ADAPTIVE SMALL-ANIMAL SPECT/CT

Singular value decomposition for photon-processing nuclear imaging systems and applications for reconstruction and computing null functions

Recent advances in technology are enabling a new class of nuclear imaging systems consisting of detectors that use real-time maximum-likelihood (ML) methods to estimate the interaction position, deposited energy, and other attributes of each photon-interaction event and store these attributes in a list format. This class of systems, which we refer to as photon-processing (PP) nuclear imaging systems, can be described by a fundamentally different mathematical imaging operator that allows processing of the continuous-valued photon attributes on a per-photon basis. Unlike conventional photon-counting (PC) systems that bin the data into images, PP systems do not have any binning-related information loss. Mathematically, while PC systems have an infinite-dimensional null space due to dimensionality considerations, PP systems do not necessarily suffer from this issue. Therefore, PP systems have the potential to provide improved performance in comparison to PC systems. To study these advantages, we propose a framework to perform the singular-value decomposition (SVD) of the PP imaging operator. We use this framework to perform the SVD of operators that describe a general two-dimensional (2D) planar linear shift-invariant (LSIV) PP system and a hypothetical continuously rotating 2D single-photon emission computed tomography (SPECT) PP system. We then discuss two applications of the SVD framework. The first application is to decompose the object being imaged by the PP imaging system into measurement and null components. We compare these components to the measurement and null components obtained with PC systems. In the process, we also present a procedure to compute the null functions for a PC system. The second application is designing analytical reconstruction algorithms for PP systems. The proposed analytical approach exploits the fact that PP systems acquire data in a continuous domain to estimate a continuous object function. The approach is parallelizable and implemented for graphics processing units (GPUs). Further, this approach leverages another important advantage of PP systems, namely the possibility to perform photon-by-photon real-time reconstruction. We demonstrate the application of the approach to perform reconstruction in a simulated 2D SPECT system. The results help to validate and demonstrate the utility of the proposed method and show that PP systems can help overcome the aliasing artifacts that are otherwise intrinsically present in PC systems.

Molecular Imaging in the College of Optical Sciences - An Overview of Two Decades of Instrumentation Development

During the past two decades, researchers at the University of Arizona's Center for Gamma-Ray Imaging (CGRI) have explored a variety of approaches to gamma-ray detection, including scintillation cameras, solid-state detectors, and hybrids such as the intensified Quantum Imaging Device (iQID) configuration where a scintillator is followed by optical gain and a fast CCD or CMOS camera. We have combined these detectors with a variety of collimation schemes, including single and multiple pinholes, parallel-hole collimators, synthetic apertures, and anamorphic crossed slits, to build a large number of preclinical molecular-imaging systems that perform Single-Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), and X-Ray Computed Tomography (CT). In this paper, we discuss the themes and methods we have developed over the years to record and fully use the information content carried by every detected gamma-ray photon.

Design and performance of a compact and stationary microSPECT system

PURPOSE

Over the last ten years, there has been an extensive growth in the development of microSPECT imagers. Most of the systems are based on the combination of conventional, relatively large gamma cameras with poor intrinsic spatial resolution and multipinhole collimators working in large magnification mode. Spatial resolutions range from 0.58 to 0.76 mm while peak sensitivities vary from 0.06% to 0.4%. While pushing the limits of performance is of major importance, the authors believe that there is a need for smaller and less complex systems that bring along a reduced cost. While low footprint and low-cost systems can make microSPECT available to more researchers, the ease of operation and calibration and low maintenance cost are additional factors that can facilitate the use of microSPECT in molecular imaging. In this paper, the authors simulate the performance of a microSPECT imager that combines high space-bandwidth detectors and pinholes with truncated projection, resulting in a small and stationary system.

METHODS

A system optimization algorithm is used to determine the optimal SPECT systems, given our high resolutions detectors and a fixed field-of-view. These optimal system geometries are then used to simulate a Defrise disk phantom and a hot rod phantom. Finally, a MOBY mouse phantom, with realistic concentrations of Tc99m-tetrofosmin is simulated.

RESULTS

Results show that the authors can successfully reconstruct a Defrise disk phantom of 24 mm in diameter without any rotating system components or translation of the object. Reconstructed spatial resolution is approximately 800 μm while the peak sensitivity is 0.23%. Finally, the simulation of the MOBY mouse phantom shows that the authors can accurately reconstruct mouse images.

CONCLUSIONS

These results show that pinholes with truncated projections can be used in small magnification or minification mode to obtain a compact and stationary microSPECT system. The authors showed that they can reach state-of-the-art system performance and can successfully reconstruct images with realistic noise levels in a preclinical context. Such a system can be useful for dynamic SPECT imaging.

The AdaptiSPECT Imaging Aperture

In this paper, we present the imaging aperture of an adaptive SPECT imaging system being developed at the Center for Gamma Ray Imaging (AdaptiSPECT). AdaptiSPECT is designed to automatically change its configuration in response to preliminary data, in order to improve image quality for a particular task. In a traditional pinhole SPECT imaging system, the characteristics (magnification, resolution, field of view) are set by the geometry of the system, and any modification can be accomplished only by manually changing the collimator and the distance of the detector to the center of the field of view. Optimization of the imaging system for a specific task on a specific individual is therefore difficult. In an adaptive SPECT imaging system, on the other hand, the configuration can be conveniently changed under computer control. A key component of an adaptive SPECT system is its aperture. In this paper, we present the design, specifications, and fabrication of the adaptive pinhole aperture that will be used for AdaptiSPECT, as well as the controls that enable autonomous adaptation.

Design and Validation of an Adaptive SPECT System: AdaptiSPECT

In order to obtain optimal image quality with respect to a particular task, adaptive imaging systems automatically change their acquisition parameters in response to preliminary data being recorded from the object under study. Currently, the adaptive aspect in Single Photon Emission Computed Tomography (SPECT) is limited to a manual collimator interchange and the choice of detector rotation radius. Furthermore, there is often no optimization of any kind with respect to a certain task. There is thus a need for more versatile SPECT systems that autonomously optimize their acquisition geometry for every task and every patient. Here we describe a pinhole SPECT imager, AdaptiSPECT, which is being developed at the Center for Gamma Ray Imaging (CGRI) to enable adaptive SPECT imaging in a pre-clinical context. Furthermore, ideas for an autonomous adaptation procedure are discussed and some preliminary results are reported upon.

Maximum-Likelihood Calibration of an X-ray Computed Tomography System

We present a maximum-likelihood (ML) method for calibrating the geometrical parameters of an x-ray computed tomography (CT) system. This method makes use of the full image data and not a reduced set of data. This algorithm is particularly useful for CT systems that change their geometry during the CT acquisition, such as an adaptive CT scan. Our ML search method uses a contracting-grid algorithm that does not require initial starting values to perform its estimate, thus avoiding problems associated with choosing initialization values.