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