Study and optimization of positioning algorithms for monolithic PET detectors blocks (original) (raw)
Related papers
Evaluation of a PET prototype using LYSO:Ce monolithic detector blocks
2011 IEEE Nuclear Science Symposium Conference Record, 2011
We have analyzed the performance of a PET demonstrator formed by two sectors of four monolithic detector blocks placed face-to-face. Both front-end and read-out electronics have been evaluated by means of coincidence measurements using a rotating 22 Na source placed at the center of the sectors in order to emulate the behavior of a complete full ring. A continuous training method based on neural network (NN) algorithms has been carried out to determine the entrance points over the surface of the detectors. Reconstructed images from 1 MBq 22 Na point source and 22 Na Derenzo phantom have been obtained using both fi I tered back projection (FBP) analytic methods and the OSEM 3D iterative algorithm available in the STIR software package [1]. Preliminary data on image reconstruction from a 22 Na point source with 0 = 0.25 mm show spatial resolutions from 1.7 to 2.1 mm FWHM in the transverse plañe. The results confirm the viability of this design for the development of a full-ring brain PET scanner compatible with magnetic resonance imaging for human studies.
Corrected position estimation in PET detector modules with multi-anode PMTs using neural networks
IEEE Transactions on Nuclear Science, 2006
This paper studies the use of Neural Networks (NNs) for estimating the position of impinging photons in gamma ray detector modules for PET cameras based on continuous scintillators and Multi-Anode Photomultiplier Tubes (MA-PMTs). The detector under study is composed of a 49 49 10 mm 3 continuous slab of LSO coupled to a flat panel H8500 MA-PMT. Four digitized signals from a charge division circuit, which collects currents from the 8 8 anode matrix of the photomultiplier, are used as inputs to the NN, thus reducing drastically the number of electronic channels required. We have simulated the computation of the position for 511 keV gamma photons impacting perpendicularly to the detector surface. Thus, we have performed a thorough analysis of the NN architecture and training procedures in order to achieve the best results in terms of spatial resolution and bias correction. Results obtained using GEANT4 simulation toolkit show a resolution of 1.3 mm/1.9 mm FWHM at the center/edge of the detector and less than 1 mm of systematic error in the position near the edges of the scintillator. The results confirm that NNs can partially model and correct the non-uniform detector response using only the position-weighted signals from a simple 2D DPC circuit. Linearity degradation for oblique incidence is also investigated. Finally, the NN can be implemented in hardware for parallel real time corrected Line-of-Response (LOR) estimation. Results on resources occupancy and throughput in FPGA are presented.
Optimization of a monolithic detector block design for a prototype human brain PET scanner
2008 IEEE Nuclear Science Symposium Conference Record, 2008
We are presently developing a novel PET scanner for human brain functional imaging based on monolithic scintillator crystals read by APD matrices, capable of being inserted into an MRI system. In this work we report on the detailed study that has been made of the design of the detector blocks, aiming at defining the most suitable geometrical and readout configuration for optimizing the overall performance of the entire scanner. Both parallel and trapezoidal geometries have been simulated, featuring two layers of active scintillator material with different or similar thickness and APD readout on the front or back side. Results of this study indicate that a trapezoidal geometry with equal thickness of both layers is the best solution for improving the expected scanner performance. P
Evaluation of monolithic detector blocks for high- sensitivity PET imaging of the human brain
2007 IEEE Nuclear Science Symposium Conference Record, 2007
We propose and evaluate an improved design at the level of PET detector blocks based on monolithic crystals that will eventually be used on a research prototype for human brain PET/MRI imaging-the BrainPET scanner. These new detector blocks, when compared with pixilated designs, feature simpler mechanics, lower cost, larger sensitive volume, better energy and spatial resolutions, all of which contribute to improvements in PET detector technology. Moreover, the magnetic compatibility of all the materials composing the block makes it suitable for operation inside an MRI scanner. Results from both experimental data and Monte Carlo simulations allow an evaluation of the performance of the detector blocks, illustrating their potential for high-sensitivity PET imaging of the human brain.
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2011
We are developing a human brain PET scanner prototype compatible with MRI based on monolithic scintillator crystals, APD matrices and a dedicated ASIC front-end readout. In this work we report on the performance of individual detector modules and on the operation of such modules in PET coincidence. Results will be presented on the individual characterization of detector blocks and its ASIC front-end readout, with measured energy resolutions of 13% full-width half-maximum (FWHM) at 511 keV and spatial resolutions of the order of 2 mm FWHM. First results on PET coincidence performance indicate spatial resolutions as good as 2.1 mm FWHM for SSRB/FBP reconstruction of tomographic data obtained using a simple PET demonstrator based on a pair of monolithic detector blocks with ASIC readout.
Simulation and evaluation of a cost-effective high-performance brain PET scanner
2017
Positron Emission Tomography (PET) plays a vital role in molecular imaging, primarily for cancer detection. Most commercialized PET scanners are dedicated to the whole body (WB) studies, with very few dedicated to organs like the brain, heart and breast. Brain studies require scanners with high spatial resolution and sensitivity, due to the pathologies associated with the brain. On one hand, semiconductor-based PET detectors have an excellent intrinsic spatial resolution but are not cost effective. On the other hand, scintillator-based PET detectors can provide high system sensitivity and are cost-effective, but they lack the spatial resolution required to detect very small brain lesions. Therefore, the intrinsic spatial resolution of such detectors needs to be improved. In order to improve the spatial resolution of scintillator detectors, a brain PET scanner (“MB-PET”) employing 1 × 1× 10 mm3 pixelated lutetium yttrium oxyorthosilicate (LYSO) detector was simulated using Geant4 app...
Technical performance evaluation of a human brain PET/MRI system
European Radiology, 2012
Objectives Technical performance evaluation of a human brain PET/MRI system. Methods The magnetic field compatible positron emission tomography (PET) insert is based on avalanche photodiode (APD) arrays coupled with lutetium oxyorthosilicate (LSO) crystals and slip-fits into a slightly modified clinical 3-T MRI system. The mutual interference between the two imaging techniques was minimised by the careful design of the hardware to maintain the quality of the B 0 and B 1 field homogeneity.
Calibration of positron emission tomograph detector modules using new neural method
Electronics Letters, 2004
In this paper we describe a neural network-based method aimed at automatically calibrating the detector module contained in a scanner for a highresolution positron emission tomography (PET) system for small animals. The detector module is composed of crystal elements, arranged in a regular matrix and sensitive to gamma rays emitted by a radioactive source. The crystal matrix is optically coupled to a position-sensitive photo-multiplier tube, which reconstructs the original image. Calibration, required to cope with spatial distortions introduced by the optical system, consists of a segmentation process of the image produced after the photo-multiplier tube into a fixed number of areas. The purpose of this segmentation is to map each pixel of the perceived image onto the pertinent crystal, which was actually struck by the gamma ray emitted by the radioactive source.
Applied Sciences, 2020
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 mm 3 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 XY 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