A finely segmented semi-monolithic detector tailored for high-resolution PET

BACKGROUND

Preclinical research and organ-dedicated applications use and require high (spatial-)resolution positron emission tomography (PET) detectors to visualize small structures (early) and understand biological processes at a finer level of detail. Researchers seeking to improve detector and image spatial resolution have explored various detector designs. Current commercial high-resolution systems often employ finely pixelated or monolithic scintillators, each with its limitations.

PURPOSE

We present a semi-monolithic detector, tailored for high-resolution PET applications with a spatial resolution in the range of 1 mm or better, merging concepts of monolithic and pixelated crystals. The detector features LYSO slabs measuring (24 × 10 × 1) mm3, coupled to a 12 × 12 readout channel photosensor with 4 mm pitch. The slabs are grouped in two arrays of 44 slabs each to achieve a higher optical photon density despite the fine segmentation.

METHODS

We employ a fan beam collimator for fast calibration to train machine-learning-based positioning models for all three dimensions, including slab identification and depth-of-interaction (DOI), utilizing gradient tree boosting (GTB). The data for all dimensions was acquired in less than 2 h. Energy calculation was based on a position-dependent energy calibration. Using an analytical timing calibration, time skews were corrected for coincidence timing resolution (CTR) estimation.

RESULTS

Leveraging machine-learning-based calibration in all three dimensions, we achieved high detector spatial resolution: down to 1.18 mm full width at half maximum (FWHM) detector spatial resolution and 0.75 mm mean absolute error (MAE) in the planar-monolithic direction, and 2.14 mm FWHM and 1.03 mm MAE for DOI at an energy window of (435-585) keV. Correct slab interaction identification in planar-segmented direction exceeded 80%, alongside an energy resolution of 12.7% and a CTR of 450 ps FWHM.

CONCLUSIONS

The introduced finely segmented, high-resolution slab detector demonstrates appealing performance characteristics suitable for high-resolution PET applications. The current benchtop-based detector calibration routine allows these detectors to be used in PET systems.

Histopathological biomarkers for predicting the tumour accumulation of nanomedicines

The clinical prospects of cancer nanomedicines depend on effective patient stratification. Here we report the identification of predictive biomarkers of the accumulation of nanomedicines in tumour tissue. By using supervised machine learning on data of the accumulation of nanomedicines in tumour models in mice, we identified the densities of blood vessels and of tumour-associated macrophages as key predictive features. On the basis of these two features, we derived a biomarker score correlating with the concentration of liposomal doxorubicin in tumours and validated it in three syngeneic tumour models in immunocompetent mice and in four cell-line-derived and six patient-derived tumour xenografts in mice. The score effectively discriminated tumours according to the accumulation of nanomedicines (high versus low), with an area under the receiver operating characteristic curve of 0.91. Histopathological assessment of 30 tumour specimens from patients and of 28 corresponding primary tumour biopsies confirmed the score's effectiveness in predicting the tumour accumulation of liposomal doxorubicin. Biomarkers of the tumour accumulation of nanomedicines may aid the stratification of patients in clinical trials of cancer nanomedicines.

The quest for multifunctional and dedicated PET instrumentation with irregular geometries

We focus on reviewing state-of-the-art developments of dedicated PET scanners with irregular geometries and the potential of different aspects of multifunctional PET imaging. First, we discuss advances in non-conventional PET detector geometries. Then, we present innovative designs of organ-specific dedicated PET scanners for breast, brain, prostate, and cardiac imaging. We will also review challenges and possible artifacts by image reconstruction algorithms for PET scanners with irregular geometries, such as non-cylindrical and partial angular coverage geometries and how they can be addressed. Then, we attempt to address some open issues about cost/benefits analysis of dedicated PET scanners, how far are the theoretical conceptual designs from the market/clinic, and strategies to reduce fabrication cost without compromising performance.

Near-field coded-mask technique and its potential for proton therapy monitoring

Objective.Prompt-gamma imaging encompasses several approaches to the online monitoring of the beam range or deposited dose distribution in proton therapy. We test one of the imaging techniques - a coded mask approach - both experimentally and via simulations.Approach.Two imaging setups have been investigated experimentally. Each of them comprised a structured tungsten collimator in the form of a modified uniformly redundant array mask and a LYSO:Ce scintillation detector of fine granularity. The setups differed in detector dimensions and operation mode (1D or 2D imaging). A series of measurements with radioactive sources have been conducted, testing the performance of the setups for near-field gamma imaging. Additionally, Monte Carlo simulations of a larger setup of the same type were conducted, investigating its performance with a realistic gamma source distribution occurring during proton therapy.Main results.The images of point-like sources reconstructed from two small-scale prototypes' data using the maximum-likelihood expectation maximisation algorithm constitute the experimental proof of principle for the near-field coded-mask imaging modality, both in the 1D and the 2D mode. Their precision allowed us to calibrate out certain systematic offsets appearing due to the limited alignment accuracy of setup elements. The simulation of the full-scale setup yielded a mean distal falloff retrieval precision of 0.72 mm in the studies for beam energy range 89.5-107.9 MeV and with 1 × 108protons (a typical number for distal spots). The implemented algorithm of image reconstruction is relatively fast-a typical procedure needs several seconds.Significance.Coded-mask imaging appears a valid option for proton therapy monitoring. The results of simulations let us conclude that the proposed full-scale setup is competitive with the knife-edge-shaped and the multi-parallel slit cameras investigated by other groups.

