David Schug

Objective. In preclinical research, in vivo imaging of mice and rats is more common than any other animal species, since their physiopathology is very well-known and many genetically altered disease models exist. Animal studies based on small rodents are usually performed using dedicated preclinical imaging systems with high spatial resolution. For studies that require animal models such as mini-pigs or New-Zealand White (NZW) rabbits, imaging systems with larger bore sizes are required. In case of hybrid imaging using positron emission tomography (PET) and magnetic resonance imaging (MRI), clinical systems have to be used, as these animal models do not typically fit in preclinical simultaneous PET-MRI scanners. Approach. In this paper, we present initial imaging results obtained with the Hyperion II^D PET insert which can accommodate NZW rabbits when combined with a large volume MRI RF coil. First, we developed a rabbit-sized image quality phantom of comparable size to a NZW rabbit in order to evaluate the PET imaging performance of the insert under high count rates. For this phantom, radioactive spheres with inner diameters between 3.95 and 7.86 mm were visible in a warm background with a tracer activity ratio of 4.1 to 1 and with a total ¹⁸F activity in the phantom of 58 MBq at measurement start. Second, we performed simultaneous PET-MR imaging of atherosclerotic plaques in a rabbit in vivo using a single injection containing ¹⁸F-FDG for detection of inflammatory activity, and Gd-ESMA for visualization of the aortic vessel wall and plaques with MRI. Main results. The fused PET-MR images reveal ¹⁸F-FDG uptake within an active plaques with plaque thicknesses in the sub-millimeter range. Histology showed colocalization of ¹⁸F-FDG uptake with macrophages in the aortic vessel wall lesions. Significance. Our initial results demonstrate that this PET insert is a promising system for simultaneous high-resolution PET-MR atherosclerotic plaque imaging studies in NZW rabbits.

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.

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 × 10 ⁸ protons (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.

In PET a high percentage of gamma photons being detected undergo Compton scattering in the scintillator. Scintillator blocks are often built from optically isolated crystals. Depending on the angle of incidence and the scintillator geometry this might lead to inter crystal scatter (ICS) events, where energy is deposited in two or more crystals in the detector, which common positioning and reconstruction algorithms cannot resolve. Therefore, ICS events worsen the spatial resolution and the signal-to-noise ratio in the reconstructed image. We want to address this challenge by recovering individual crystals from ICS events with their corresponding energy deposits. This information could ultimately be fed into an image reconstruction framework. In this work, we established an algorithm based on a detector that couples a readout channel to each crystal (one-to-one coupling), which combines a measurement-driven calibration and a fitting routine to achieve the recovery of crystal interactions from measured light patterns. Using Geant4 simulations, we validated and optimized this approach by comparing the recovered events to the simulation ground truth. We showed that, with the best performing algorithm versions, all correct crystals could be identified for 95-97% of the simulated events and the crystal energies as well as the event energy sum could be recovered adequately. For the event energy sum a deviation of less than 5% could be achieved for 96% of all events. Overall, the developed ICS recovery algorithm was successfully validated for one-to-one coupled detector. Future application for other detector configurations should be possible and will be investigated. Additionally, using the new crystal interaction information to determine the most likely first interaction crystal is being examined to improve efficiency and signal-to-noise ratio in the PET reconstruction.

Artificial intelligence (AI) is entering medical imaging, mainly enhancing image reconstruction. Nevertheless, improvements throughout the entire processing, from signal detection to computation, potentially offer significant benefits. This work presents a novel and versatile approach to detector optimization using machine learning (ML) and residual physics. We apply the concept to positron emission tomography (PET), intending to improve the coincidence time resolution (CTR). PET visualizes metabolic processes in the body by detecting photons with scintillation detectors. Improved CTR performance offers the advantage of reducing radioactive dose exposure for patients. Modern PET detectors with sophisticated concepts and read-out topologies represent complex physical and electronic systems requiring dedicated calibration techniques. Traditional methods primarily depend on analytical formulations successfully describing the main detector characteristics. However, when accounting for higher-order effects, additional complexities arise matching theoretical models to experimental reality. Our work addresses this challenge by combining traditional calibration with AI and residual physics, presenting a highly promising approach. We present a residual physics-based strategy using gradient tree boosting and physics-guided data generation. The explainable AI framework SHapley Additive exPlanations (SHAPs) was used to identify known physical effects with learned patterns. In addition, the models were tested against basic physical laws. We were able to improve the CTR significantly (more than 20%) for clinically relevant detectors of 19height, reaching CTRs of 185(450–550 keV).

