Online program Overview Session: M-LT-05

Abstract

Proton range verification (PRV) in proton therapy is an outstanding clinical need. Prompt-gammas (PG) emitted along the proton track constitute a strong signal to determine the Bragg Peak position. However, the high energy of PGs (1-7 MeV), the high particle flux and the presence of significant background radiation make prompt-gamma imaging (PGI) challenging.
We report on the development of pure Cherenkov pixelated detectors for PRV. The detector consisted of an 8×8 pixelated PbF2 crystal array coupled to an 8×8 S14161-3050HS-08 SiPM array from Hamamatsu, read out by a row-column signal processing board. Two analytical event reconstruction algorithms were evaluated: rise-to-the-power with k=2 (RTP2) and k=3 (RTP3).
The detector was exposed to both a sealed 228Th source and a 67.5 MeV proton beam at a current of 100 pA. Floodmaps were obtained using both RTP2 and RTP3 methods to resolve the 8×8 matrix of the crystal under laboratory source irradiation. Similar results were obtained under proton beam irradiation, showing the robustness of this detector concept.
In this contribution we demonstrate for the first time the ability of a pixelated pure Cherenkov detector to provide spatial resolution information in clinically relevant proton beam conditions. The prompt emission of Cherenkov light and exclusive sensitivity to highly energetic PGs offer a very high count rate capability, effective background rejection, and capacity to multiplex signals, enabling to use less channels and low power readout electronics. Future work will focus on quantitative floodmap quality metrics, detector modeling to optimize the detector geometry, and acquisition of additional data in the proton beam line at higher proton energies and beam currents.

Abstract

Accurate proton range verification (PRV) in proton therapy (PT) is an unmet clinical need and is essential to exploit the full potential of PT to maximize treatment effectiveness and minimize toxicity. We present a Monte Carlo (GATE 10) study of a Cherenkov-based prompt gamma imaging system combining an array of PbF2 crystal detectors with a MURA coded-aperture collimator for real-time PRV. The detector response was studied through detected Cherenkov photon distributions generated by prompt-gammas with energies ranging from 0.5 MeV to 7.0 MeV in steps of 0.5 MeV. A threshold of 15 detected Cherenkov photons was applied to reject low-energy background, which effectively suppressed 511 keV annihilation gammas and sub-MeV contributions while retaining the prompt gammas of interest. Image reconstruction was performed using the Maximum Likelihood Expectation Maximization algorithm, with the system matrix computed with 4.4 MeV point-like sources. The imaging performance was validated on isotropic gamma point sources in both 1D and 2D coded-mask configurations across a field of view of ∼130 mm. A direct comparison between discrimination based on energy deposition (E > 1 MeV) and discrimination based on the number of detected Cherenkov photons (nckv ≥ 15) showed comparable performance, with position residuals consistent with zero within statistical uncertainties in both cases. For the proton beam measurements, background rejection was performed exclusively via the Cherenkov photon count threshold. Under realistic proton beam conditions, the distal fall-off position was reconstructed with a systematic residual below 1.5 mm and a statistical precision of less than 2 mm for beam intensities above 10^8 protons, corresponding to approximately 10 ms of irradiation at a standard proton current of 2 nA (1.25 × 10^10 protons/s). This work presents the concept and shows the feasibility of using Cherenkov-based coded-aperture imaging for PRV by means of a simulation framework.

Abstract

Respiratory phase mismatches between PET and CT commonly cause artifacts in PET/CT, especially at lung–liver and lung–spleen interfaces. Since CT captures a single respiratory phase while PET reflects an average over breathing, this misalignment leads to attenuation correction errors and inaccurate lesion quantification, particularly near the liver dome. We propose a deep learning framework that uses PET-based pseudo-CT to guide deformable CT-to-PET alignment. A retrospective dataset of 10 18F-FDG exams with respiratory phase mismatch (banana) artifacts, scanned on a Total-Body BGO-based PET/CT scanner, was used for evaluation. A previously trained 3D residual neural network was used to generate pseudo-CT images from non-attenuation corrected (NAC) PET images. The pseudo-CT images, inherently aligned with the PET data, were then used as a reference for deformable registration of the CT images. The original and registered CT images were used for attenuation correction and reconstruction of the PET data using a BSREM algorithm. Regions of interested were placed in dome of the liver to quantitatively measure the SUV changes in those regions affected by the artifacts. Compared to baseline PET/CT without correction, the proposed method effectively reduced or eliminated respiratory-induced PET/CT misalignment artifacts were significantly reduced or eliminated. Quantitatively, the method achieved improvements in liver SUVmean towards the expected values. By leveraging the native alignment between PET and the generated pseudo-CT, the method avoids direct multimodal registration and instead enables accurate deformable alignment of CT to PET space. This approach improves attenuation correction and quantitative accuracy, with potential to enhance diagnostic confidence in routine clinical PET/CT imaging