Monte Carlo simulation of the system performance of a long axial field-of-view PET based on monolithic LYSO detectors

BACKGROUND

In light of the milestones achieved in PET design so far, further sensitivity improvements aim to optimise factors such as the dose, throughput, and detection of small lesions. While several longer axial field-of-view (aFOV) PET systems based on pixelated detectors have been installed, continuous monolithic scintillation detectors recently gained increased attention due to their depth of interaction capability and superior intrinsic resolution. As a result, the aim of this work is to present and evaluate the performance of two long aFOV, monolithic LYSO-based PET scanner designs.

METHODS

Geant4 Application for Tomographic Emission (GATE) v9.1 was used to perform the simulations. Scanner designs A and B have an aFOV of 36.2 cm (7 rings) and 72.6 cm (14 rings), respectively, with 40 detector modules per ring each and a bore diameter of 70 cm. Each module is a 50 × 50 × 16 mm³ monolithic LYSO crystal. Sensitivity, noise equivalent count rate (NECR), scatter fraction, spatial resolution, and image quality tests were performed based on NEMA NU-2018 standards.

RESULTS

The sensitivity of design A was calculated to be 29.2 kcps/MBq at the centre and 27 kcps/MBq at 10 cm radial offset; similarly, the sensitivity of design B was found to be 106.8 kcps/MBq and 98.3 kcps/MBq at 10 cm radial offset. NECR peaks were reached at activity concentrations beyond the range of activities used for clinical studies. In terms of spatial resolution, the values for the point sources were below 2 mm for the radial, tangential, and axial full width half maximum. The contrast recovery coefficient ranged from 53% for design B and 4:1 contrast ratio to 90% for design A and 8:1 ratio, with a reasonably low background variability.

CONCLUSIONS

Longer aFOV PET designs using monolithic LYSO have superior spatial resolution compared to current pixelated total-body PET (TB-PET) scanners. These systems combine high sensitivity with improved contrast recovery.

Analysis of a convex time skew calibration for light sharing-based PET detectors

Objective.Positron emission tomography (PET) detectors providing attractive coincidence time resolutions (CTRs) offer time-of-flight information, resulting in an improved signal-to-noise ratio of the PET image. In applications with photosensor arrays that employ timestampers for individual channels, timestamps typically are not time synchronized, introducing time skews due to different signal pathways. The scintillator topology and transportation of the scintillation light might provoke further skews. If not accounted for these effects, the achievable CTR deteriorates. We studied a convex timing calibration based on a matrix equation. In this work, we extended the calibration concept to arbitrary structures targeting different aspects of the time skews and focusing on optimizing the CTR performance for detector characterization. The radiation source distribution, the stability of the estimations, and the energy dependence of calibration data are subject to the analysis.Approach.A coincidence setup, equipped with a semi-monolithic detector comprising 8 LYSO slabs, each 3.9 mm × 31.9 mm × 19.0 mm, and a one-to-one coupled detector with 8 × 8 LYSO segments of 3.9 mm × 3.9 mm × 19.0 mm volume is used. Both scintillators utilize a dSiPM (DPC3200-22-44, Philips Digital Photon Counting) operated in first photon trigger. The calibration was also conducted with solely one-to-one coupled detectors and extrapolated for a slab-only setup.Main results.All analyzed hyperparameters show a strong influence on the calibration. Using multiple radiation positions improved the skew estimation. The statistical significance of the calibration dataset and the utilized energy window was of great importance. Compared to a one-to-one coupled detector pair achieving CTRs of 224 ps the slab detector configuration reached CTRs down to 222 ps, demonstrating that slabs can compete with a clinically used segmented detector design.Significance.This is the first work that systematically studies the influence of hyperparameters on skew estimation and proposes an extension to arbitrary calibration structures (e.g. scintillator volumes) of a known calibration technique.

A semi-monolithic detector providing intrinsic DOI-encoding and sub-200 ps CRT TOF-capabilities for clinical PET applications

BACKGROUND

Current clinical positron emission tomography (PET) systems utilize detectors where the scintillator typically contains single elements of 3-6-mm width and about 20-mm height. While providing good time-of-flight performance, this design limits the spatial resolution and causes radial astigmatism as the depth-of-interaction (DOI) remains unknown.

