High-Resolution Gamma-Ray Imaging with Columnar Scintillators and CCD/CMOS Sensors, and FastSPECT III: A Third-Generation Stationary SPECT Imager
AuthorMiller, Brian William
AdvisorFurenlid, Lars R
Barrett, Harrison H
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractA new class of scintillation detector has emerged that combines columnar scintillators and CCD/CMOS sensors for high-resolution imaging. Originally developed for single-photon gamma-ray imaging, these detectors provide better than an order-of-magnitude improvement in spatial resolution compared to conventional photomultiplier tube (PMT)-based gamma cameras; sub-100 micron detector resolutions have been achieved. This work reviews the several detector configurations developed in recent years, with a specific emphasis on a type of CCD/CMOS detector developed at the Center for Gamma-Ray Imaging, which we call BazookaSPECT, that amplifies scintillation light using an image intensifier to achieve both high spatial resolution and high event-rate capability.Ongoing research into scintillator deposition techniques has led to a new form of scintillation material where crystallites are organized into columns. Similar to optical fibers, this columnar structure helps to channels scintillation light towards an exit face while restricting lateral light spread. However, because they are not perfect optical fibers, light spreads laterally and is absorbed by an amount relating to the interaction depth. Taking advantage of this phenomenon, we discuss the use of maximum-likelihood methods to estimate the 3D position and energy of gamma-ray interactions in columnar CsI(Tl)/EMCCD-based detectors.Finally, we present new imaging applications that have arisen from BazookaSPECT. These include the the development of a gamma-ray microscope using micro-coded apertures, feasibility studies for photon-counting digital mammography and eventually X-ray CT, and FastSPECT III -- a third generation small animal stationary SPECT imager. FastSPECT III system design, fabrication methods, data acquisition system, system calibration procedure, and initial tomographic reconstructions are presented.
Degree ProgramGraduate College
Degree GrantorUniversity of Arizona
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SemiSPECT: A small-animal SPECT imager based on eight cadmium zinc tellurium detector arraysBarrett, Harrison H.; Kim, Hyunki (The University of Arizona., 2004)We have completed a new small-animal imaging system, called SemiSPECT, based on eight pixellated cadmium zinc telluride (CdZnTe) gamma-ray detector arrays. The detector is a 2.5 cm x 2.5 cm x 0.15 cm slab having a 64 x 64 pixel array. A read-out application-specific integrated circuit (ASIC) is attached onto the detector via indium-bump bonding, and a -180 V bias is applied onto the detector surface to transport electron-hole pairs generated by gamma-ray interaction. Eight detectors are arranged in an octagonal lead-shielded ring. An eight-pinhole aperture is placed at the center of the ring, and an object is imaged onto each detector through a pinhole. The object can be rotated about a vertical axis to attain sufficient angular projections for tomographic reconstruction. The whole system gantry is compact enough to be placed onto a desktop-sized optical breadboard. Eight front-end boards were developed to detect events, generate list-mode data arrays, and send them to back-end boards. Four back-end boards are utilized to hold the list-mode data arrays and transfer them to a host computer. Eight clock-and-bias boards provide clock and bias signals to the eight ASICs. Eight control-and-bias boards were developed to monitor and control the temperatures on the eight detectors, analog and digital currents supplied to the eight ASICs, and -180 V biases applied to the eight detector surfaces. The spatial resolution provided by SemiSPECT, estimated both based on the system geometry and via the Fourier crosstalk approach, is about 1∼2 mm. The system sensitivity measured with a point source is about 1.53 x 10⁻⁴, and the estimated one from the system geometry is about 1.41 x 10⁻⁴. The energy resolution acquired by summing neighboring pixel signals in a 3 x 3 window is about 10% full-width-at-half-maximum for 140 keV gamma rays. The detectabilities for multiple signal spheres simulating various lesions or organs in a small animal are presented and discussed. A line-phantom image demonstrates that the spatial resolution achieved after tomographic reconstruction is on the order of 1 mm³. A walnut-phantom image is presented to demonstrate how well SemiSPECT reproduces complex structure of an object and compared with a CT image. Images of various organs of mice, such as bone, kidney, and myocardium, and human-lung cancer implanted into a nude mouse are presented and discussed.
Design and fabrication of a preclinical adaptive SPECT imaging system : AdaptiSPECTChaix, Cécile; Kovalsky, Stephen; Kupinski, Matthew A.; Barrett, Harrison H.; Furenlid, Lars R.; College of Optical Sciences, University of Arizona; Center for GammaRay Imaging, Department of Medical Imaging, University of Arizona (2014-11-07)
Simulation and Analysis of an Adaptive SPECT Imaging System for Tumor EstimationKupinski, Matthew; Trumbull, Tara; Clarkson, Eric; Furenlid, Lars; Barrett, Harrison H (The University of Arizona., 2011)We have developed a simulation of the AdaptiSPECT small-animal Single Photon Emission Computed Tomography (SPECT) imaging system. The simulation system is entitled SimAdaptiSPECT and is written in C, NVIDIA CUDA, and Matlab. Using this simulation, we have accomplished an analysis of the Scanning Linear Estimation (SLE) technique for estimating tumor parameters, and calculated sensitivity information for AdaptiSPECT configurations.SimAdaptiSPECT takes, as input, simulated mouse phantoms (generated by MOBY) contained in binary files and AdaptiSPECT configuration geometry contained in ASCII text files. SimAdaptiSPECT utilizes GPU parallel processing to simulate AdaptiSPECT images. SimAdaptiSPECT also utilizes GPU parallel processing to perform 3-D image reconstruction from 2-D AdaptiSPECT camera images (real or simulated), using a novel variant of the Ordered Subsets Expectation Maximization (OSEM) algorithm. Methods for generating the inputs, such as a population of randomly varying numerical mouse phantoms with randomly varying hepatic lesions, are also discussed.