Image Reconstruction Analysis for Positron Emission Tomography With Heterostructured Scintillators.

The concept of structure engineering has been proposed for exploring the next generation of radiation detectors with improved performance. A TOF-PET geometry with heterostructured scintillators with a pixel size of 3.0 × 3.1 × 15 mm3 was simulated using Monte Carlo. The heterostructures consisted of alternating layers of BGO as a dense material with high stopping power and plastic (EJ232) as a fast light emitter. The detector time resolution was calculated as a function of the deposited and shared energy in both materials on an event-by-event basis. While sensitivity was reduced to 32% for 100-µm thick plastic layers and 52% for 50 µm, the coincidence time resolution (CTR) distribution improved to 204 ± 49 and 220 ± 41 ps, respectively, compared to 276 ps that we considered for bulk BGO. The complex distribution of timing resolutions was accounted for in the reconstruction. We divided the events into three groups based on their CTR and modeled them with different Gaussian TOF kernels. On an NEMA IQ phantom, the heterostructures had better contrast recovery in early iterations. On the other hand, BGO achieved a better contrast-to-noise ratio (CNR) after the 15th iteration due to the higher sensitivity. The developed simulation and reconstruction methods constitute new tools for evaluating different detector designs with complex time responses.

Advances in heterostructured scintillators: toward a new generation of detectors for TOF-PET.

Objective.Time-of-flight-positron emission tomography would highly benefit from a coincidence time resolution (CTR) below 100 ps: improvement in image quality and patient workflow, and reduction of delivered dose are among them. This achievement proved to be quite challenging, and many approaches have been proposed and are being investigated for this scope. One of the most recent consists in combining different materials with complementary properties (e.g. high stopping power for 511 keVγ-ray and fast timing) in a so-calledheterostructure,metascintillatorormetapixel. By exploiting a mechanism of energy sharing between the two materials, it is possible to obtain a fraction of fast events which significantly improves the overall time resolution of the system.Approach.In this work, we present the progress on this innovative technology. After a simulation study using the Geant4 toolkit, aimed at understanding the optimal configuration in terms of energy sharing, we assembled four heterostructures with alternating plates of BGO and EJ232 plastic scintillator. We fabricated heterostructures of two different sizes (3 × 3 × 3 mm3and  3 × 3 × 15 mm3), each made up of plates with two different thicknesses of plastic plates. We compared the timing of these pixels with a standard bulk BGO crystal and a structure made of only BGO plates (layeredBGO).Main results.CTR values of 239 ± 12 ps and 197 ± 10 ps FWHM were obtained for the 15 mm long heterostructures with 100µm and 200µm thick EJ232 plates (both with 100µm thick BGO plates), compared to 271 ± 14 ps and 303 ± 15 ps CTR for bulk and layered BGO, respectively.Significance.Significant improvements in timing compared to standard bulk BGO were obtained for all the configurations tested. Moreover, for the long pixels, depth of interaction (DOI) collimated measurements were also performed, allowing to validate a simple model describing light transport inside the heterostructure.

Concept development of an on-chip PET system.

BACKGROUND: 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 as monitoring physiologic processes within OOCs is vital for the continuous improvement of this technology. Positron Emission Tomography (PET) would be ideal for OOC imaging, however, current PET systems are insufficient for this task due to their inadequate spatial resolution. In this work, we propose the concept of an On-Chip PET system capable of imaging OOCs and optimize its design using a Monte Carlo Simulation (MCS). MATERIAL AND METHODS: The proposed system consists of four detectors arranged around the OOC device. Each detector is made of two monolithic LYSO crystals and covered with Silicon photomultipliers (SiPMs) on multiple surfaces. We use a Convolutional Neural Network (CNN) trained with data from a 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. RESULTS: The CNN achieves a mean average prediction error of 0.80 mm in the best configuration. The proposed system achieves a sensitivity of 34.81% for 13 mm thick crystals and does not show a prediction degradation near the boundaries of the detector. 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 0.55 mm. We show that 25,000 Line of Responses (LORs) are needed to reconstruct a realistic OOC phantom with adequate image quality. CONCLUSIONS: 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 a thinner crystal performs better than a thicker one, that increasing the SiPM size from 3 mm to 6 mm only slightly decreases the prediction performance, and that certain surfaces encode significantly more information for the scintillation-point prediction than others.

Improving depth-of-interaction resolution in pixellated PET detectors using neural networks.

