Concept Development of an On-Chip PET System.

Organs-on-Chips (OOCs), microdevices mimicking in vivo organs, find growing applications in disease modeling and drug discovery. With the increasing number of uses comes a strong demand for imaging capabilities of OOCs. Positron Emission Tomography (PET) would be ideal for OOC imaging, however, current PET systems have insufficient spatial resolution for this task. In this work, we propose the concept of an On-Chip PET system capable of imaging OOCs. Our system consists of four detectors arranged around the OOC device. Each detector is made of two monolithic Lutetium-yttrium oxyorthosilicate (LYSO) crystals and covered with Silicon photomultipliers (SiPMs) on multiple surfaces. We use a Convolutional Neural Network (CNN) trained with data from a Monte Carlo Simulation (MCS) to predict the first gamma-ray interaction position inside the detector from the light patterns that are recorded by the SiPMs on the detector's surfaces. With the Line of Responses (LORs) created by the predicted interaction positions, we reconstruct with Simultaneous Algebraic Reconstruction Technique (SART). The CNN achieves a mean average prediction error of 0.78 mm in the best configuration. We use the trained network to reconstruct an image of a grid of 21 point sources spread across the field-of-view and obtain a mean spatial resolution of 0.53 mm. We demonstrate that it is possible to achieve a spatial resolution of almost 0.5 mm in a PET system made of multiple monolithic LYSO crystals by directly predicting the scintillation position from light patterns created with SiPMs. We observe that CNNs from the ResNet family perform better than those from the EfficientNet family and that certain surfaces encode significantly more information for the scintillation-point prediction than others.

Deep Learning for Predicting Gamma-Ray Interaction Positions in LYSO Detector.

Positron Emission Tomography (PET) is among the most commonly used medical imaging modalities in clinical practice, especially for oncological applications. In contrast to conventional imaging modalities like X-ray Computed Tomography (CT) or Magnetic Resonance Imaging (MRI), PET retrieves in vivo information about biochemical processes rather than just anatomical structures. However, physical limitations and detector constraints lead to an order of magnitude lower spatial resolution in PET images. In recent years, the use of monolithic detector crystals has been investigated to overcome some of the factors limiting spatial resolution. The key to increasing PET systems' resolution is to estimate the gamma-ray interaction position in the detector as precisely as possible.In this work, we evaluate a Convolutional Neural Network (CNN) based reconstruction algorithm that predicts the gamma-ray interaction position using light patterns recorded with Silicon photomultipliers (SiPMs) on the crystal's surfaces. The algorithm is trained on data from a Monte Carlo Simulation (MCS) that models a gamma point source and a detector consisting of Lutetium-yttrium oxyorthosilicate (LYSO) crystals and SiPMs added to five surfaces. The final Mean Absolute Error (MAE) on the test dataset is 1.48 mm.

On light sharing TOF-PET modules with depth of interaction and 157 ps FWHM coincidence time resolution.

The performance of a light sharing and recirculation mechanism that allows the extraction of depth of interaction (DOI) are investigated in this paper, with a particular focus on timing. In parallel, a method to optimize the coincidence time resolution (CTR) of PET detectors by use of the DOI information is proposed and tested. For these purposes, a dedicated 64-channels readout setup has been developed with intrinsic timing resolution of 16 ps FWHM. Several PET modules have been produced, based on LYSO:Ce scintillators and commercial silicon photomultiplier (SiPM) arrays, with [Formula: see text] mm2 individual SiPM size. The results show the possibility to achieve a timing resolution of 157 ps FWHM, combined with the already demonstrated spatial resolution of 1.5 mm FWHM, DOI resolution of 3 mm FWHM, and energy resolution of 9% FWHM at 511 keV, with 15 mm long crystals of section [Formula: see text] mm2 and [Formula: see text] mm2. At the same time, the extraction of the DOI coordinate has been demonstrated not to deteriorate the timing performance of the PET module.

A new method for depth of interaction determination in PET detectors.

A new method for obtaining depth of interaction (DOI) information in PET detectors is presented in this study, based on sharing and redirection of scintillation light among multiple detectors, together with attenuation of light over the length of the crystals. The aim is to obtain continuous DOI encoding with single side readout, and at the same time without the need for one-to-one coupling between scintillators and detectors, allowing the development of a PET scanner with good spatial, energy and timing resolutions while keeping the complexity of the system low. A prototype module has been produced and characterized to test the proposed method, coupling a LYSO scintillator matrix to a commercial SiPMs array. Excellent crystal separation is obtained for all the scintillators in the array, light loss due to depolishing is found to be negligible, energy resolution is shown to be on average 12.7% FWHM. The mean DOI resolution achieved is 4.1 mm FWHM on a 15 mm long crystal and preliminary coincidence time resolution was estimated in 353 ps FWHM.