Depth of Interaction Estimation in a Preclinical PET Scanner Equipped with Monolithic Crystals Coupled to SiPMs Using a Deep Neural Network

Abstract

<jats:p>The scintillation light distribution produced by photodetectors in positron emission tomography (PET) provides the depth of interaction (DOI) information required for high-resolution imaging. The goal of positioning techniques is to reverse the photodetector signal’s pattern map to the coordinates of the incident photon energy position. By considering the DOI information, monolithic crystals offer good spatial, energy, and timing resolution along with high sensitivity. In this work, a supervised deep neural network was used for the approximation of DOI and to assess through Monte Carlo (MC) simulations the performance on a small-animal PET scanner consisting of ten 50 × 50 × 10 mm3 continuous Lutetium-Yttrium Oxyorthosilicate doped with Cerium (LYSO: Ce) crystals and 12 × 12 silicon photomultiplier (SiPM) arrays. The scintillation position was predicted by a multilayer perceptron neural network with 256 units and 4 layers whose inputs were the number of fired pixels on the SiPM plane and the total deposited energy. A GEANT4 MC code was used to generate training and test datasets by altering the photons’ incident position, energy, and direction, as well as readout of the photodetector output. The calculated spatial resolutions in the X-Y plane and along the Z-axis were 0.96 and 1.02 mm, respectively. Our results demonstrated that using a multilayer perceptron (MLP)-based positioning algorithm in the detector modules, constituting the PET scanner, enhances the spatial resolution by approximately 18% while the absolute sensitivity remains constant. The proposed algorithm proved its ability to predict the DOI for depth under 7 mm with an error below 8.7%.</jats:p>

The promise of artificial intelligence and deep learning in PET and SPECT imaging

“…In this regard, Peng et al trained a CNN classifier that was fed with signals from each Silicon photomultiplier's channel to the coordinates of the scintillation point for a quasi-monolithic crystal [17]. Another study applied a multi-layer perceptron to predict the 3D coordinates of the interaction position inside a monolithic crystal and compared the performance of this positioning algorithm with anger logic for a preclinical PET scanner based on NEMA NU4 2008 standards [18]. Fig.…”

“…In comparison with state-of-the-art methods, their proposed model was able to generate comparable image quality while being 500 faster [44]. Another study employed a multi-input U-Net to predict 2D transaxial slices of 18 F-Florbetaben full-dose PET images from corresponding low-dose images, taking advantage of available T1, T2, and Diffusion-weighted MR sequences [43]. Liu et al employed three modified U-Net architectures to enhance the noise characteristics of PET images through concurrent MR images without the need for full-dose PET images with a higher signal-to-noise ratio [50].…”

“…The 3D U-Net architecture was able to reduce the noise and PET quantification bias while enhancing image quality of brain and chest 18 F-FDG PET images [52]. To compensate for the limited training dataset, they pre-trained the model using simulation studies in the first stage and then fine-tuned the last layers of the network with realistic data.…”

Nanodiamond-containing composites for tissue scaffolds and surgical implants: A review

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An overview of the development of composites containing Mg and Zn for drug delivery

“…Moreover, the attempts towards overcoming negative aspects of conventional drug delivery that are formed by compression of tablets, coating, and encapsulating bioactive drug molecules have resulted in technological advancements in drug delivery systems and revolution in medication methods [50,56]. In this regard, computational simulations have also provided a unique insight into the mechanisms of drug diffusion and adsorption in porous carriers at the atomic level [57][58][59][60].…”