PURPOSE

We propose an alternative, aiming to combine the advantages of current detectors with the DOI capabilities shown for monolithic concepts, based on semi-monolithic scintillators (slabs). Here, the optical photons spread along one dimension enabling DOI-encoding with a still small readout area beneficial for timing performance.

METHODS

An array of eight monolithic LYSO slabs of dimensions 3.9 × 32 × 19 mm³ was read out by a 64-channel photosensor containing digital SiPMs (DPC3200-22-44, Philips Digital Photon Counting). The position estimation in the detector's monolithic and DOI direction was based on a calibration with a fan beam collimator and the machine learning technique gradient tree boosting (GTB).

RESULTS

We achieved a positioning performance in terms of mean absolute error (MAE) of 1.44 mm for the monolithic direction and 2.12 mm for DOI considering a wide energy window of 300-700 keV. The energy resolution was determined to be 11.3%, applying a positional-dependent energy calibration. We established both an analytical and machine-learning-based timing calibration approach and applied them for a first-photon trigger. The analytical timing calibration corrects for electronic and optical time skews leading to 240 ps coincidence resolving time (CRT) for a pair of slab-detectors. The CRT was significantly improved by utilizing GTB to predict the time difference based on specific training data and applied on top of the analytical calibration. We achieved 209 ps for the wide energy window and 198 ps for a narrow selection around the photopeak (411-561 keV). To maintain the detector's sensitivity, no filters were applied to the data during processing.

CONCLUSION

Overall, the semi-monolithic detector provides attractive performance characteristics. Especially, a good CRT can be achieved while introducing DOI capabilities to the detector, making the concept suitable for clinical PET scanners.

A neural network-based algorithm for simultaneous event positioning and timestamping in monolithic scintillators

Objective. Monolithic scintillator crystals coupled to silicon photomultiplier (SiPM) arrays are promising detectors for PET applications, offering spatial resolution around 1 mm and depth-of-interaction information. However, their timing resolution has always been inferior to that of pixellated crystals, while the best results on spatial resolution have been obtained with algorithms that cannot operate in real-time in a PET detector. In this study, we explore the capabilities of monolithic crystals with respect to spatial and timing resolution, presenting new algorithms that overcome the mentioned problems. Approach. Our algorithms were tested first using a simulation framework, then on experimentally acquired data. We tested an event timestamping algorithm based on neural networks which was then integrated into a second neural network for simultaneous estimation of the event position and timestamp. Both algorithms are implemented in a low-cost field-programmable gate array that can be integrated in the detector and can process more than 1 million events per second in real-time. Results. Testing the neural network for the simultaneous estimation of the event position and timestamp on experimental data we obtain 0.78 2D FWHM on the (x, y) plane, 1.2 depth-of-interaction FWHM and 156 coincidence time resolution on a 25 mm × 25 mm × 8 mm × LYSO monolith read-out by 64 3 mm × 3 mm Hamamatsu SiPMs. Significance. Our results show that monolithic crystals combined with artificial intelligence can rival pixellated crystals performance for time-of-flight PET applications, while having better spatial resolution and DOI resolution. Thanks to the use of very light neural networks, event characterization can be done on-line directly in the detector, solving the issues of scalability and computational complexity that up to now were preventing the use of monolithic crystals in clinical PET scanners.

Understanding Query Combination Behavior in Exploratory Searches

In exploratory search, users sometimes combine two or more issued queries into new queries. We present such a kind of search behavior as query combination behavior. We find that the queries after combination usually can better meet users’ information needs. We also observe that users combine queries for different motivations, which leads to different types of query combination behaviors. Previous work on understanding user exploratory search behaviors has focused on how people reformulate queries, but not on how and why they combine queries. Being able to answer these questions is important for exploring how users search and learn during information retrieval processes and further developing support to assist searchers. In this paper, we first describe a two-layer hierarchical structure for understanding the space of query combination behavior types. We manually classify query combination behavior sessions from AOL and Sogou search engines and explain the relationship from combining queries to success. We then characterize some key aspects of this behavior and propose a classifier that can automatically classify types of query combination behavior using behavioral features. Finally, we summarize our findings and show how search engines can better assist searchers.

Hybrid total-body pet scanners-current status and future perspectives

Purpose Since the 1990s, PET has been successfully combined with MR or CT systems. In the past years, especially PET systems have seen a trend towards an enlarged axial field of view (FOV), up to a factor of ten. Methods Conducting a thorough literature research, we summarize the status quo of contemporary total-body (TB) PET/CT scanners and give an outlook on possible future developments. Results Currently, three human TB PET/CT systems have been developed: The PennPET Explorer, the uExplorer, and the Biograph Vision Quadra realize aFOVs between 1 and 2 m and show a tremendous increase in system sensitivity related to their longer gantries. Conclusion The increased system sensitivity paves the way for short-term, low-dose, and dynamic TB imaging as well as new examination methods in almost all areas of imaging.

