Most small animal PET scanners are based on arrays of pixelated scintillators crystals. As the read-out of individual pixels would be too expensive, identification of the crystal of interaction is usually made by center of energy methods based for instance on Anger logic. This allows for a reduction in the number of signals to be acquired, but prevents the identification of multi-hit events, that is, events in that one (or several) photon produces several hits in the detector, thus blurring the correct positioning of the interaction. Improving the identification of the pixel of interaction is pursued in this work by combining all the information acquired by the scanner without increasing the number of signals. The probability for every individual event for being single or multi-pixel is estimated from the XY positioning and energy information. This probability is fed into a 3D-OSEM iterative statistical reconstruction method. Every coincidence event detected may be analyzed combining information such as deposited energy, PMT XY location, time difference between both singles of the coincidence and coincidence and single rates, if available. With the proposed method, improved peak/noise ratio and better resolution are obtained without the introduction of additional hardware.
Abstract-The potential of PET imaging for pre-clinical studies will be fully realized only if repeatable, reliable and accurate quantitative analysis can be performed. The characteristic blurring of PET images due to positron range and non co-linearity, as well as random, pile-up and scatter contributions, that may be significant for fully 3D PET acquisitions of small animal, make it difficult their quantitative analysis. In this work specific activity versus specific counts in the image calibration curves for 3D-OSEM reconstructions from a commercially available small animal PET scanner are determined. Both linear and non-linear calibration curves are compared and the effect of corrections for random and scatter contributions are studied. To assess the improvement in the calibration procedure when scatter and random corrections are considered, actual data from a rat tumor pre-and postcancer therapy are analyzed. The results show that correcting for random and scatter corrections can increase the sensitivity of PET images to changes in the biological response of tumors by more than 15%, compared to uncorrected reconstructions.
PeneloPET is a Monte Carlo application based on PENELOPE. We present here the new features and results of validation tests for the new version of PeneloPET that has been compared against data from real scanners. PeneloPET was built as a powerful tool for PET simulation, it is easy to use, fast and very accurate. Recently, many improvements have been made in the code with the incorporation of a very realistic signal processing chain and by adding the possibility of running simulations in parallel mode on cluster computers. A comparison between data obtained with two small animal scanners and the results of PeneloPET simulations has been performed. The small animal PET scanners were an eXplore Vista DR (GEHC) and a partial ring, rotating rPET (SUINSA Medical Systems). Intrinsic resolution, scatter fractions, noise equivalent count rates and sensitivity measurements for the real acquisitions and simulations were compared. NEMA protocol was applied using mouse size and rat size cylinders, spheres and line sources as phantoms. Results show small differences (less than 10%) between real acquisitions and simulated data, proving that PeneloPET is an accurate tool for PET simulations.
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