Online program Overview Session: M-LT-04

Abstract

Cell tracking in vivo is a key challenge in translational medicine. Positron emission tomography (PET) and magnetic resonance imaging (MRI) offer complementary strengths for this task, yet the validation of tracking algorithms against a reliable ground truth remains unresolved. Fluorescence microscopy can fulfill this role, but its integration into the highly constrained PET/MRI environment poses significant technical challenges. Here, we present a fluorescence microscope (FM) designed for simultaneous operation inside a small-animal PET/MRI scanner, with all custom structural and housing components 3D printed in polylactic acid (PLA). The assembled instrument has a total length of 95 cm and fits within the 35 mm PET insert bore, accommodating sample diameters of up to 21 mm. The imaging probe is rotated via a non-ferromagnetic, friction-driven mechanism, enabling multi-angle acquisition without repositioning the sample, and reliable rotation over the full 360° range was demonstrated under manual control. The system follows an epi-fluorescence configuration with 10× magnification, an excitation wavelength of 488 nm, and a 500 nm longpass filter in the detection path. Characterization with a calibration slide and a Convallaria majalis test specimen yielded a pixel pitch of 0.144 μm/pixel (6.96 pixels/μm), a spatial resolution of 2.32 μm (FWHM), a signal-to-noise ratio (SNR) of 7.21, and a field of view (FoV) of 0.55 × 0.31 mm2 (3816 × 2160 pixels), sufficient to resolve individual cells. These results establish the feasibility of integrating fluorescence microscopy into a PET/MRI scanner and provide the basis for future multimodal cell tracking studies with a reliable optical ground truth.

Abstract

Hadron therapy is a highly conformal cancer treatment technique that enables superior dose delivery to the tumor while sparing surrounding healthy tissues. However, its full clinical benefit is still limited by uncertainties in proton range within tissue, mainly due to the indirect estimation of ion stopping power from X-ray CT images. These uncertainties lead to safety margins that reduce treatment precision. Real-time, high-accuracy ion-range verification is essential to improve dose delivery and further enhance the therapeutic potential of proton therapy. Among the available approaches, PGI and PET are regarded as the most promising, as they provide complementary information. PGI offers direct correlation with the Bragg peak during irradiation, whereas PET yields high-resolution activity distributions from beta+ emitters produced along the proton path.

In this work, we report recent developments and experimental results obtained with a hybrid PGI-PET system for in situ proton range verification. The system is based on four high-efficiency two-plane Compton imagers arranged in a cross configuration, specifically optimized to enhance PGI sensitivity at the expense of a reduced PET field of view. Experimental campaigns were carried out at commercial IBA Proteus Plus and Proteus One proton therapy facilities using 100 and 125 MeV pencil beams delivered at minimum clinical currents. Polyethylene phantoms were irradiated at controlled positions and data were acquired during both in-spill and off-spill periods.

The first reconstructed 2D images demonstrate that PGI can directly identify the Bragg peak position through the maximum of the PGI distribution over a broad field of view, while PET provides a narrower and higher-resolution distribution associated with the induced beta+ activity. These results confirm the feasibility and complementarity of the hybrid PGI-PET approach and support its potential for accurate, clinically relevant proton range verification.

Abstract

We introduce a novel quantum-inspired method for identifying intercrystal Compton scattering in pixelated scintillation detectors. The approach treats each SiPM readout as a spatial signal and converts it into a discrete eigenfunction representation using a Hamiltonian built from the measured intensity distribution. Event structure is then assessed through quantum localization, quantified by the participation ratio. Single-site events produce highly localized dominant modes, whereas Compton-scattered events produce multiple localized modes associated with distinct interaction sites. A dual criterion based on quantum localization and eigenvalue proximity is used to classify multi-site events without calibration-intensive response modeling or supervised training. The method was evaluated with Geant4 optical photon simulations and measurements from LYSO:Ce detector arrays with thicknesses from 2 to 25 mm. Experimental classification agreed closely with simulation, with differences below 1% for most thicknesses and below 5% in all cases. These results indicate that the proposed representation captures the topology of gamma-ray interactions directly from detector signals.