Simulation study on the performance of time-over-threshold based positioning in monolithic PET detectors

The vast majority of PET detectors in the field today are based on pixelated scintillators. Yet, the resolution of this type of detector is limited by the pixel size. To overcome this limitation, one can use monolithic detectors. However, this detector architecture demands specific and high-speed detector readout of the photodetector array. A commonly used approach is to integrate the current pulses generated by every pixel but such circuitry quickly becomes bulky, power consuming and expensive. The objective of this work is to investigate a novel readout and event positioning scheme for monolithic PET detectors, based on time-over-threshold (ToT). In this case, we measure the time that the pulse is above a certain threshold through a comparator. The pulse widths are used for event positioning using a mean nearest neighbour approach (mNN_ToT). For energy determination one integrating multiplexed channel is foreseen. We evaluate the positioning accuracy and uniformity of such a ToT detector by means of Monte Carlo simulations. The impact of the threshold value is investigated and the results are compared to a detector using mean nearest neighbour with pulse-integration (mNN_int), which has already proven to allow sub-mm resolution. We show minimal degradation in spatial resolution and bias performance compared to mNN_int. The highest threshold results in the worst resolution performance but degradation remains below 0.1 mm. Bias is largely constant over different thresholds for mNN_ToTand close to identical to mNN_int. Furthermore we show that ToT performs well in terms of detector uniformity and that scattered photons can be positioned inside the crystal with high accuracy. We conclude from this work that ToT is a valuable alternative to pulse-integration for monolithic PET detectors. This novel approach has an impact on PET detector development since it has the advantage of lower power consumption, compactness and inherent amplitude-to-time conversion.

Reducing Calibration Time in PET Systems Based on Monolithic Crystals

In the past years, the gamma-ray detector designs based on the monolithic crystals have demonstrated to be excellent candidates for the design of high-performance PET systems. The monolithic crystals allow to achieve the intrinsic detector resolutions well below state-of-the-art; to increase packing fraction thus, increasing the system sensitivity; and to improve lesion detectability at the edges of the scanner field of view (FOV) because of their intrinsic depth of interaction (DOI) capabilities. The bottleneck to translate to the clinical PET systems based on a large number of monolithic detectors is eventually the requirement of mechanically complex and time-consuming calibration processes. To mitigate this drawback, several methods have been already proposed, such as using non-physically collimated radioactive sources or implementing the neuronal networks (NN) algorithms trained with simulated data. In this work, we aimed to simplify and fasten a calibration process of the monolithic based systems. The Normal procedure consists of individually acquiring a 11 × 11 ²²Na source array for all the detectors composing the PET system and obtaining the calibration map for each module using a method based on the Voronoi diagrams. Two reducing time methodologies are presented: (i) TEST1, where the calibration map of one detector is estimated and shared among all others, and (ii) TEST2, where the calibration map is slightly modified for each module as a function of their detector uniformity map. The experimental data from a dedicated prostate PET system was used to compare the standard calibration procedure with both the proposed methods. A greater similarity was exhibited between the TEST2 methodology and the Normal procedure; obtaining spatial resolution variances within 0.1 mm error bars and count rate deviations as small as 0.2%. Moreover, the negligible reconstructed image differences (13% deviation at most in the contrast-to-noise ratio) and almost identical contrast values were reported. Therefore, this proposed method allows us to calibrate the PET systems based on the monolithic crystals reducing the calibration time by approximately 80% compared with the Normal procedure.

Application of Artificial Intelligence in PET Instrumentation

Artificial intelligence (AI) has been widely used throughout medical imaging, including PET, for data correction, image reconstruction, and image processing tasks. However, there are number of opportunities for the application of AI in photon detector performance or the data collection process, such as to improve detector spatial resolution, time-of-flight information, or other PET system performance characteristics. This review outlines current topics, research highlights, and future directions of AI in PET instrumentation.

High throughput software-based gradient tree boosting positioning for PET systems

The supervised machine learning technique Gradient Tree Boosting (GTB) has shown good accuracy for position estimation of gamma interaction in PET crystals for bench-top experiments while its computational requirements can easily be adjusted. Transitioning to preclinical and clinical applications requires near real-time processing in the scale of full PET systems. In this work, a high throughput GTB-based singles positioning C++ implementation is proposed and a series of optimizations are evaluated regarding their effect on the achievable processing throughput. Moreover, the crucial feature and parameter selection for GTB is investigated for the segmented detectors of the Hyperion II^DPET insert with two main models and a range of GTB hyperparameters. The proposed framework achieves singles positioning throughputs of more than 9.5 GB/s for smaller models and of 240 MB/s for more complex models on a recent Intel Skylake server. Detailed throughput analysis reveals the key performance limiting factors, and an empirical throughput model is derived to guide the GTB model selection process and scanner design decisions. The throughput model allows for throughput estimations with a mean absolute error (MAE) of 175.78 MB/s.

