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In this study, we optimized the optical conditions and associated positioning scheme for an ultrahighspatial-resolution, solid-state gamma detector. The detector module consisted of an array of seven hexagonal silicon drift detectors (SDDs) packed hexagonally and coupled to a single slab of crystal via a light guide glass. Because the optical behavior and requirements of the detector module and noise characteristics of the SDD sensor are different from those of conventional photomultiplier tube (PMT)-based detectors, the following parameters were studied to determine the optimum condition: scintillator selection, the effect of cooling on signal-to-noise ratio (SNR), the depth dependence of the scintillation light distribution, and optimum shaping time. To that end, a modified, Anger-style positioning algorithm with a denoise scheme was also developed to address the estimation bias (pincushion distortion) caused by the excessively confined light distribution and the leakage current induced by the SDD sensor. The results of this study proved that the positioning algorithm, together with the optimized optical configuration of the detector module, improves the positioning accuracy of the prototype detector. Our results confirmed the ability of the prototype to achieve a spatial resolution of about 0.7 mm in full width at half maximum (FWHM) for 122 keV gamma rays under the equivalent noise count (ENC) of 100 (e-rms) per SDD channel. The results also confirmed NaI(Tl) to be a more desirable scintillator for our prototype with an energy resolution performance of about 8%.

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The objective of this research is to develop 2D/3D registration algorithm for PET/CT and CT or MR images acquired by different systems at different times. We matched two anatomical images first (one from PET/CT and the other from standalone CT or MR) that contains affluent anatomical information. Then we geometrically transformed PET image according to the result of transformation parameters calculated by previous step. We developed two stages of registration algorithm. The first stage is global registration. It is consists of 4 independent steps. After selection of reference and target images different data types and ROI of images have been normalized in the preprocessing step. As a next step, target image is geometrically transformed then the similarity between two images has been measured quantitatively. The optimization step updates transformation parameters to find the best matched parameter set efficiently. In second stage that is called fine adjustment, we introduce feature based registration algorithm. The features of each image slices are extracted by independent component analysis(ICA) and the extracted feature plane is used to measure the similarity. B-spline based freeform deformation is done to form a registered image as a final step. The result of proposed algorithm shows good agreement of images that between PET/CT to CT and PET/CT to MR. We will expand the application of the algorithm to different imaging modalities.