Depth of Interaction Estimation in a Preclinical PET Scanner Equipped with Monolithic Crystals Coupled to SiPMs Using a Deep Neural Network  
[PERSON]  
1 Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland; [EMAIL_ADDRESS]
2  
[PERSON]  
1 Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland; [EMAIL_ADDRESS]
3 Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands  
4 Department of Nuclear Medicine, University of Southern Denmark, DK-500 Odense, Denmark  
4 Correspondence: [EMAIL_ADDRESS]; Tel. : +41-22-372-7258; Fax: +41-22-372-71694  
avalanche photodetectors (APDs) and silicon photomultipliers (SiPMs) is the extraction of 3D position information [1; 2]. Providing the DOI information or the third dimension of the scintillation position improves the spatial resolution by decreasing the parallax error at the corners of the detectors. Accurate determination of the 3D position is crucial in small-bore PET scanners, including preclinical and organ-specific PET scanners dedicated to brain, prostate, and female breast imaging [3; 4]. In recent years, a number of commercial preclinical PET scanners using advanced analytical positioning methods were designed and fabricated. The Bruker Albira is a multipurpose preclinical PET scanner equipped with monolithic crystals and multi-anode photomultiplier tubes. The achieved radial and axial spatial resolutions were 1.72 and 2.45 mm, respectively, whereas the maximum absolute sensitivity was 5.3% [5]. The MOLECUBES \(\beta\)-CUBE is a commercial small-animal PET scanner that was designed using a monolithic Lutetium-Yttrium Oxyorthosilicate doped with Cerium (LYSO: Ce) crystal with 8 mm thickness attached to 3 mm \(\times\) 3 mm Silicon photomultipliers. The spatial resolution and absolute sensitivity of this scanner are 1 mm and 12.4%, respectively, at the center of the field-of-view (FOV) [6]. A model was fitted to the optical photons distribution to estimate the 3D scintillation event's position inside the monolithic crystal. For each interaction position, the maximum likelihood was estimated based on the optical photon distribution map as a successfully implemented option following training [7]. Another study presented a mathematical framework based on the distribution of pixel intensity values and the attenuation and stopping power of the scintillation material [8]. In this approach, the DOI was estimated as the ratio of the photon's deposited energy to the maximum local intensity. Besides the analytical techniques based on pure mathematical approaches, a body of literature is accumulating on the use of machine learning, particularly deep neural networks (DNNs) to create a lookup table built by irradiating the scintillator crystal side [9]. Recently, [PERSON] et al. introduced an artificial neural network for extracting the 3D interaction position of the scintillation point inside the crystal [10]. Another group devised a technique that reduces the nonlinear dimensionality to predict the features of the detector response [11]. The DOI estimation in monolithic scintillators faces several challenges, including a computational burden and complicated calibration. Among the above-mentioned approaches, the Gradient Tree Boosting algorithm showed the best performance for extracting the scintillation position in continuous crystals, enabling a spatial resolution of 2.12 mm to be achieved [12]. More recently, [PERSON] et al. used deep neural networks for 3D interaction position estimation for Cherenkov-based detectors through Monte Carlo (MC) simulations [13]. The core inspiration of the current work is to present a new positioning technique based on a deep learning algorithm aimed at achieving an enhanced spatial resolution while maintaining the high sensitivity of monolithic crystals.