High-resolution monolithic LYSO detector with 6-layer depth-of-interaction for clinical PET

The system spatial resolution of whole-body positron emission tomography (PET) is limited to around 2 mm due to positron physics and the large diameter of the bore. To stay below this 'physics'-limit a scintillation detector with an intrinsic spatial resolution of around 1.3 mm is needed. Currently used detector technology consists of arrays of 2.6-5 mm segmented scintillator pixels which are the dominant factor contributing to the system resolution. Pixelated detectors using smaller pixels exist but face major drawbacks in sensitivity, timing, energy resolution and cost. Monolithic continuous detectors, where the spatial resolution is determined by the shape of the light distribution on the photodetector array, are a promising alternative. Without having the drawbacks of pixelated detectors, monolithic ones can also provide depth-of-interaction (DOI) information. In this work we present a monolithic detector design aiming to serve high-resolution clinical PET systems while maintaining high sensitivity. A 50 × 50 × 16 mm³Lutetium-Yttrium oxyorthosilicate scintillation crystal with silicon photomultiplier (SiPM) backside readout is calibrated in singles mode by a collimated beam obtaining a reference dataset for the event positioning. A mean nearest neighbour (MNN) algorithm and an artificial neural network for positioning are compared. The targeted intrinsic detector resolution of 1.3 mm needed to reach a 2 mm resolution on system level was accomplished with both algorithms. The neural network achieved a mean spatial resolution of 1.14 mm FWHM for the whole detector and 1.02 mm in the centre (30 × 30 mm²). The MNN algorithm performed slightly worse with 1.17 mm for the whole detector and 1.13 mm in the centre. The intrinsic DOI information will also result in uniform system spatial resolution over the full field of view.

Deep residual-convolutional neural networks for event positioning in a monolithic annular PET scanner

PET scanners based on monolithic pieces of scintillator can potentially produce superior performance characteristics (high spatial resolution and detection sensitivity, for example) compared to conventional PET scanners. Consequently, we initiated development of a preclinical PET system based on a single 7.2 cm long annulus of LYSO, called AnnPET. While this system could facilitate creation of high-quality images, its unique geometry results in optics that can complicate estimation of event positioning in the detector. To address this challenge, we evaluated deep-residual convolutional neural networks (DR-CNN) to estimate the three-dimensional position of annihilation photon interactions. Monte Carlo simulations of the AnnPET scanner were used to replicate the physics, including optics, of the scanner. It was determined that a ten-layer-DR-CNN was most suited to application with AnnPET. The errors between known event positions, and those estimated by this network and those calculated with the commonly used center-of-mass algorithm (COM) were used to assess performance. The mean absolute errors (MAE) for the ten-layer-DR-CNN-based event positions were 0.54 mm, 0.42 mm and 0.45 mm along thex(axial)-,y(transaxial)- andz- (depth-of-interaction) axes, respectively. For COM estimates, the MAEs were 1.22 mm, 1.04 mm and 2.79 mm in thex-,y- andz-directions, respectively. Reconstruction of the network-estimated data with the 3D-FBP algorithm (5 mm source offset) yielded spatial resolutions (full-width-at-half-maximum (FWHM)) of 0.8 mm (radial), 0.7 mm (tangential) and 0.71 mm (axial). Reconstruction of the COM-derived data yielded spatial resolutions (FWHM) of 1.15 mm (radial), 0.96 mm (tangential) and 1.14 mm (axial). These findings demonstrated that use of a ten-layer-DR-CNN with a PET scanner based on a monolithic annulus of scintillator has the potential to produce excellent performance compared to standard analytical methods.

Advanced Monte Carlo simulations of emission tomography imaging systems with GATE

Built on top of the Geant4 toolkit, GATE is collaboratively developed for more than 15 years to design Monte Carlo simulations of nuclear-based imaging systems. It is, in particular, used by researchers and industrials to design, optimize, understand and create innovative emission tomography systems. In this paper, we reviewed the recent developments that have been proposed to simulate modern detectors and provide a comprehensive report on imaging systems that have been simulated and evaluated in GATE. Additionally, some methodological developments that are not specific for imaging but that can improve detector modeling and provide computation time gains, such as Variance Reduction Techniques and Artificial Intelligence integration, are described and discussed.

