Development and Application of Realistic Anatomical and Imaging Models for Virtual Clinical Trials in Computed Tomography

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2018

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Abstract

The purpose of this dissertation was to develop comprehensive toolsets for performing quality based virtual clinical trials in computed tomography. The developed toolsets in this dissertation enable rigorous quantification and evaluation of computed tomography scanners which not possible using ground-truth limited clinical trials or simplistic physical phantoms. This projection was outlined in three sections: 1) modeling high-resolution human models with intra-organ heterogeneities, 2) modeling a scanner-specific and realistic computed tomography simulator, and 3) performing a virtual clinical trial to evaluate and optimize geometrical imaging parameters in computed tomography.

In chapter 2, an anatomically-informed mathematical model was developed to extend the non-parenchyma structures, including airways, arteries, and veins to the level of terminal branches while avoiding intersections. A geometrical validation was done to ensure that the generated lung models have anatomical attributes close to morphometry studies. Additionally, a texture synthesis algorithm, informed by a high-resolution lung specimen, was used to develop a heterogenous parenchyma background within the lung regions.

In chapter 3, an algorithm, informed by a high-resolution bone dataset, was developed to model the trabecular and cortical bone within the human models. In chapter 4, a realistic and scanner-specific energy-integrating computed tomography simulator, DukeSim, was developed to synthesize projection images of the high-resolution human models developed in chapters 2 and 3. DukeSim calculates projection images using a combination of ray-tracing and Monte Carlo techniques. It accounts for the geometry and physics of a specific scanner. To validate DukeSim, clinical and simulated computed tomography scans of a phantom was imaged and quantitatively compared against each other. The results showed that DukeSim is capable of simulating computed tomography images with image quality metrics close to clinical images.

In chapter 5, DukeSim was extended to synthesize photon-counting projection images. Similar to chapter 4, the photon-counting feature of the DukeSim was validated by comparing the quality of the real and simulated images. The results showed that DukeSim is capable of simulating photon-counting computed tomography images with image quality metrics close to real images.

In chapter 6, the developed human and imaging models were integrated to perform a virtual clinical trial. The purpose of this chapter was to investigate the effects of beam collimation and pitch on image quality in computed tomography under different respiratory and cardiac motion levels. A realistic human model with added lung lesions was used to cardiac and respiratory motions. Each case was imaged using DukeSim at multiple pitches and beam collimations. The images were compared against the known ground truth using task-generic and task-specific metrics. All task-generic metrics degraded by increasing pitch. When imaged with motion, increasing pitch reduced motion artifacts. The image quality metrics remained largely unchanged with changes in beam collimations studied. Patient motion exhibited negative effects on the image quality metrics. The study concluded that while desirable for fast imaging, high pitch and large beam collimations can negatively affect image quality of CT images.

In conclusion, this dissertation provides a set of realistic toolsets that can be used to study, investigate, and optimize computed tomography technology and protocols in with a known-ground-truth, in a cost-effective manner, and without any radiation safety concerns.

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Abadi, Ehsan (2018). Development and Application of Realistic Anatomical and Imaging Models for Virtual Clinical Trials in Computed Tomography. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/18262.

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Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license.