Abstract

The HD-MetaPET project is developing a high-resolution positron emission tomography insert for simultaneous magnetic resonance imaging. The insert will consist of 120 Hyperion Visor Max detector tiles with an area of 56.5 x 56.5 mm², arranged in two semi-circles and coupled to LYSO:Ce crystal scintillators. A crucial optimisation parameter for the upcoming system performance is the crystal topology. This study evaluates Time-of-Flight and Depth-of-Interaction positioning performance across several crystal topologies: continuous slabs, pseudoslabs, and pixelated arrays with a light guide coupled to the face opposite the photosensitive detector. The study utilizes the Hyperion Visor Max and a custom development board, both featuring the TOFPET2 ASIC. Experimental measurements are performed using a ²²Na point source. The preliminary results with crystals of different materials show that pixelated arrays with light guides outperform continuous slabs. Specifically, pixelated designs achieved a Coincidence Time Resolution of ≈235 ps, compared to >400 ps for the slabs. The results indicate that the pixelated designs utilizing the light guide also demonstrated superior accuracy in the evaluation of the Depth-of-Interaction. The pixelated light-guide topology shows promising performance, circumventing the traditional compromise between spatial depth accuracy and timing resolution. Consequently, this configuration emerges as a good candidate for integration into the upcoming HD-MetaPET scanner. While these preliminary results are promising, the comprehensive evaluations utilizing final-scale crystals of the same material and the final scanner electronics will be presented at the conference.

Abstract

Tracer kinetic modeling using the two-tissue (2T) compartment model remains challenging in dynamic SPECT imaging, particularly under realistic noise conditions. In this study, we propose a noise-robust Bayesian parameter estimation method that incorporates physiological prior information for the 2T model in dynamic SPECT imaging. To validate the proposed method, we conducted both simulation study and dynamic ^99mTc-4BOH clinical study. The proposed method was compared with the conventional nonlinear least-squares (NLS) method. In the simulation study, the proposed method yielded lower relative bias in the estimated kinetic parameters (K₁, k₂, k₃, and k₄) than the NLS method under both low and high noise conditions. In the clinical ^99mTc-4BOH study, K₁ estimated using the proposed method demonstrated significantly stronger agreement and lower relative bias with respect to head-to-head ¹³N-NH₃ PET myocardial blood flow (MBF), which was used as the reference standard, than the NLS method. In conclusion, the proposed method substantially improved the accuracy and precision of parameter estimation for the 2T model compared with the NLS method.

Abstract

We propose a kinetic-consistent conditional flow matching framework for dynamic [11C raclopride PET SRTM parametric imaging. Conventional indirect SRTM fitting is vulnerable to frame-wise count noise, causing unstable voxel-wise R1 , k2 , and BPND maps. Using 105 scans from 35 subjects, count-reduced datasets were generated at 50%, 25%, and 10% of the original counts. The model refines reduced-count indirect SRTM estimates toward full-count SRTM pseudo-reference maps using kinetic consistency, count/noise conditioning, and physiological constraints, reducing BPND  deviation, high-percentile errors, and kinetic-parameter disagreement compared with indirect SRTM and denoising and regularization-based baselines

Abstract

Metal implants can degrade CT image quality and introduce artifacts that affect dose calculation in particle therapy. This study investigates attenuation images (µ‑MLAA) reconstructed from time‑of‑flight PET data using maximum‑likelihood reconstruction of attenuation and activity for metal artifact reduction. The linearity of a calibration curve relating CT number to relative electron density was first evaluated using a PMMA phantom with metal and tissue‑equivalent inserts. Metal artifacts were then studied in a water phantom containing a Ti6Al4V total endoprosthesis (TEP) implant. µ‑MLAA attenuation images reconstructed from PET list mode data and CT images reconstructed using iterative metal artifact reduction (iMAR) were evaluated using gamma index analysis (10%, 1 mm) relative to an artifact‑free reference slice. The calibration curve remained linear in the presence of metal inserts (R² > 0.999 ). For the TEP phantom, µ‑MLAA images achieved a higher gamma pass rate (96.3%) than iMAR CT (70.8%). Using the calibration curve, the relative electron density of the TEP shaft was estimated as n_{e, rel} = 3.58±0.12  corresponding to a 3.5% deviation from reported reference values. These results indicate the potential of µ‑MLAA images for reducing metal artifacts and supporting implant characterization in radiotherapy imaging.

