Nephrolithiasis can be a painful problem due to presence of kidney stones. Kidney stone is among the common painful disorders of the urinary system. Various imaging modalities are used to diagnose patients with symptoms of renal or urinary tract disease such as plain kidney, ureter, bladder x-ray (KUB), intravenous pyelography (IVP), and computed tomography (CT). As a traditional three-dimensional (3D) nephrolithiasis and kidney stones detection technique, computed tomography (CT) provides detailed cross-sectional images as well as 3D structure of kidney from moving the x-ray beam in a circle around the body. However, the risk of CT scans of the kidney is relatively higher exposure to radiation which is more than regular x-rays. C-arm technique is a new x-ray imaging modality that uses 2D array detector and cone shaped x-ray beam to create 3D information about the scanned object. Both x-ray source and 2D array detector cells mounted on C-shaped wheeled structure (C-arm). A series of projection images are acquired by rotating the C-arm around the patient in along circular path with a single rotation. The characteristic structure of C-arm allows to provide wide variety of movements around the patient that helps to remain the patient stationary during scanning time. In this work, we investigated a C-arm technique to generate a series of tomographic images for nephrolithiasis and detection of kidney stones. C-arm tomographic technique (C-arm tomosynthesis) as a new three dimensional (3D) kidney imaging method that provides a series of two dimensional (2D) images along partial circular orbit over limited view angle. Our experiments were done with kidney phantom which formed from a pig kidney with two embedded kidney stones inside it and low radiation dosage. Radiation dose and scanning time needed for kidney imaging are all dramatically reduced due to the cone beam geometry and also to limitation of angular rotation. To demonstrate the capability of our C-arm tomosynthesis to generate 3D kidney information for kidney stone detection, two groups of tomographic image reconstruction algorithms were developed for C-arm tomosynthesis: direct algorithms such as filtered back projection (FBP) and iterative algorithms such as simultaneous algebraic reconstruction technique (SART), maximum likelihood expectation maximization (MLEM), ordered- subset maximum likelihood expectation maximization (OS-MLEM) and Pre-computed penalized likelihood reconstruction (PPL). Three reconstruction methods were investigated including: pixel-driven method (PDM), ray-driven method (RDM) and distance driven method (DDM). Each method differs in their efficiency of calculation accuracy per computing time. Preliminary results demonstrated the capability of proposed technique to generate volumetric data about the kidney for nephrolithiasis and kidney stone detection by using all investigated reconstruction algorithms. In spite of each algorithms differs in their strategies, embedded kidney stone can be clearly visualized in all reconstruction results. Computer simulation studies were also done on simulated phantom to evaluate the results for each reconstruction algorithm. To mimic kidney phantom, simulated phantom was simulated with two different size kidney stones. Dataset of projection images was collated by using a virtual C-arm tomosynthesis with geometric configuration similar to real technique. All investigated algorithms were used to reconstruct 3D information. Different of image quality functions were applied to evaluate the imaging system and the reconstruction algorithms. The results show the capability of C-arm tomosynthesis to generate 3D information of kidney structures and to identify the size and location of kidney stones with limited amount of radiation dose.
| Identifer | oai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:dissertations-2282 |
| Date | 01 December 2016 |
| Creators | MALALLA, NUHAD ABDULWAHED YOUNIS |
| Publisher | OpenSIUC |
| Source Sets | Southern Illinois University Carbondale |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations |
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