Physics and technology of time-of-flight PET detectors

The imaging performance of clinical positron emission tomography (PET) systems has evolved impressively during the last ∼15 years. A main driver of these improvements has been the introduction of time-of-flight (TOF) detectors with high spatial resolution and detection efficiency, initially based on photomultiplier tubes, later silicon photomultipliers. This review aims to offer insight into the challenges encountered, solutions developed, and lessons learned during this period. Detectors based on fast, bright, inorganic scintillators form the scope of this work, as these are used in essentially all clinical TOF-PET systems today. The improvement of the coincidence resolving time (CRT) requires the optimization of the entire detection chain and a sound understanding of the physics involved facilitates this effort greatly. Therefore, the theory of scintillation detector timing is reviewed first. Once the fundamentals have been set forth, the principal detector components are discussed: the scintillator and the photosensor. The parameters that influence the CRT are examined and the history, state-of-the-art, and ongoing developments are reviewed. Finally, the interplay between these components and the optimization of the overall detector design are considered. Based on the knowledge gained to date, it appears feasible to improve the CRT from the values of 200-400 ps achieved by current state-of-the-art TOF-PET systems to about 100 ps or less, even though this may require the implementation of advanced methods such as time resolution recovery. At the same time, it appears unlikely that a system-level CRT in the order of ∼10 ps can be reached with conventional scintillation detectors. Such a CRT could eliminate the need for conventional tomographic image reconstruction and a search for new approaches to timestamp annihilation photons with ultra-high precision is therefore warranted. While the focus of this review is on timing performance, it attempts to approach the topic from a clinically driven perspective, i.e. bearing in mind that the ultimate goal is to optimize the value of PET in research and (personalized) medicine.

Evaluation of the PETsys TOFPET2 ASIC in multi-channel coincidence experiments

BACKGROUND

Aiming to measure the difference in arrival times of two coincident γ-photons with an accuracy in the order of 200ps, time-of-flight positron emission tomography systems commonly employ silicon photomultipliers (SiPMs) and high-resolution digitization electronics, application specific integrated circuits (ASICs). This work evaluates the performance of the TOFPET2 ASIC, released by PETsys Electronics S.A. in 2017, dependent on its configuration parameters in multi-channel coincidence measurements.

METHODS

SiPM arrays fabricated by different vendors (KETEK, SensL, Hamamatsu, Broadcom) were tested in combination with the ASIC. Scintillator arrays featuring different reflector designs and different configurations of the TOFPET2 ASIC software parameters were evaluated. The benchtop setup used is provided with the TOFPET2 ASIC evaluation kit by PETsys Electronics S.A.

RESULTS

Compared to existing studies featuring the TOFPET2 ASIC, multi-channel performance results dependent on a larger set of ASIC configuration parameters were obtained that have not been reported to this extend so far. The ASIC shows promising CRTs down to 219.9 ps in combination with two Hamamatsu S14161-3050-HS-08 SiPM arrays (128 channels read out, energy resolution 13.08%) and 216.1 ps in combination with two Broadcom AFBR-S4N44P643S SiPM arrays (32 channels read out, energy resolution 9.46%). The length of the trigger delay of the dark count suppression scheme has an impact on the ASIC performance and can be configured to further improve the coincidence resolution time. The integrator gain configuration has been investigated and allows an absolute improvement of the energy resolution by up to 1% at the cost of the linearity of the energy spectrum.

CONCLUSION

Measuring up to the time-of-flight performance of state-of-the-art positron emission tomography (ToF-PET) systems while providing a uniform and stable readout for multiple channels at the same time, the TOFPET2 ASIC is treated as promising candidate for the integration in future ToF-PET systems.

Artificial neural networks for positioning of gamma interactions in monolithic PET detectors

To detect gamma rays with good spatial, timing and energy resolution while maintaining high sensitivity we need accurate and efficient algorithms to estimate the first gamma interaction position from the measured light distribution. Furthermore, monolithic detectors are investigated as an alternative to pixelated detectors due to increased sensitivity, resolution and intrinsic DOI encoding. Monolithic detectors, however, are challenging because of complicated calibration setups and edge effects. In this work, we evaluate the use of neural networks to estimate the 3D first (Compton or photoelectric) interaction position. Using optical simulation data of a 50×50×16 mm³LYSO crystal, performance is evaluated as a function of network complexity (two to five hidden layers with 64 to 1024 neurons) and amount of training data (1000 to 8000 training events per calibration position). We identify and address the potential pitfall of overfitting on the training grid through evaluation on intermediate positions that are not in the training set. Additionally, the performance of neural networks is directly compared with nearest neighbour positioning. Optimal performance was achieved with a network containing three hidden layers of 256 neurons trained on 1000 events/position. For more complex networks, the performance degrades at intermediate positions and overfitting starts to occur. A median 3D positioning error of 0.77 mm and a 2D FWHM of 0.46 mm is obtained. This is a 17% improvement in terms of FWHM compared to the nearest neighbour algorithm. Evaluation only on events that are not Compton scattered results in a 3D positioning error of 0.40 mm and 2D FWHM of 0.42 mm. This reveals that Compton scatter results in a considerable increase of 93% in positioning error. This study demonstrates that very good spatial resolutions can be achieved with neural networks, superior to nearest neighbour positioning. However, potential overfitting on the training grid should be carefully evaluated.

