Improving the Timing Resolution of Positron Emission Tomography Detectors Using Boosted Learning-A Residual Physics Approach

ASICs in PET: what we have and what we need

BACKGROUND

Designing positron emission tomography (PET) scanners involves several significant challenges. These include the precise measurement of the time of arrival of signals, accurate integration of the pulse shape, maintaining low power consumption, and supporting the readout of thousands of channels. To address these challenges, researchers and engineers frequently develop application-specific integrated circuits (ASICs), which are custom-designed readout electronics optimized for specific tasks. As a result, a wide range of ASIC solutions has emerged in PET applications. However, there is currently no comprehensive or standardized comparison of these ASIC designs across the field.

METHODS

In this paper, we evaluate the requirements posed to readout electronics in the field of PET, give an overview of the most important ASICs available for PET applications and discuss how to characterize their essential features and performance parameters. We thoroughly review the hardware characteristics of the different circuits, such as the number of readout channels provided, their power consumption, input and output design. Furthermore, we summarize their performance as characterized in literature.

RESULTS

While the ASICs described show common trends towards lower power consumption or a higher number of readout channels over the past two decades, their characteristics and also their performance assessment by the developers, producers and vendors differ in many aspects. To cope with the challenge of selecting a suitable ASIC for a given purpose and PET application from the varying information available, this article suggests a protocol to assess an ASIC's performance parameters and characteristics.

CONCLUSION

ASICs developed for PET applications are versatile. With novel benchmarks set for the impact of scintillator and photosensor on the time-of-flight performance, the pressure on ASICs to deliver higher timing resolution and cope with an even higher data rate is enormous. Latest developments promise new circuits and improvements in time-of-flight performance. This article provides an overview on existing and emerging readout solutions in PET over the past 20 years, which is currently lacking in literature.

3D in-system calibration method for PET detectors

BACKGROUND

Light-sharing detector designs for positron emission tomography (PET) systems have sparked interest in the scientific community. Particularly, (semi-)monoliths show generally good performance characteristics regarding 2D positioning, energy-, and timing resolution, as well as readout area. This is combined with intrinsic depth-of-interaction (DOI) capability to ensure a homogeneous spatial resolution across the entire field of view (FoV). However, complex positioning calibration processes limit their use in PET systems, especially in large-scale clinical systems.

PURPOSE

This work proposes a new 3D positioning in-system calibration method for fast and convenient (re-)calibration and quality control of assembled PET scanners. The method targets all kinds of PET detectors that achieve the best performance with individual calibration, including complex segmented detector designs. The in-system calibration method is evaluated and empirically compared to a state-of-the-art fan-beam calibration for a small-diameter proof of concept (PoC) scanner. A simulation study evaluates the method's applicability to different scanner geometries.

METHODS

A PoC scanner geometry of 120 mm inner diameter and 150 mm axial extent was set up consisting of five identical finely segmented slab detectors (one detector under test and four collimation detectors). A 2 2Na point source was moved in a circular path inside the FoV. Utilizing virtual collimation and by selecting gamma rays incident approximately perpendicular to the detector normal of the detector under test, training data was created for the training of a 2D positioning model with the machine-learning technique gradient tree boosting (GTB). Data with oblique ray angles was acquired in the same measurement for subsequent angular DOI calibration. For this, a 2D position estimate in the detector under test was calculated first. On this basis, the DOI label was calculated geometrically from the ray path within the detector to finally establish up to 3D training data.

RESULTS

With a mean absolute error (MAE) of 0.8  and 1.19 mm full-width at half maximum (FWHM) along the planar-monolithic slab dimension, the in-system methods performed similarly within 1% to the fan-beam collimator results. The DOI performance was at ∼90% with 1.13 mm MAE and 2.47 mm FWHM to the fan-beam collimator. Analytical calculations suggest an improved performance for larger scanner geometries.

CONCLUSION

The functionality of the 3D in-system positioning calibration method was successfully demonstrated with the measurements within a PoC scanner configuration with similar positioning performance as the bench-top fan-beam setup. The in-system calibration method can be used to calibrate and test fully assembled PET systems to enable more complex light-sharing detector architectures in, for example, large PET systems with many detectors. The acquired data can further be used for more complex energy and time calibrations.

Design and characterization of a hybrid PET detector with DOI capability

BACKGROUND

Monolithic or semi-monolithic detectors are attractive for positron emission tomography (PET) scanners with depth-of-interaction (DOI) capability. However, they often require complicated calibrations to determine the interaction positions of gamma photons.

PURPOSE

We introduce a novel hybrid detector design that combines pixelated and semi-monolithic elements to achieve DOI capability while simplifying the calibrations for positioning.

METHODS

A prototype detector with eight hybrid lutetium-yttrium oxyorthosilicate (LYSO) layers having dimensions of 25.8 × 12.9 × 15 mm3 was constructed. The energy-weighted and energy-squared weighted averages were used for estimating the x- (pixelated direction) and y-positions (non-pixelated direction). Pseudo-pixels were defined as discrete areas on the flood image based on the crystal look-up table (LUT). The intrinsic spatial resolutions in the pixelated and non-pixelated directions were measured. The ratio of the maximum to the sum of the multipixel photon counter (MPPC) signals was used to estimate the DOI positions. The coincidence timing resolution (CTR) was measured using the average and energy-weighted average of the earliest n time stamps. Two energy windows of 250-700 and 400-600 keV were applied for the measurements.

RESULTS

The pattern of the flood images showed discrete event clusters, demonstrating that simple calibrations for determining the x- and y-positions of events could be achieved. Under 400-600 keV energy window, the average intrinsic spatial resolutions were 1.15 and 1.34 mm for the pixelated and non-pixelated directions; the average DOI resolution of the second row of pseudo-pixels was 5.1 mm in full width at half maximum (FWHM); when using the energy-weighted average of the earliest four-time stamps, the best CTR of 350 ps was achieved. Applying a broader energy window of 250-700 keV only slightly degrades the DOI resolution while maintaining the intrinsic resolution; the best CTR degrades to 410 ps.

CONCLUSIONS

The proposed hybrid detector concept was verified, and a prototype detector showed high performance for 3D positioning and timing resolution. The novel detector concept shows promise for preclinical and clinical PET scanners with DOI capability.

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.

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.