Quantum optimization algorithms for CT image segmentation from X-ray data

Computed tomography (CT) is an important imaging technique used in medical analysis of the internal structure of the human body. Previously, image segmentation methods were required after acquiring reconstructed CT images to obtain segmented CT images which made it susceptible to errors from both reconstruction and segmentation algorithms. However, this paper introduces a new approach using an advanced quantum optimization algorithm called quadratic unconstrained binary optimization (QUBO). This algorithm enables acquisition of segmented CT images from X-ray projection data with minimized discrepancies between experimentally obtained sinograms and quantized sinograms derived from quantized segmented CT images using the Radon transform. This study utilized D-Wave's hybrid solver system for verification on real-world X-ray data.Comment: 7 Pages, 3 figure

Efficient methodologies for system matrix modelling in iterative image reconstruction for rotating high-resolution PET

A fully 3D iterative image reconstruction algorithm has been developed for high-resolution PET cameras composed of pixelated scintillator crystal arrays and rotating planar detectors, based on the ordered subsets approach. The associated system matrix is precalculated with Monte Carlo methods that incorporate physical effects not included in analytical models, such as positron range effects and interaction of the incident gammas with the scintillator material. Custom Monte Carlo methodologies have been developed and optimized for modelling of system matrices for fast iterative image reconstruction adapted to specific scanner geometries, without redundant calculations. According to the methodology proposed here, only one-eighth of the voxels within two central transaxial slices need to be modelled in detail. The rest of the system matrix elements can be obtained with the aid of axial symmetries and redundancies, as well as in-plane symmetries within transaxial slices. Sparse matrix techniques for the non-zero system matrix elements are employed, allowing for fast execution of the image reconstruction process. This 3D image reconstruction scheme has been compared in terms of image quality to a 2D fast implementation of the OSEM algorithm combined with Fourier rebinning approaches. This work confirms the superiority of fully 3D OSEM in terms of spatial resolution, contrast recovery and noise reduction as compared to conventional 2D approaches based on rebinning schemes. At the same time it demonstrates that fully 3D methodologies can be efficiently applied to the image reconstruction problem for high-resolution rotational PET cameras by applying accurate pre-calculated system models and taking advantage of the system's symmetries

Electron Beam X-Ray Computed Tomography for Multiphase Flows and An Experimental Study of Inter-channel Mixing

This thesis consists of two parts. In the first, a high speed X-ray Computed Tomography (CT) system for multiphase flows is developed. X-ray Computed Tomography (CT) has been employed in the study of multiphase flows. The systems developed to date often have excellent spatial resolution at the expense of poor temporal resolution. Hence, X-ray CT has mostly been employed to examining time averaged phase distributions. In the present work, we report on the development of a Scanning Electron Beam X-ray Tomography (SEBXT) CT system that will allow for much higher time resolution with acceptable spatial resolution. The designed system, however, can have issues such as beam-hardening and limited angle artifacts. In the present study, we developed a high speed, limited angle SEBXT system along with a new CT reconstruction algorithm designed to enhance the CT reconstruction results of such system. To test the performance of the CT system, we produced example CT reconstruction results for two test phantoms based on the actual measured sinograms and the simulated sinograms. The second part examines, the process by which fluid mixes between two parallel flow channels through a narrow gap. This flow is a canonical representation of the mixing and mass transfer processes that often occur in thermo-hydraulic systems. The mixing can be strongly related to the presence of large-scale periodic flow structures that form within the gap. In the present work, we have developed an experimental setup to examine the single-phase mixing through the narrow rectangular gaps connecting two rectangular channels. Our goal is to elucidate the underlying flow processes responsible for inter-channel mixing, and to produce high-fidelity data for validation of computational models. Dye concentration measurements were used to determine the time average rate of mixing. Particle Imaging Velocimetry (PIV) was used to measure the flow fields within the gap. A Proper Orthogonal Decomposition (POD) of the PIV flow fields revealed the presence of coherent flow structure. The decomposed flow fields were then used to predict the time averaged mixing, which closely matched the experimentally measured values.PHDNaval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138666/1/seongjin_2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138666/2/seongjin_1.pd

TD-Net: A Tri-domain network for sparse-view CT reconstruction

Sparse-view CT reconstruction, aimed at reducing X-ray radiation risks, frequently suffers from image quality degradation, manifested as noise and artifacts. Existing post-processing and dual-domain techniques, although effective in radiation reduction, often lead to over-smoothed results, compromising diagnostic clarity. Addressing this, we introduce TD-Net, a pioneering tri-domain approach that unifies sinogram, image, and frequency domain optimizations. By incorporating Frequency Supervision Module(FSM), TD-Net adeptly preserves intricate details, overcoming the prevalent over-smoothing issue. Extensive evaluations demonstrate TD-Net's superior performance in reconstructing high-quality CT images from sparse views, efficiently balancing radiation safety and image fidelity. The enhanced capabilities of TD-Net in varied noise scenarios highlight its potential as a breakthrough in medical imaging