High-performance coded aperture gamma camera based on monolithic GAGG:Ce crystal

A 50 × 50 × 10 mm³ monolithic gadolinium aluminum gallium garnet (Gd₃Al₂Ga₃O₁₂; GAGG):Ce crystal coupled to a 8 × 8 silicon photomultiplier (SiPM) array was developed; it showed very good system uniformity and a high energy resolution of 7.4% at 662 keV. By using a convolutional neural network-based positioning algorithm and a fan-beam calibration method, the detector achieved a position resolution of ∼1.4 mm and a depth of interaction resolution of ∼2 mm. Based on this high-performance monolithic detector, we developed a coded aperture gamma camera. A 1-mCi Cs-137 source centered at a 2-m distance from the mask could be reconstructed with a signal-to-noise ratio of 6.5 in 1 s. Furthermore, the imaging ability of a low-energy Am-241 source and a low-activity Cs-137 source when the background-to-signal ratio was approximately 1:1 and a double low-activity source (Cs-137 and Na-22) was demonstrated. It is shown that the monolithic-crystal-based coded aperture gamma camera can achieve high performance and has a large potential for further improvement.

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

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%.

Emerging methods in radiology

Imaging modalities have developed rapidly in recent decades. In addition to improved resolution as well as whole-body and faster image acquisition, the possibilities of functional and molecular examination of tissue pathophysiology have had a decisive influence on imaging diagnostics and provided ground-breaking knowledge. Many promising approaches are currently being pursued to increase the application area of devices and contrast media and to improve their sensitivity and quantitative informative value. These are complemented by new methods of data processing, multiparametric data analysis, and integrated diagnostics. The aim of this article is to provide an overview of technological innovations that will enrich clinical imaging in the future, and to highlight the resultant diagnostic options. These relate to the established imaging methods such as CT, MRI, ultrasound, PET, and SPECT but also to new methods such as magnetic particle imaging (MPI), optical imaging, and photoacoustics. In addition, approaches to radiomic image evaluation are explained and the chances and difficulties for their broad clinical introduction are discussed. The potential of imaging to describe pathophysiological relationships in ever increasing detail, both at whole-body and tissue level, can in future be used to better understand the mechanistic effect of drugs, to preselect patients to therapies, and to improve monitoring of therapy success. Consequently, the use of interdisciplinary integrated diagnostics will greatly change and enrich the profession of radiologists.

Comments on the NEMA NU 4-2008 Standard on Performance Measurement of Small Animal Positron Emission Tomographs

The National Electrical Manufacturers Association’s (NEMA) NU 4-2008 standard specifies methodology for evaluating the performance of small-animal PET scanners. The standard’s goal is to enable comparison of different PET scanners over a wide range of technologies and geometries used. In this work, we discuss if the NEMA standard meets these goals and we point out potential flaws and improvements to the standard.For the evaluation of spatial resolution, the NEMA standard mandates the use of filtered backprojection reconstruction. This reconstruction method can introduce star-like artifacts for detectors with an anisotropic spatial resolution, usually caused by parallax error. These artifacts can then cause a strong dependence of the resulting spatial resolution on the size of the projection window in image space, whose size is not fully specified in the NEMA standard. If the PET ring has detectors which are perpendicular to a Cartesian axis, then the resolution along this axis will typically improve with larger projection windows.We show that the standard’s equations for the estimation of the random rate for PET systems with intrinsic radioactivity are circular and not satisfiable. However, a modified version can still be used to determine an approximation of the random rates under the assumption of negligible random rates for small activities and a constant scatter fraction. We compare the resulting estimated random rates to random rates obtained using a delayed coincidence window and two methods based on the singles rates. While these methods give similar estimates, the estimation method based on the NEMA equations overestimates the random rates.In the NEMA standard’s protocol for the evaluation of the sensitivity, the standard specifies to axially step a point source through the scanner and to take a different scan for each source position. Later, in the data analysis section, the standard does not specify clearly how the different scans have to be incorporated into the analysis, which can lead to unclear interpretations of publicized results.The standard’s definition of the recovery coefficients in the image quality phantom includes the maximum activity in a region of interest, which causes a positive correlation of noise and recovery coefficients. This leads to an unintended trade-off between desired uniformity, which is negatively correlated with variance (i.e., noise), and recovery.With this work, we want to start a discussion on possible improvements in a next version of the NEMA NU-4 standard.

