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

Holistic evaluation of a machine learning-based timing calibration for PET detectors under varying data sparsity

Objective.Modern PET scanners offer precise TOF information, improving the SNR of the reconstructed images. Timing calibrations are performed to reduce the worsening effects of the system components and provide valuable TOF information. Traditional calibration procedures often provide static or linear corrections, with the drawback that higher-order skews or event-to-event corrections are not addressed. Novel research demonstrated significant improvements in the reachable timing resolutions when combining conventional calibration approaches with machine learning, with the disadvantage of extensive calibration times infeasible for a clinical application. In this work, we made the first steps towards an in-system application and analyzed the effects of varying data sparsity on a machine learning timing calibration, aiming to accelerate the calibration time. Furthermore, we demonstrated the versatility of our calibration concept by applying the procedure for the first time to analog readout technology.Approach.We modified experimentally acquired calibration data used for training regarding their statistical and spatial sparsity, mimicking reduced measurement time and variability of the training data. Trained models were tested on unseen test data, characterized by fine spatial sampling and rich statistics. In total, 80 decision tree models with the same hyperparameter settings, were trained and holistically evaluated regarding data scientific, physics-based, and PET-based quality criteria.Main results.The calibration procedure can be heavily reduced from several days to some minutes without sacrificing quality and still significantly improving the timing resolution from(304±5)psto(216±1)pscompared to conventionally used analytical calibration methods.Significance.This work serves as the first step in making the developed machine learning-based calibration suitable for an in-system application to profit from the method's capabilities on the system level. Furthermore, this work demonstrates the functionality of the methodology on detectors using analog readout technology. The proposed holistic evaluation criteria here serve as a guideline for future evaluations of machine learning-based calibration approaches.

Bright Innovations: Review of Next-Generation Advances in Scintillator Engineering

This review focuses on modern scintillators, the heart of ionizing radiation detection with applications in medical diagnostics, homeland security, research, and other areas. The conventional method to improve their characteristics, such as light output and timing properties, consists of improving in material composition and doping, etc., which are intrinsic to the material. On the contrary, we review recent advancements in cutting-edge approaches to shape scintillator characteristics via photonic and metamaterial engineering, which are extrinsic and introduce controlled inhomogeneity in the scintillator's surface or volume. The methods to be discussed include improved light out-coupling using photonic crystal (PhC) coating, dielectric architecture modification producing the Purcell effect, and meta-materials engineering based on energy sharing. These approaches help to break traditional bulk scintillators' limitations, e.g., to deal with poor light extraction efficiency from the material due to a typically large refractive index mismatch or improve timing performance compared to bulk materials. In the Outlook section, modern physical phenomena are discussed and suggested as the basis for the next generations of scintillation-based detectors and technology, followed by a brief discussion on cost-effective fabrication techniques that could be scalable.

The Nanoplasmonic Purcell Effect in Ultrafast and High-Light-Yield Perovskite Scintillators

The development of X-ray scintillators with ultrahigh light yields and ultrafast response times is a long sought-after goal. In this work, a fundamental mechanism that pushes the frontiers of ultrafast X-ray scintillator performance is theoretically predicted and experimentally demonstrated: the use of nanoscale-confined surface plasmon polariton modes to tailor the scintillator response time via the Purcell effect. By incorporating nanoplasmonic materials in scintillator devices, this work predicts over tenfold enhancement in decay rate and 38% reduction in time resolution even with only a simple planar design. The nanoplasmonic Purcell effect is experimentally demonstrated using perovskite scintillators, enhancing the light yield by over 120% to 88 ± 11 ph/keV, and the decay rate by over 60% to 2.0 ± 0.2 ns for the average decay time, and 0.7 ± 0.1 ns for the ultrafast decay component, in good agreement with the predictions of our theoretical framework. Proof-of-concept X-ray imaging experiments are performed using nanoplasmonic scintillators, demonstrating 182% enhancement in the modulation transfer function at four line pairs per millimeter spatial frequency. This work highlights the enormous potential of nanoplasmonics in optimizing ultrafast scintillator devices for applications including time-of-flight X-ray imaging and photon-counting computed tomography.

Transformer-CNN hybrid network for improving PET time of flight prediction

Objective.In positron emission tomography (PET) reconstruction, the integration of time-of-flight (TOF) information, known as TOF-PET, has been a major research focus. Compared to traditional reconstruction methods, the introduction of TOF enhances the signal-to-noise ratio of images. Precision in TOF is measured by full width at half maximum (FWHM) and the offset from ground truth, referred to as coincidence time resolution (CTR) and bias.Approach.This study proposes a network combining transformer and convolutional neural network (CNN) to utilize TOF information from detector waveforms, using event waveform pairs as inputs. This approach integrates the global self-attention mechanism of Transformer, which focuses on temporal relationships, with the local receptive field of CNN. The combination of global and local information allows the network to assign greater weight to the rising edges of waveforms, thereby extracting valuable temporal information for precise TOF predictions. Experiments were conducted using lutetium yttrium oxyorthosilicate (LYSO) scintillators and silicon photomultiplier (SiPM) detectors. The network was trained and tested using the waveform datasets after cropping.Main results.Compared to the constant fraction discriminator (CFD), CNN, CNN with attention, long short-term memory (LSTM) and Transformer, our network achieved an average CTR of 189 ps, reducing it by 82 ps (more than 30%), 13 ps (6.4%), 12 ps (6.0%), 16 ps (7.8%) and 9 ps (4.6%), respectively. Additionally, a reduction of 10.3, 8.7, 6.7 and 4 ps in average bias was achieved compared to CNN, CNN with attention, LSTM and Transformer.Significance.This work demonstrates the potential of applying the Transformer for PET TOF estimation using real experimental data. Through the integration of both CNN and Transformer with local and global attention, it achieves optimal performance, thereby presenting a novel direction for future research in this field.

