Positron Emission Tomography (PET) scanners provide functional three-dimensional images of the body that are extremely useful in cancer and brain research. The goal of this work is the modeling, design and characterization of a CMOS-based photodetector for PET. To this aim, first a model for the energy resolution and coincidence resolution time (CRT) for digital, SPAD-based detectors is developed. Then, a top-to-bottom detector architecture is proposed, containing an innovative in-pixel com-pression technique that allows for high fill-factor (FF) and efficient readout. At the top-level of the architecture, an integrated discriminator monitors the photon flux for incoming gamma events, enabling an event-based readout scheme. The first complete implementation of this archi-tecture is described, the SPADnet-I sensor, which is composed by an 8×16 pixel array, each of around 0.6 × 0.6 mm2 with 720 SPADs, resulting in a pixel FF of 42.6%. The sensor can obtain the discrete photon flux estimation at up to 100 Msamples/s, which are used by the discriminator and also output at real-time. The complete characterization of the sensor is presented, and the best sensor configuration was found to be at 84% of the SPADs enabled (disabled starting with the highest DCR one), with 2 V SPAD excess bias and 150 ns integration time. This configuration results in an energy resolution of 10.8% and a CRT of 288 ps, the latter which was obtained with a new, hardware-friendly time of arrival (ToA) estimation algorithm, also described in this thesis. Finally, the sensor model, validated by the experimental results, is used to predict the perfor-mance of possible modifications in the sensor, and some design improvements are suggested for a future implementation of the architecture.
| Identifer | oai:union.ndltd.org:unitn.it/oai:iris.unitn.it:11572/369044 |
| Date | January 2014 |
| Creators | Huf Campos Braga, Leo |
| Contributors | Huf Campos Braga, Leo, Stoppa, David |
| Publisher | Università degli studi di Trento, place:TRENTO |
| Source Sets | Università di Trento |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | firstpage:1, lastpage:123, numberofpages:123 |
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