Abstract

Purpose: We are developing a Personalized Remote Radiation Tracking Portable Organ Dosimetry Device (PRRT PODD) for at-home monitoring of patients receiving radiopharmaceutical therapy (RPT). We report on an FPGA-based data acquisition system capable of wireless communication in support of this goal.

Methods: Our acquisition system supports16-radiation detectors and outputs an energy histogram for each using a time-over-threshold (ToT) energy measurement. The electronics consists of a custom analog board that converts each detector's analog signal into a digital ToT signal; an FPGA board for signal processing; and a microcontroller board that serves as the system controller. The microcontroller communicates with the FPGA via UART to retrieve energy histogram data, manages system configuration parameters, and provides Bluetooth Low Energy connectivity to a mobile Android app. The electronics fits within the PODD’s base assembly and can operate on cell phone battery power. Each detector has a small thermistor for individual probe temperature monitoring and to characterize MPPC gain versus temperature. An Android app was developed to control data acquisition and wireless transfer of data to a remote workstation. The detector and electronics acqusition system was tested using Tc-99m, Na-22 and Lu-177 radioactive sources at clinically relevant counting rates.

Results: Sixteen channel operation of our FPGA-based data acquisition system has been validated using a variety of radionuclides. The output properties of the probes have been characterized versus temperature to enable post acquisition normalization of signals.

Conclusion and future work: A compact, wireless 16-channel gamma spectroscopy system for remote monitoring of Lu-177 RTP was designed and tested. Future work includes implementing real-time temperature compensation based on continuous temperature readings, ensuring stable operation without post-processing.

Abstract

While recent deep learning techniques for accelerating Monte Carlo (MC) radiotherapy dose calculation have shown promising results, most state-of-the-art methods rely on a denoising paradigm. This approach aims to predict a high-sampling (HS) reference dose map from a fast low-sampling (LS) input. However, information loss and stochastic noise in LS inputs often limit the accurate recovery of fine structural details and dose gradients, and may lead to hallucinations. To address these limitations, we propose a novel learning framework, Energy-Shifting, which reconstructs a time-intensive polyenergetic HS dose distribution from a GPU-accelerated monoenergetic dose map. Our results demonstrate that this method significantly outperforms conventional denoising approaches in accuracy.

Abstract

In proton therapy, range uncertainties can result in insufficient dose delivery to the tumor or unintended dose delivery to organs at risk (OARs). To address this, several range verification methods have been investigated, and prompt gamma imaging is considered a promising method due to the strong correlation between the dose profile and prompt gamma distribution at the distal dose fall-off location. Based on prompt gamma imaging, a treatment monitoring system was developed to measure the in vivo beam range and detect range shifts during the treatment. The developed system consists of a 72-slit parallel collimator, 144 CsI(Tl) scintillation detectors, a dedicated field-programmable gate array (FPGA)-based data acquisition (DAQ) system, and a dedicated program with a graphical user interface (GUI). The DAQ system calculates pulse height and timing of each signal from 144 detectors. The dedicated program reconstructs 1D prompt gamma distribution using pulse data and determines the beam range of each spot using a dedicated range estimation algorithm [1]. The system's performance was evaluated using a clinical proton beam prescribed to deliver 0.5 Gy to a spherical clinical target volume (CTV) with 50 mm diameter in a homogeneous PMMA phantom. To evaluate range shift detection performance, range shifts were induced at both the global range shift (1–10 mm) and local range shift (5 and 10 mm) conditions. At the same time, the computation time for range deviation analysis was evaluated. The system detected global range shifts with an accuracy of 1 mm and identified the presence and location of local range shifts exceeding 5 mm. Furthermore, the time required for range deviation analysis per layer was found much shorter than energy layer switching time. These results demonstrate the system can verify the in vivo beam range during treatment in clinical practice.