Advancements in Positron Emission Tomography Detectors: From Silicon Photomultiplier Technology to Artificial Intelligence Applications

This review article focuses on PET detector technology, which is the most crucial factor in determining PET image quality. The article highlights the desired properties of PET detectors, including high detection efficiency, spatial resolution, energy resolution, and timing resolution. Recent advancements in PET detectors to improve these properties are also discussed, including the use of silicon photomultiplier technology, advancements in depth-of-interaction and time-of-flight PET detectors, and the use of artificial intelligence for detector development. The article provides an overview of PET detector technology and its recent advancements, which can significantly enhance PET image quality.

16-channel SiPM high-frequency readout with time-over-threshold discrimination for ultrafast time-of-flight applications

BACKGROUND

Over the past five years, ultrafast high-frequency (HF) readout concepts have advanced the timing performance of silicon photomultipliers (SiPMs). The shown impact in time-of-flight (TOF) techniques can further push the limits in light detection and ranging (LiDAR), time-of-flight positron-emission tomography (TOF-PET), time-of-flight computed tomography (TOF-CT) or high-energy physics (HEP). However, upscaling these electronics to a system-applicable, multi-channel readout, has remained a challenging task, posed by the use of discrete components and a high power consumption. To this day, there are no means to exploit the high TOF resolution of these electronics on system scale or to measure the actual timing performance limits of a full detector block.

METHODS

In this work, we present a 16-channel HF readout board, including leading-edge discrimination and a linearized time-over-threshold (TOT) method, which is fully compatible with a high-precision time-to-digital converters (TDCs), such as the picoTDC developed at CERN. The discrete implementation allows ideal adaptation of this readout to a broad range of detection tasks. As a first step, the functionality of the circuit has been tested using the TOFPET2 ASIC as back-end electronics to emulate the TDC, also in view of its properties as a highly scalable data acquisition solution.

RESULTS

The produced board is able to mitigate influences of baseline shifts in the TOFPET2 front end, which has been shown in experiments with a pulsed laser, increasing the achievable intrinsic coincidence timing resolution (CTR) of the TOFPET2 readout electronics from 70 ps (FWHM) to 62 ps (FWHM). Single-channel coincidence experiments including a [Formula: see text]-source, 2[Formula: see text]2[Formula: see text]3 mm[Formula: see text] LYSO:Ce,Ca crystals and Broadcom NUV-MT SiPMs resulted in a CTR of 118 ps (FWHM). For a 4[Formula: see text]4 matrix of 3.88[Formula: see text]3.88[Formula: see text]19 mm[Formula: see text] LYSO:Ce,Ca crystals one-to-one coupled to a 4[Formula: see text]4 array of Broadcom NUV-MT SiPMs, an average CTR of 223 ps (FWHM) was obtained.

CONCLUSION

The implemented 16-channel HF electronics are fully functionall and have a negligible influence on the timing performance of the back-end electronics used, here the TOFPET2 ASIC. The ongoing integration of the picoTDC with the 16-channel HF board is expected to further set the path toward sub-100 ps TOF-PET and sub-30ps TOF resolution for single-photon detection.

Timing Estimation and Limits in TOF-PET Detectors Producing Prompt Photons

The production of prompt photons providing high photon time densities is a promising avenue to reach ultrahigh coincidence time resolution (CTR) in time-of-flight PET. Detectors producing prompt photons are receiving high interest experimentally, ignited by past exploratory theoretical studies that have anchored some guiding principles. Here, we aim to consolidate and extend the foundations for the analytical modeling of prompt generating detectors. We extend the current models to a larger range of prompt emission kinetics where more stringent requirements on the prompt photon yield rapidly emerge as a limiting factor. Lower bound and estimator evaluations are investigated with different underlying models, notably by merging or keeping separate the prompt and scintillation photon populations. We further show the potential benefits of knowing the proportion of prompt photons within a detection set to improve the CTR by mitigating the detrimental effect of population (prompt vs scintillation) mixing. Taking into account the fluctuations on the average number of detected prompt photons in the model reveals a limited influence when prompt photons are accompanied by fast scintillation (e.g., LSO:Ce:Ca) but a more significant effect when accompanied by slower scintillation (e.g., BGO). Establishing performance characteristics and limitations of prompt generating detectors is paramount to gauging and targeting the best possible timing capabilities they can offer.

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.