Resolving the depth of interaction (DOI) of a γ-photon in the scintillator is necessary to correct for parallax errors in organ-dedicated and large-scale time-of-flight positron emission tomography (TOF-PET) scanners or enable the precise recovery of Compton-scattered γ-photons. Doubling the number of readout channels and moving towards more complex detector designs are methods to encode DOI, often associated with high costs. We propose a DOI-capable TOFPET detector unit concept confining light-sharing to two detector channels, where the high benefit lies in scalability and the prospect of Compton recovery between adjacent units. We evaluate these scalable, DOI-capable unit concepts, realizing DOI encoding between two LYSO:Ce,Ca crystals (3x3x20 mm3; Taiwan Applied Crystals) one-to-one coupled to two Broadcom AFBR-S4N33C013 silicon-photomultipliers (SiPMs) read out with the TOFPET2 ASIC. The best-performing unit employing a triangular reflector sheet and optical glue between the two crystals and mounted on two FBK NUV-MT SiPMs results in a DOI resolution of about 3 mm (RMSE) based on the energy ratio digitized by the two channels while maintaining a coincidence time resolution (CTR) of 226 ps (FWHM) with TOFPET2 ASIC readout, applying a linear DOI correction. Using HF readout, the CTR of the proposed detector unit was improved to 141 ps (FWHM).

The Unity 1.5T MR-linac has clinically been introduced to provide stereotactic precision for moving targets. We are now developing an integrated 1.5T MRI/PET system with identical MRI performance for planning of MR-linac treatments. The system offers 70 cm bore size and includes PET detectors mounted in the gap of a split gradient coil. Recently, the first ring of PET detectors was installed. We performed extensive interference tests and acquired the first simultaneous images using a brain phantom. Our vision is that the combination of MR-linac with MRI/PET will allow the use of PET information for stereotactic radiation therapy guidance.

Typical positron emission tomography (PET) detectors consist of one-layer segmented scintillators coupled to silicon photomultipliers (SiPMs). Light-sharing detectors, e.g., semi-monoliths, additionally provide depth-of-interaction (DOI) estimation, performing best when calibrated individually. To establish those designs in large PET systems, scalable (re-)calibration methods are needed, possibly transferable to assembled systems. Here, two DOI calibration methods, potentially allowing in-system calibration, are evaluated and compared with an established calibration scheme. Both methods are based on angular detector irradiation using a fan-beam slit collimator and gradient tree boosting (GTB) for 3-Dimensional (3D) position estimation. The positioning performance was assessed for irradiation angles between 0∘ (lateral) and 90∘ (detector normal). With lateral irradiation, a unique DOI position is given, whereas with angular irradiation a gamma-individual reference position must be retrieved. The first method employs one angular beam and calculates DOI from the beam path and planar position estimation. The second method uses two intersecting beams. The intersection defines DOI for gamma interactions that are spatially localized there. Those gamma photons are identified by light distribution comparison using a nearest-neighbor routine. The methods were evaluated on a semimonolithic LYSO slab detector (32×3.9×19 mm slabs). Both methods performed similarly to the benchmark lateral irradiation within 1 %, and 6 %, respectively, for shallow irradiation angles up to 45∘.

In modern large-scale PET systems, transferring the digitized raw detector data at high count rates to a centralized processing unit is a challenge. Processing data on FPGAs close to the detectors can reduce data early on and improve scalability of the PET system. We present and evaluate an FPGA implementation of gradient tree boosting (GTB) for one-dimensional position estimation of gamma interactions in the scintillator. GTB is a supervised machine learning algorithm based on building ensembles of binary decision trees. Models were trained offline and inferred in an FPGA (XC7K410T-2FFG676 Kintex-7). Input features and GTB parameters influencing both positioning performance and model size were varied while evaluating the inferred models concerning data throughput and FPGA resource consumption as well as positioning performance. We achieved throughputs per detector between 2.94 × 106 and 4.55 × 106 gamma interactions per second. For an optimized GTB model, resource consumption could be reduced by factors of 17 and 10 to less than 1% (2.51 × 103 look-up tables) of available logic and 1.26% (20 BRAMs) of memory resources, while maintaining a positioning performance of 98.63% when compared to the model with the best positioning performance. The presented framework can be easily adapted to other photosensors and scintillator configurations.