Machine Learning in PET: from Photon Detection to Quantitative Image Reconstruction

Machine learning has found unique applications in nuclear medicine from photon detection to quantitative image reconstruction. While there have been impressive strides in detector development for time-of-flight positron emission tomography, most detectors still make use of simple signal processing methods to extract the time and position information from the detector signals. Now with the availability of fast waveform digitizers, machine learning techniques have been applied to estimate the position and arrival time of high-energy photons. In quantitative image reconstruction, machine learning has been used to estimate various corrections factors, including scattered events and attenuation images, as well as to reduce statistical noise in reconstructed images. Here machine learning either provides a faster alternative to an existing time-consuming computation, such as in the case of scatter estimation, or creates a data-driven approach to map an implicitly defined function, such as in the case of estimating the attenuation map for PET/MR scans. In this article, we will review the abovementioned applications of machine learning in nuclear medicine.

(Hybrid) SPECT and PET Technologies

SPECT and PET are nuclear tomographic imaging modalities that visualize functional information based on the accumulation of radioactive tracer molecules. However, SPECT and PET lack anatomical information, which has motivated their combination with an anatomical imaging modality such as CT or MRI. This chapter begins with an overview over the fundamental physics of SPECT and PET followed by a presentation of the respective detector technologies, including detection requirements, principles and different detector concepts. The reader is subsequently provided with an introduction into hybrid imaging concepts, before a dedicated section presents the challenges that arise when hybridizing SPECT or PET with MRI, namely, mutual distortions of the different electromagnetic fields in MRI on the nuclear imaging system and vice versa. The chapter closes with an overview about current hybrid imaging systems of both clinical and preclinical kind. Finally, future developments in hybrid SPECT and PET technology are discussed.

Sensors for Positron Emission Tomography Applications

Positron emission tomography (PET) imaging is an essential tool in clinical applications for the diagnosis of diseases due to its ability to acquire functional images to help differentiate between metabolic and biological activities at the molecular level. One key limiting factor in the development of efficient and accurate PET systems is the sensor technology in the PET detector. There are generally four types of sensor technologies employed: photomultiplier tubes (PMTs), avalanche photodiodes (APDs), silicon photomultipliers (SiPMs), and cadmium zinc telluride (CZT) detectors. PMTs were widely used for PET applications in the early days due to their excellent performance metrics of high gain, low noise, and fast timing. However, the fragility and bulkiness of the PMT glass tubes, high operating voltage, and sensitivity to magnetic fields ultimately limit this technology for future cost-effective and multi-modal systems. As a result, solid-state photodetectors like the APD, SiPM, and CZT detectors, and their applications for PET systems, have attracted lots of research interest, especially owing to the continual advancements in the semiconductor fabrication process. In this review, we study and discuss the operating principles, key performance parameters, and PET applications for each type of sensor technology with an emphasis on SiPM and CZT detectors—the two most promising types of sensors for future PET systems. We also present the sensor technologies used in commercially available state-of-the-art PET systems. Finally, the strengths and weaknesses of these four types of sensors are compared and the research challenges of SiPM and CZT detectors are discussed and summarized.

Optical simulation study on the spatial resolution of a thick monolithic PET detector

The intrinsic spatial resolution of clinical positron emission tomography (PET) detectors is ~3-4 mm. A further improvement of the resolution using pixelated detectors will not only result in a prohibitive cost, but is also inevitably accompanied by a strong degradation of important performance parameters like timing, energy resolution and sensitivity. Therefore, it is likely that future generation high resolution PET detectors will be based on continuous monolithic scintillation detectors. Monolithic detectors have attractive properties to reach superior 3D spatial resolution while outperforming pixelated detectors in timing, energy resolution and sensitivity. In this work, optical simulations including an advanced surface reflection model, allow us to investigate the influence of three parameters on the spatial resolution: silicon photomultiplier (SiPM) pixel size, photon detection efficiency (PDE) and the number of channels used to read out the SiPM array. A lutetium-yttrium oxyorthosilicate (LYSO) crystal with dimensions 50  ×  50  ×  16 mm³ coupled to an SiPM array is calibrated and a nearest neighbor (NN) algorithm is used to position events. Findings show that the tested parameters affect the spatial resolution resulting in 0.40-0.66 mm full width at half maximum (FWHM). Best resolution could be obtained with smaller SiPM pixels, higher PDE, and an individual channel readout. However, it was shown that combining channels by adding their signals can significantly reduce the amount of readout channels while having small or no significant impact on the resolution. The mean depth of interaction (DOI) estimation error is 1.6 mm. This study demonstrates the ultimate spatial resolution that can be obtained with this detector without being constrained by practical limitations of experimental setups. In the future these optical simulations may be used as a more precise and fast method to obtain calibration data for real monolithic detectors.