Online program Overview Session: M-LT-06

Abstract

Minimally-invasive radio-guided surgery requires simultaneous functional nuclear guidance and anatomical visualization to enable precise intraoperative localization of radiolabeled targets [1]. We propose a dual-modality imaging drop-in capsule designed for deployment through a standard 15-mm laparoscopic trocar. It consists of a 2-mm thick semiconductor detector with a sub-millimeter pixel pitch [2,3], a custom lofthole collimator embedded within a 3D-printed tungsten capsule, and two miniaturized stereoscopic micro-cameras.

Preliminary results show: real-time 3D depth maps validated against an ultrasonic distance sensor; a seamless 360° panoramic view stitched from sequential frames using cylindrical warping and multi-band Laplacian blending [4,5]; spectral localization of multiple isotopes ⁵⁷Co and ²⁴¹Am within a scattering biological medium; and a preliminary 2D co-registration overlaying the functional dataset onto the anatomical scene. We will present the system design and components, and discuss the steps towards a fully integrated prototype for laparoscopic radio-guided surgery.

Abstract

Lutetium-177 (¹⁷⁷Lu)  is one of the most representative radionuclides in integrated nuclear medicine theranostics. To image ¹⁷⁷Lu, we have developed a full-ring, high-sensitivity cascade gamma photon coincidence imaging system. This work presents the system construction and preliminary performance evaluation.

The detector ring consists of 40 continuous LaBr₃ crystal modules arranged in 10 radial × 4 axial configuration (each 50 × 50 × 6 mm³). The inner diameter of the ring is 110 mm, with axial module spacing of 60 mm. Each crystal is coupled to four MPPC arrays and read out via PETsys FEM256 electronics, with three FEB/D boards and one DAQ for data acquisition. Both the ASICs and MPPCs are cooled by a water-cooling system, maintaining water at 8 °C and ASIC temperature below 20 °C.

The collimator assembly comprises 10 tungsten plates (26 × 128 × 8 mm³), each covering four axial detector modules. Plates are arranged alternately with pinholes and slits (1 mm), placed at a vertical distance of 36.5 mm from the ring center, creating an imaging field of Φ30 × 30 mm. The prototype hardware has been fully assembled and is capable of normal data acquisition.

Simulation results indicate a coincidence sensitivity of 2.97 × 10⁻⁵ at the central field of view, spatial resolution of ~1.7 mm, and hot cylindrical object resolution up to 1.2 mm. Preliminary experimental tests have validated the feasibility of the system. Ongoing optimization includes crystal position calibration, energy resolution improvement, and collimator geometry alignment. The full system performance evaluation is expected to be completed before the upcoming conference.

Abstract

In matter, a positron annihilates with an electron into two photons through several mechanisms, including direct annihilation and the formation of positronium, a metastable bound state that exists as either para-positronium or ortho-positronium. Each channel has a characteristic lifetime, allowing their contributions to be distinguished in time. The two photons are predicted to be entangled in their polarizations [1], and recent measurements indicate that the degree of entanglement depends on the annihilation channel [2]. In particular, the ortho-positronium decay is sensitive to the local concentration of molecular oxygen, and can serve as a probe of tissue hypoxia in PET imaging [3]. The Jagiellonian Positron Emission Tomograph is a scanner built from plastic scintillators, in which 511 keV photons interact predominantly through Compton scattering [4]. The Compton scattering plane of each photon is correlated with its polarization. We measure annihilations of positrons emitted by a sodium-22 source embedded in porous polymer. Sodium-22 emits a positron accompanied by a 1274 keV prompt photon, which provides a time reference for the positron lifetime. The polarization correlation distribution of the annihilation photon pairs is consistent with quantum entanglement, between the limits for maximally entangled and classically separable states [2]. Selecting events by positron lifetime reveals distinct polarization correlation distributions for different annihilation channels. A detailed comparison will be presented at the conference. These results motivate quantum-informed PET imaging, with tissue hypoxia as a clinical biomarker.

