4π neutron emission into a narrow beam. This work describes the development and evaluation of a series of collimator designs to collimate the DD generator for narrow beams suitable for NSECT imaging.

A neutron collimator made of high-density polyethylene (HDPE) and lead was modeled and simulated using the GEANT4 toolkit. The collimator was designed as a 52 x 52 x 52 cm3 HDPE block coupled with 1 cm lead shielding. Non-tapering (cylindrical) and tapering (conical) opening designs were modeled into the collimator to permit passage of neutrons. The shape, size, and geometry of the aperture were varied to assess the effects on the collimated neutron beam. Parameters varied were: inlet diameter (1-5 cm), outlet diameter (1-5 cm), aperture diameter (0.5-1.5 cm), and aperture placement (13-39 cm). For each combination of collimator parameters, the spatial and energy distributions of neutrons and gammas were tracked and analyzed to determine three performance parameters: neutron beam-width, primary neutron flux, and the output quality. To evaluate these parameters, the simulated neutron beams are then regenerated for a NSECT breast scan. Scan involved a realistic breast lesion implanted into an anthropomorphic female phantom.

This work indicates potential for collimating and shielding a DD neutron generator for use in a clinical NSECT system. The proposed collimator designs produced a well-collimated neutron beam that can be used for NSECT breast imaging. The aperture diameter showed a strong correlation to the beam-width, where the collimated neutron beam-width was about 10% larger than the physical aperture diameter. In addition, a collimator opening consisting of a tapering inlet and cylindrical outlet allowed greater neutron throughput when compared to a simple cylindrical opening. The tapering inlet design can allow additional neutron throughput when the neck is placed farther from the source. On the other hand, the tapering designs also decrease output quality (i.e. increase in stray neutrons outside the primary collimated beam). All collimators are cataloged in measures of beam-width, neutron flux, and output quality. For a particular NSECT application, an optimal choice should be based on the collimator specifications listed in this work.

Resonance Energy Transfer-Based Molecular Switch Designed Using a Systematic Design Process Based on Monte Carlo Methods and Markov Chains

A RET network consists of a network of photo-active molecules called chromophores that can participate in inter-molecular energy transfer called resonance energy transfer (RET). RET networks are used in a variety of applications including cryptographic devices, storage systems, light harvesting complexes, biological sensors, and molecular rulers. In this dissertation, we focus on creating a RET device called closed-diffusive exciton valve (C-DEV) in which the input to output transfer function is controlled by an external energy source, similar to a semiconductor transistor like the MOSFET. Due to their biocompatibility, molecular devices like the C-DEVs can be used to introduce computing power in biological, organic, and aqueous environments such as living cells. Furthermore, the underlying physics in RET devices are stochastic in nature, making them suitable for stochastic computing in which true random distribution generation is critical.

In order to determine a valid configuration of chromophores for the C-DEV, we developed a systematic process based on user-guided design space pruning techniques and built-in simulation tools. We show that our C-DEV is 15x better than C-DEVs designed using ad hoc methods that rely on limited data from prior experiments. We also show ways in which the C-DEV can be improved further and how different varieties of C-DEVs can be combined to form more complex logic circuits. Moreover, the systematic design process can be used to search for valid chromophore network configurations for a variety of RET applications.

We also describe a feasibility study for a technique used to control the orientation of chromophores attached to DNA. Being able to control the orientation can expand the design space for RET networks because it provides another parameter to tune their collective behavior. While results showed limited control over orientation, the analysis required the development of a mathematical model that can be used to determine the distribution of dipoles in a given sample of chromophore constructs. The model can be used to evaluate the feasibility of other potential orientation control techniques.

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.