Abstract

Positron emission tomography systems are becoming increasingly complex due to the growing number of detector channels and the large amount of data generated, especially in total-body imaging configurations. In this work, we present PETRARCA, a positron emission tomography detector system integrating an analog neural network ASIC (ANNA) for real-time edge computing of gamma-ray interaction coordinates within a monolithic scintillation crystal. The system consists of a 32 mm × 32 mm × 10 mm monolithic LYSO crystal coupled to an 8 × 8 SiPM matrix read out by dedicated front-end electronics. The detector architecture includes both a conventional digital acquisition path and a parallel analog inference path based on the ANNA ASIC, enabling simultaneous acquisition of sensor data and hardware neural-network predictions. A first evaluation of the system has been carried out using simulated data and the real ASIC response to emulate a reduced-scale configuration using a 4 × 4 SiPM array coupled to a 26 mm × 26 mm × 10 mm monolithic crystal. The performances of the ASIC in predicting the interaction coordinates were tested, and proved to have a mean absolute error (MAE) of around 1 mm on both the x and y axes. The preliminary results demonstrate the feasibility of analog neural-network edge computing for spatial localization in PET detectors and support further developments toward fully integrated real-time detector systems.

Abstract

Monte Carlo simulations play a vital role in many aspects of proton therapy treatment, including proton depth of penetration monitoring. A key limiting factor of full simulation engines is computation time: simulations can take several days, far longer than the time between treatment fractions. This project aims to develop a Monte Carlo simulation software, MIMOSA, for prompt gamma-ray based proton range verification, that is fast enough to be deployed before the next treatment fraction. Clinically acceptable gamma pass rates above 99% for 2%/2 mm and 97% for 1%/1 mm with respect to TOPAS were achieved on both homogenous and heterogenous phantoms. To benchmark computational speed, state-of-the-art GPU codes MOQUI and FRED were tested on the same computer. Comparatively, MIMOSA achieved a notable speed increase, able to simulate 1e9 primary protons with a 200 MeV energy in approximately 3 minutes. Along with maintaining accuracy and increasing computational speed, the project uses the hardware agnostic SYCL standard, and will be available with an open-source license and extensive documentation. Ongoing work is devoted to simulating realistic radiotherapy treatment plans, and including prompt gamma-ray transport and detector response.

Abstract

Compton scattering in Positron Emission Tomography (PET) is commonly seen as an important degrading factor for the image quality. In particular, object scattering is known to reduce  image contrast and distort quantification. Conventional scatter correction techniques do not model scatter directly in the system matrix, despite its potential to better handle low-count data scenarios. In this work, we propose an analytical model for single Compton scattering events and subsequently derive expressions of the system matrix and sensitivity. We validate the obtained expressions with Monte Carlo simulations. Ongoing work includes studying the impact of such approach by using low-count simulated data, and a comparison of the proposed method with conventional scatter correction techniques.

Abstract

Inter-crystal scatter (ICS) is a primary limiting factor for spatial resolution and sensitivity in ultra-high resolution (UHR) PET imaging. This study provides a multiparametric quantification of ICS distributions across three successive LabPET-II scanner generations: Mouse, UHR, and SAVANT. Using GATE Monte Carlo simulations, we evaluate the complex tradeoff between substantial sensitivity gains and increased scatter contamination. Results demonstrate that while the SAVANT architecture provides a 39.9% increase in coincidence yield over the UHR, ICS events constitute 54.21% of total counts. These events are characterized through stable energy deposition profiles (approx. 0.326 MeV), multi-rank angular distributions, and a refined Line of Response (LOR) midpoint error metric.