Abstract

This research presents the development and first in-vivo demonstration of Positronium Ratio Imaging, a novel functional imaging technique that uses the three-photon to two-photon decay ratio of positronium. While conventional Positron Emission Tomography primarily maps radiopharmaceutical distribution, Positronium Ratio Imaging provides insights into the tissue microenvironment by exploiting the sensitivity of ortho-positronium to local oxygen levels and morphology. We characterized the system's geometric stability using a four-rod phantom with varying dimensions and evaluated the results against Monte Carlo simulations using the Geant4 toolkit. The phantom study identified a size-dependent uncertainty baseline of approximately twenty percent, primarily arising from detector sensitivity and geometric factors. Furthermore, we achieved the first in-vivo ratio imaging using a tumor-bearing mouse model administered with fluorine-18 fluorodeoxyglucose. Quantitative analysis revealed that the three-photon to two-photon ratio in brown adipose tissue was approximately two and a half times higher than in the heart and bladder. This significant contrast is consistent with theoretical trends derived from tissue-specific positronium lifetimes and far exceeds the established hardware-induced uncertainty. These findings validate the feasibility of using standard positron emitters for functional three-photon imaging without the need for specialized prompt-gamma emitters. Future work will focus on refining quantitative calculation methods, evaluating signal-to-noise ratios, and implementing hardware-specific calibrations to enhance absolute quantification. This technology lays a foundation for clinical applications such as the non-invasive visualization of tumor hypoxia and metabolic characterization of the tissue microenvironment, offering a breakthrough in functional molecular imaging.

Abstract

We present quantitative 4D PET imaging of photosynthate carbon transport and rhizosphere exudation in intact Phaseolus vulgaris plant–soil systems using the R-PET (Rhizo-PET) scanner with the ¹¹C radiotracer (t₁/₂ = 20.3 min). Across N = 6 plants, time–activity curves (TACs) extracted from 225 cross-pattern ROIs per dataset—spanning 3 roots × 3 axial depths × ±3 mm radial extent—reveal a reproducible source-to-sink carbon allocation hierarchy: upper root zones receive ≈50% of total plant carbon, while root 2 (primary taproot) dominates at 20.8 ± 6.3%. Time-to-peak (TTP) increases from 31.5±4.4 min (upper) to 48.0±13.1 min (lower root 2), corresponding to a phloem transport velocity of ≈2.4 mm min⁻¹. Rhizosphere carbon exudation follows an exponential decay AUC(r) = A e⁻ᵏʳ with k = 0.10–0.20 mm⁻¹, placing the active deposition zone within 4–14 mm of the root surface—the first spatially resolved 3D+t measurement of this process in intact soil.

Abstract

Walk-through positron emission tomography (WT-PET) is an emerging upright imaging geometry based on two vertically opposing flat detector panels. Although this design may improve patient comfort and acquisition efficiency, respiratory motion remains a major source of image degradation. This study evaluates respiratory motion correction strategies for WT-PET using XCAT anthropomorphic simulations. A 30s acquisition was simulated in a WT-PET geometry and reconstructed using an in-house MLEM-based algorithm. Data-driven amplitude- and phase-based respiratory gating were followed by non-rigid motion correction using Advanced Normalization Tools (ANTs) and Fast Algorithms for Motion Correction (FALCON). Overlapping-frame reconstruction using 0.9375s windows shifted every 0.3125s was also evaluated to assess the effect of temporal smoothing. Performance was quantified against a motion-free reference using normalized root mean square error (NRMSE), diaphragm edge width, and line profile analysis. ANTs phase-based 4-gate correction achieved the best performance, reducing NRMSE from 64% to 6% and restoring the diaphragm edge width to the motion-free reference level (25.10 mm vs. 25.79 mm). FALCON phase-based 4-gate correction reduced NRMSE to 12% but showed limited recovery of diaphragm sharpness, with an edge width of 28.40 mm compared with 28.62 mm for no motion correction. Overlapping-frame ANTs correction reduced NRMSE to 15% and yielded an intermediate edge width of 27.13 mm, although line profiles indicated residual local displacement and smoothing. These findings show that data-driven respiratory gating combined with deformable registration can substantially improve image quality in upright WT-PET.