Parallax error is a common issue in high-resolution preclinical positron emission tomography (PET) scanners as well as in clinical scanners that have a long axial field of view (FOV), which increases estimation uncertainty of the annihilation position and therefore degrades the spatial resolution. A way to address this issue is depth-of-interaction (DOI) estimation. In this work we propose two machine learning-based algorithms, a dense and a convolutional neural network (NN), as well as a multiple linear regression (MLR)-based method to estimate DOI in depolished PET detector arrays with single-sided readout. The algorithms were tested on an 8× 8 array of 1.53× 1.53× 15 mm3 crystals and a 4× 4 array of 3.1× 3.1× 15 mm3 crystals, both made of Ce:LYSO scintillators and coupled to a 4× 4 array of 3× 3 mm3 silicon photomultipliers (SiPMs). Using the conventional linear DOI estimation method resulted in an average DOI resolution of 3.76 mm and 3.51 mm FWHM for the 8× 8 and the 4× 4 arrays, respectively. Application of MLR outperformed the conventional method with average DOI resolutions of 3.25 mm and 3.33 mm FWHM, respectively. Using the machine learning approaches further improved the DOI resolution, to an average DOI resolution of 2.99 mm and 3.14 mm FWHM, respectively, and additionally improved the uniformity of the DOI resolution in both arrays. Lastly, preliminary results obtained by using only a section of the crystal array for training showed that the NN-based methods could be used to reduce the number of calibration steps required for each detector array.

TOF-PET Image Reconstruction With Multiple Timing Kernels Applied on Cherenkov Radiation in BGO.

Today Time-of-Flight (TOF), in PET scanners, assumes a single, well-defined timing resolution for all events. However, recent BGO-Cherenkov detectors, combining prompt Cherenkov emission and the typical BGO scintillation, can sort events into multiple timing kernels, best described by the Gaussian mixture models. The number of Cherenkov photons detected per event impacts directly the detector time resolution and signal rise time, which can later be used to improve the coincidence timing resolution. This work presents a simulation toolkit which applies multiple timing spreads on the coincident events and an image reconstruction that incorporates this information. A full cylindrical BGO-Cherenkov PET model was compared, in terms of contrast recovery and contrast-to-noise ratio, against an LYSO model with a time resolution of 213 ps. Two reconstruction approaches for the mixture kernels were tested: 1) mixture Gaussian and 2) decomposed simple Gaussian kernels. The decomposed model used the exact mixture component applied during the simulation. Images reconstructed using mixture kernels provided similar mean value and less noise than the decomposed. However, typically, more iterations were needed. Similarly, the LYSO model, with a single TOF kernel, converged faster than the BGO-Cherenkov with multiple kernels. The results indicate that the model complexity slows down convergence. However, due to the higher sensitivity, the contrast-to-noise ratio was 26.4% better for the BGO model.

Inactivation of Escherichia coli and Staphylococcus aureus by pulsed electric fields increases with higher bacterial population and with agitation of liquid medium.

Inactivation of Escherichia coli, E. coli O157:H7, and Staphylococcus aureus in liquid media by pulsed electric fields (PEF) was conducted at varying bacterial populations with and without sample agitation. A laboratory-scale PEF batch unit with a rectangular electric pulse was used, operating under the following conditions: 25 kV/cm (E. coli, E. coli O157:H7) and 30 kV/cm (S. aureus) electric field strengths, 1-µs pulse width, 1-Hz pulse repetition rate, and 20 to 350 pulses for all samples. Not surprisingly, bacterial inactivation (for all three strains) increased with increasing pulse number, achieving the highest reduction at 350 pulses. Log CFU per milliliter microbial inactivation increased commensurately with increasing bacterial population (P < 0.05) but only when samples were treated with more than 200 pulses. For example, when E. coli was treated with 200 pulses at 10(5) CFU/ml, inactivation was only 3.0 Log versus 4.8 Log at the 10(10) inoculation level. When E. coli O157:H7 was treated with 200 pulses at 10(5) CFU/ml, inactivation was only 2.5 Log versus 4.6 Log at the 10(10) inoculation level. When S. aureus was treated with 200 pulses at 10(6) CFU/ml, inactivation was only 2.6 Log versus 4.8 Log at the 10(10) inoculation level. Inactivation of populations was also found to be statistically greater (P < 0.05) when liquid samples were agitated, in comparison to nonagitated samples. Because PEF inactivation activity is influenced by bacterial population and sample agitation, future studies should carefully consider these factors in experimental designs and/or scaled-up industry application.