Improvements in four-dimensional and dual energy computed tomography

Dual energy and 4D computed tomography (CT) seek to address some of the limitations in traditional CT imaging. Dual energy CT, among other purposes, allows for the quantification and improved visualization of contrast materials, and 4D CT is often used in radiation therapy applications as it allows for the visualization and quantification of object motion. While much research has been done with these technologies, areas remain for potential improvement, both in preclinical and clinical settings, which will be explored in this dissertation. Preclinical dual energy cone-beam CT (CBCT) can benefit from wider separation between the peak energy of the two energy spectra. Using simulations and an x-ray source with a wide kVp range the contrast to noise ratio and Iodine concentration accuracy and precision were determined from Iodine material images. Improvements of 80% in CNR and 58% in precision were observed in the optimal energy pair of 60kVp/200kVp compared to a standard energy pair of 80kVp/140kVp. In 4D imaging, using projection data to obtain the required respiratory signal (“data driven”) can reduce setup complexity and cost of preclinical respiratory monitoring and reduce clinical 4D CT artifacts. Several clinical data driven 4D CBCT methods were modified for mice. Errors in projection sorting were within 4% of a breathing phase and were statistically less than the previous method for data driven 4D CBCT in mice. In clinical 4D CT, semi-automatically drawn target volumes and artifacts were compared between data driven and standard 4D CT images. Target volumes were shown to be statistically at least as large as standard contours, and artifacts were significantly reduced using the data driven technique. 4D CBCT is promising for use in evaluating tumor motion immediately prior to radiation treatment, but suffers from under sampling artifacts. An iterative volume of interest based reconstruction (I4D VOI) that aims to reduce artifacts without increases in computation time was compared to several other reconstruction techniques using a long scan patient data set. No statistical difference in tumor motion error was observed between I4D VOI and any of the other reconstruction methods. However, potential improvement over non-iterative VOI was demonstrated and computation time was reduced compared to TV minimization

Quantitative Analysis of Three-Dimensional Cone-Beam Computed Tomography Using Image Quality Phantoms

In the clinical setting, weight-bearing static 2D radiographic imaging and supine 3D radiographic imaging modalities are used to evaluate radiographic changes such as, joint space narrowing, subchondral sclerosis, and osteophyte formation. These respective imaging modalities cannot distinguish between tissues with similar densities (2D imaging), and do not accurately represent functional joint loading (supine 3D imaging). Recent advances in cone-beam CT (CBCT) have allowed for scanner designs that can obtain weight-bearing 3D volumetric scans. The purpose of this thesis was to analyze, design, and implement advanced imaging techniques to quantify image quality parameters of reconstructed image volumes generated by a commercially-available CBCT scanner, and a novel ceiling-mounted CBCT scanner. In addition, imperfections during rotation of the novel ceiling-mounted CBCT scanner were characterized using a 3D printed calibration object with a modification to the single marker bead method, and prospective geometric calibration matrices

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

Analysis of 3D Cone-Beam CT Image Reconstruction Performance on a FPGA

Efficient and accurate tomographic image reconstruction has been an intensive topic of research due to the increasing everyday usage in areas such as radiology, biology, and materials science. Computed tomography (CT) scans are used to analyze internal structures through capture of x-ray images. Cone-beam CT scans project a cone-shaped x-ray to capture 2D image data from a single focal point, rotating around the object. CT scans are prone to multiple artifacts, including motion blur, streaks, and pixel irregularities, therefore must be run through image reconstruction software to reduce visual artifacts. The most common algorithm used is the Feldkamp, Davis, and Kress (FDK) backprojection algorithm. The algorithm is computationally intensive due to the O(n4) backprojection step, running slowly with large CT data files on CPUs, but exceptionally well on GPUs due to the parallel nature of the algorithm. This thesis will analyze the performance of 3D cone-beam CT image reconstruction implemented in OpenCL on a FPGA embedded into a Power System

Accurate molecular imaging of small animals taking into account animal models, handling, anaesthesia, quality control and imaging system performance

Small-animal imaging has become an important technique for the development of new radiotracers, drugs and therapies. Many laboratories have now a combination of different small-animal imaging systems, which are being used by biologists, pharmacists, medical doctors and physicists. The aim of this paper is to give an overview of the important factors in the design of a small animal, nuclear medicine and imaging experiment. Different experts summarize one specific aspect important for a good design of a small-animal experiment