Abstract

We investigated whether a positron emission
tomography-derived latent biomarker can serve as a compact
prognostic signal after longitudinal modeling. The study used
3,458 scans from 1,624 participants in the Alzheimer’s Disease
Neuroimaging Initiative. A Bayesian mixed-effects model was
fitted to the biomarker trajectory and evaluated in two practical
settings: forecasting future visits for previously observed
subjects and estimating short-term dementia risk from predicted
biomarker values. For subjects already observed during training,
future visits were predicted with a mean root mean squared error
of 0.298 and a mean absolute error of 0.250. For entirely new
subjects, a change-from-baseline formulation reduced prediction
error to a mean root mean squared error of 0.217 and a mean
absolute error of 0.171 after an initial scan. A training-only
logistic calibration applied to held-out predictions reached an
area under the receiver operating characteristic curve of 0.826
for future dementia risk estimation. These results suggest that
the latent biomarker can move beyond representation learning
and support short-term longitudinal forecasting and clinically
interpretable risk stratification.

Abstract

Patient motion remains a common challenge in dynamic cardiac positron emission tomography (PET), as it degrades image quality and complicates reliable evaluation of motion correction methods. Realistic motion simulation requires motion models that capture the variability observed in clinical data, but this remains difficult when simulations rely on only a limited set of motion patterns. In this work, we propose a data driven framework for motion model selection and simulation in dynamic cardiac PET. We characterize motion trajectories using physically interpretable features and analyze them using clustering and trajectory similarity based on dynamic time warping to identify a compact set of representative motion models. These models are integrated with patient-specific motion scaling for frame-wise simulation of motion-corrupted PET data. The proposed framework produces stable normalized error levels across 1308 patient–model evaluations while using only a small number of representative motion models. The results suggest that the selected models effectively capture dominant motion behaviors observed in the dataset and enable realistic PET motion simulation.

Abstract

Deep learning-based attenuation correction (DL-AC) offers potential advantages over conventional CT-based attenuation correction (CT-AC) in PET imaging, including avoidance of additional radiation exposure and reduced susceptibility to PET–CT misalignment. However, in DL-AC application scenarios, when CT-derived attenuation maps (CT-μ) are unavailable as reference images, quality control (QC) of deep learning-derived attenuation maps (DL-μ) often relies on manual inspection. To address this limitation, we propose a patch-level 3D convolutional neural network (3D CNN) framework for automated anomaly detection and localization in DL-μ without requiring CT during inference. During training, CT-μ was used to assist patch-level annotation of DL-μ, and artifact-free CT-μ patches without observer-identified abnormalities were incorporated as normal samples to help the model learn normal attenuation patterns. During inference, only DL-μ was used, and sliding-window prediction was used to aggregate patch-level anomaly probabilities into a voxel-wise anomaly probability map. On the validation set (51 cases), the framework yielded patch-level performance with an AUC of 0.9984, an F1-score of 0.9802, a sensitivity of 97.06%, and a specificity of 98.86%. These results suggest that the proposed framework can provide spatial cues for DL-μ, potentially supporting efficient evaluation and reducing reliance on manual QC inspection.

Abstract

Quantum-inspired denoising is employed for clinical PET/CT, PET/MR, and SPECT imaging by treating images analogous to disordered materials and analyzing the eigenfunctions of an associated Hamiltonian. Quantum localization is quantified by the participation ratio, enabling filtering of non‑localized, high‑participation modes that dominate the mid‑frequency range and are associated with diffusive noise. By suppressing these modes and reconstructing from the remaining localized modes, noise is reduced without training data or manual regularization. The framework is applied to 46 multi‑vendor, multi‑tracer patient datasets from Massachusetts General Brigham to characterize scanner‑dependent noise structure and its impact on image appearance. Across the cohort, quantum‑inspired filtering yields consistent decreases in blind image quality scores (PIQE and BRISQUE) together with increases in entropy and Laplacian variance, indicating lower perceived noise and enhanced structural detail in the denoised clinical images.