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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.

Recently, high-resolution gamma cameras have been developed with detectors containing> 10 5 -10 6 elements. SPECT imagers based on these detectors usually also have a large number of voxel bins and therefore face memory storage issues for the system matrix when performing fast tomographic reconstructions using iterative algorithms. To address these issues, we have developed a method that parameterizes the detector response to a point source and generates the system matrix on the fly during MLEM or OSEM on graphics hardware. The calibration method, interpolation of coefficient data, and reconstruction results are presented in the context of a recently commissioned small-animal SPECT imager, called FastSPECT III.

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.

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.