A notable paradox is observed: the aggregate positioning error is lower for the SAVANT (11.01 mm) than for the UHR (12.22 mm), suggesting that the SAVANT's optimized geometric design constrains LOR deviations despite a higher scatter frequency. Furthermore, rank-based angular analysis confirms the invariance of primary physical signatures, with the first scattering angle ($\theta_0$) remaining remarkably consistent across all architectures (91.9° to 94.4°). This stability across the LabPET-II lineage establishes a robust physical framework for a universal deep learning-based positioning algorithm. By transforming scatter into a valuable signal rather than treating it as noise, this approach leverages the exceptional sensitivity of next-generation scanners to surpass current resolution limits through intelligent software processing.

Abstract

We have developed a 3-layer staggered LYSO de tector with DOI capability within the project SIRMIO (Small animal proton irradiator for research in molecular image-guided radiation-oncology) for proton range verification via PET imag ing. To evaluate the feasibility of detecting prompt gamma rays for quasi-instantaneous monitoring of proton beams, we propose a workflow that includes the validation of optical simulation settings and the implementation of the saturation effect of the SiPM photonsensor in the Geant4 simulation. Measurement data with ¹⁵²Eu, ²²Na and ¹³⁷Cs radioactive sources are utilized for modeling the saturation effect of SiPM, and also for validating optical boundary settings in Geant4. The measured spectra of ²²Na and ¹³⁷Cs point sources are well reproduced with the proposed simulation workflow. The responses to prompt gamma rays are then simu lated. According to the floodmaps and profiles, LYSO pixel can be identified at 2.2, 4.4 and 6.2 MeV, but the contrast decreases strongly when energy reaches 6.2 MeV. The peak-to-valley ratios drop by up to 53% at 4.4 MeV and 62% at 6.2 MeV (relative to 2.2 MeV), primarily due to the energy deposition occurring more frequently in multiple LYSO pixels. These predictions suggest the detector’s potential for detecting prompt gamma rays, while accurately recovering or rejecting inter-crystal scatter events is essential to maintain detector performance at higher gamma energy.

Abstract

We have developed a 3-layer staggered LYSO DOI detector for the SIRMIO (Small animal proton irradiator for research in molecular image-guided radiation-oncology) project, with a crystal pitch of 1.0 mm. Due to the small crystal size, inter-crystal scatter (ICS) events degrade spatial resolution, while discarding them causes significant sensitivity loss. In our detector, ICS events account for approximately 50% of all interactions at 511 keV and result in a median lateral positioning error of approximately 2 mm. To solve this problem, we propose a dual-head convolutional neural network (CNN) for separating the 8×8 MPPC total response of two-pixel ICS events into two individual response matrices, from which the pixel positions and energy fractions can be obtained—a precondition for recovering the ICS events. The CNN correctly identifies both scattering pixels in 69.3% of test events, and in 26.4% of cases one pixel is correctly identified. Among the events with one pixel mismatched, the lateral error of the misidentified pixel falls within 1.0 mm in 91.0% of cases. Meanwhile, the median energy fractions prediction error is 0.016 per pixel. The results demonstrate that CNN-based MPPC response decomposition can serve as the enabling foundation for complete ICS recovery in 3-layer staggered DOI detectors, addressing a limitation explicitly acknowledged in recent physics-based ICS studies.

Abstract

MERMAID is the first PET scanner prototype dedicated to study small aquatic animals. Particularly, it aims to support the use of adult zebrafish as a model of human diseases. The current prototype, which consists of four detection modules composed of LYSO:Ce crystals, has demonstrated the capability of providing images with submillimeter resolution. An extension of MERMAID by another four modules is planned, aimed  to increase the system sensitivity and allow for shorter scan times, making it more suitable for in-vivo imaging. In the presented work, various configurations for the extended MERMAID system are characterized by means of simulations and tailored reconstruction framework; modifications of imaging protocol for the system are  investigated.