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We have implemented and evaluated a positron emission tomography (PET) demonstrator using two monolithic detector blocks operating in coincidence with dedicated applicationspecific integrated circuit (ASIC) readout. Each detector is composed of a monolithic lutetium yttrium orthosilicate (LYSO) scintillator coupled to a pair of Hamamatsu S8550-02 APD arrays. The front-end electronics of this demonstrator is based on the VATA240 ASIC readout, which sums the charge provided by each row and column of the APD array. The ASIC has been characterized obtaining a noise per row or column less than 2000 electrons rms with the APD at its inputs and a good linear response in the range from 5 fC to 30 fC. We have acquired energy spectra of 22 Na and 137 Cs radioactive sources, achieving energy resolutions between 13.2% and 14.1% full width at half maximum (FWHM) at 511 keV. We have estimated the interaction position over the surface of the monolithic blocks using Neural Networks (NN) position determining algorithms, obtaining spatial resolutions at the detector level down to 2.1 mm FWHM. By using this detector technology and electronics we have achieved images of point sources with spatial resolutions as good as 2.1 mm FWHM for filtered back projection (FBP) reconstructions methods with single slice rebinning (SSRB). Based on the results obtained with this demonstrator, we are developing a PET insert for existing magnetic resonance imaging (MRI) equipment, to be installed in a collaborating hospital and used for clinical PET-MRI of the human brain.Index Terms-Application specific integrated circuit, artificial neural networks, position sensitive detectors, positron emission tomography.

Universitat de Barcelona (UB) and CIEMAT have designed the FlexToT ASIC for the front-end readout of SiPM-based scintillator detectors. This ASIC is aimed at time of flight (ToF) positron emission tomography (PET) applications. In this work we have evaluated the time performance of the FlexToT v2 ASIC compared to the NINO ASIC, a fast ASIC developped at CERN. NINO electronics give 64 ps sigma for single-photon time resolution (SPTR) and 93 ps FWHM for coincidence time resolution (CTR) with 2 × 2 × 5 mm3 LSO:Ce,Ca crystals and S13360-3050CS SiPMs. Using the same SiPMs and crystals, the FlexToT v2 ASIC yields 91 ps sigma for SPTR and 123 ps FWHM for CTR. Despite worse time performace than NINO, FlexToT v2 features lower power consumption (11 vs. 27 mW/ch) and linear ToT energy measurement.

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We are developing a positron emission tomography (PET) scanner based on avalanche photodiodes (APD), monolithic LYSO:Ce scintillator crystals and a dedicated readout chip. All these components allow operation inside a magnetic resonance imaging (MRI) scanner with the aim of building a PET/MRI hybrid imaging system for clinical human brain studies. Previous work verified the functional performance of our first chip (VATA240) based on a leading edge comparator and the principle of operation of our radiation sensors, which are capable of providing reconstructed images of positron point sources with spatial resolutions of 2.1 mm FWHM. The new VATA241 chip presented in this work has been designed with the aim of reducing the coincidence window of our final PET scanner by implementing an on-chip constant fraction discriminator (CFD), as well as providing a better robustness for its implementation in the full-scale PET scanner. Results from the characterization of the VATA241 chip are presented, together with the first results on coincidence performance, validating the new design for our application.

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We are developing a PET insert for existing MRI equipment to be used in clinical PET/MR studies of the human brain. The proposed scanner is based on annihilation gamma detection with monolithic blocks of cerium-doped lutetium yttrium orthosilicate (LYSO:Ce) coupled to magnetically-compatible avalanche photodiodes (APD) matrices. The light distribution generated on the LYSO:Ce block provides the impinging position of the 511 keV photons by means of a positioning algorithm. Several positioning methods, from the simplest Anger Logic to more sophisticate supervised-learning Neural Networks (NN), can be implemented to extract the incidence position of gammas directly from the APD signals. Finally, an optimal method based on a two-step Feed-Forward Neural Network has been selected. It allows us to reach a resolution at detector level of 2 mm, and acquire images of point sources using a first BrainPET prototype consisting of two monolithic blocks working in coincidence. Neural networks provide a straightforward positioning of the acquired data once they have been trained, however the training process is usually time-consuming. In order to obtain an efficient positioning method for the complete scanner it was necessary to find a training procedure that reduces the data acquisition and processing time without introducing a noticeable degradation of the spatial resolution. A grouping process and posterior selection of the training data have been done regarding the similitude of the light distribution of events which have one common incident coordinate (transversal or longitudinal). By doing this, the amount of training data can be reduced to about 5% of the initial number with a degradation of spatial resolution lower than 10%.

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