Background: Significant interest has been recently shown for using monolithic scintillation crystals in molecular imaging systems, such as positron emission tomography (PET) scanners. Monolithic-based PET scanners result in a lower cost and higher sensitivity, in contrast to systems based on the more conventional pixellated configuration. The monolithic design allows one to retrieve depth-of -interaction information of the impinging 511 keV photons without the need for additional hardware materials or complex positioning algorithms. However, the so-called edge-effect inherent to monolithic-based approaches worsens the detector performance toward the crystal borders due to the truncation of the light distribution, thus decreasing positioning accuracy. Purpose: The main goal of this work is to experimentally demonstrate the detector performance improvement when machine-learning artificial neuralnetwork (NN) techniques are applied for positioning estimation in multiple monolithic scintillators optically coupled side-by-side. Methods: In this work, we show the performance evaluation of two LYSO crystals of 33 × 25.4 × 10 mm 3 optically coupled by means of a high refractive index adhesive compound (Meltmount, refractive index n = 1.70). A 12 × 12 silicon photomultiplier array has been used as photosensor. For comparison, the same detector configuration was tested for two additional coupling cases: (1) optical grease (n = 1.46) in between crystals, and (2) isolated crystals using black paint with an air gap at the interface (named standard configuration). Regarding 2D photon positioning (XY plane), we have tested two different methods: (1) a machine-learning artificial NN algorithm and (2) a squared-charge (SC) centroid technique. Results: At the interface region of the detector, the SC method achieved spatial resolutions of 1.7 ± 0.3, 2.4 ± 0.3, and 2.6 ± 0.4 mm full-width at half -maximum (FWHM) for the Meltmount, grease, and standard configurations, respectively. These values improve to 1.0 ± 0.2, 1.2 ± 0.2, and 1.2 ± 0.3 mm FWHM when the NN algorithm was employed. Regarding energy performance, resolutions of 18 ± 2%, 20 ± 2%, and 23 ± 3% were obtained at the interface region of the detector for Meltmount, grease, and standard configurations, respectively. Conclusions: The results suggest that optically coupling together scintillators with a high refractive index adhesive, in combination with an NN algorithm, reduces edge-effects and makes it possible to build scanners with almost no gaps in between detectors.

In the past years, the gamma-ray detector designs based on the monolithic crystals have demonstrated to be excellent candidates for the design of high-performance PET systems. The monolithic crystals allow to achieve the intrinsic detector resolutions well below state-of-the-art; to increase packing fraction thus, increasing the system sensitivity; and to improve lesion detectability at the edges of the scanner field of view (FOV) because of their intrinsic depth of interaction (DOI) capabilities. The bottleneck to translate to the clinical PET systems based on a large number of monolithic detectors is eventually the requirement of mechanically complex and time-consuming calibration processes. To mitigate this drawback, several methods have been already proposed, such as using non-physically collimated radioactive sources or implementing the neuronal networks (NN) algorithms trained with simulated data. In this work, we aimed to simplify and fasten a calibration process of the monolithic based systems. The Normal procedure consists of individually acquiring a 11 × 11 22Na source array for all the detectors composing the PET system and obtaining the calibration map for each module using a method based on the Voronoi diagrams. Two reducing time methodologies are presented: (i) TEST1, where the calibration map of one detector is estimated and shared among all others, and (ii) TEST2, where the calibration map is slightly modified for each module as a function of their detector uniformity map. The experimental data from a dedicated prostate PET system was used to compare the standard calibration procedure with both the proposed methods. A greater similarity was exhibited between the TEST2 methodology and the Normal procedure; obtaining spatial resolution variances within 0.1 mm error bars and count rate deviations as small as 0.2%. Moreover, the negligible reconstructed image differences (13% deviation at most in the contrast-to-noise ratio) and almost identical contrast values were reported. Therefore, this proposed method allows us to calibrate the PET systems based on the monolithic crystals reducing the calibration time by approximately 80% compared with the Normal procedure.

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