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Annihilation photons are quantum-entangled in polarization, a phenomenon that has not been exploited in medical diagnostics so far. We present the first in vivo imaging of the degree of quantum entanglement of photons originating from positron-electron annihilation within a human subject. This study utilized the Jagiellonian Positron Emission Tomography (J-PET) scanner, constructed from plastic scintillators. In plastics, annihilation photons interact primarily via the Compton effect, which provides simultaneous information regarding the photon interaction position and time, as well as the photon polarization plane. The patient was injected with a DOTA-TATE radiopharmaceutical labeled with the $^{68}$Ga radionuclide. Using the J-PET scanner, we determined the image of the radiopharmaceutical uptake and, simultaneously, the image of the degree of quantum entanglement. The latter was determined from the relative angle between the polarization planes of the annihilation photons. The values of the degree of quantum entanglement extracted for the liver and the spleen are smaller than those predicted for maximally entangled two-photon states, yet larger than expected for separable photon

In this work we explored the use of patient specific reinforced learning to generate 3D activity maps from two 2D planar images (anterior and posterior). The solution of this problem remains unachievable using conventional methodologies and is of particular interest for dosimetry in nuclear medicine where approaches for post-therapy distribution of radiopharmaceuticals such as 177Lu-PSMA are typically done via either expensive and long 3D SPECT acquisitions or fast, yet only 2D, planar scintigraphy. Being able to generate 3D activity maps from planar scintigraphy opens the gate for new dosimetry applications removing the need for SPECT and facilitating multi-time point dosimetry studies. Our solution comprises the generation of a patient specific dataset with possible 3D uptake maps of the radiopharmaceuticals withing the anatomy of the individual followed by an AI approach (we explored both the use of 3DUnet and diffusion models) able to generate 3D activity maps from 2D planar images. We have validated our method both in simulation and real planar acquisitions. We observed enhanced results using patient specific reinforcement learning (~20% reduction on MAE and ~5% increase in SS

Radiopharmaceutical therapies are expanding rapidly, but clinical evidence generation is limited by operational constraints and biological heterogeneity in radiopharmaceutical delivery and radiation risk. Virtual theranostic trials can run as companion trials, linking quantitative imaging to patient-specific models to generate evidence for personalized injections and scheduling, accelerate development, and broaden access.

Quantitative molecular imaging is central to treatment response assessment in oncology, yet clinical practice remains largely dominated by patient-level or limited target-lesion criteria that ignore inter-lesion heterogeneity. This limitation is particularly important in prostate cancer, where PSMA PET/CT can reveal extensive skeletal and nodal metastatic disease that often evolves heterogeneously under therapy. Accurate and scalable lesion segmentation and tracking across serial PSMA PET/CT and post-therapy SPECT/CT scans is therefore essential for implementing emerging PSMA-specific response frameworks, such as RECIP 1.0, and for enabling lesion-level dosimetry in 177Lu-PSMA radiopharmaceutical therapies (RPTs). This article examines clinical motivations, technical foundations, and future pathways for automated lesion tracking in prostate cancer imaging. We focus on the unique requirements introduced by PSMA PET/CT compared with FDG PET/CT and highlight the critical role of quantitative SPECT/CT in linking imaging-derived disease characterization with delivered therapeutic dose. Recent advances in AI-based segmentation and automated lesion matching now make scalable longitudinal

In this work, we have investigated the sensitivity of the effectiveness (TCP) of molecular radiotherapy (MRT) treatment to uncertainties of the dose rate curves that may appear when reconstructing those curves. We generated different dose rate curves from experimental data, imposing the constraint of equal dose for each of them. Then, we computed TCPs and looked for correlations between metrics measuring the differences between the dose rate curves and differences in TCP. Finally, according to these results, we estimated the range of tolerable uncertainties in the dose rate curves. The study was performed for different radiopharmaceuticals and different radiosensitive parameters that can affect the dose rate response ($α/β$, sub-lethal repair rate). The best correlation between differences in the dose rate curves and TCP was found for a metric that computes averaged linear differences between the curves. With this metric, we quantified differences in dose rate curves that would lead to differences in TCP of 0.02, a parameter denoted $m_{1,\: 0.02}$ that is a surrogate of the dependence of the TCP on the dose rate profile. The results showed that the sensitivity of the TCP to dose r

This review discusses the current applications, advantages, and limitations of PBPK and PopPK models in radiopharmaceutical therapy (RPT). PBPK models simulate radiopharmaceutical kinetics by integrating prior physiological and drug parameter information, whereas PopPK models leverage population data to enhance individual dose estimation accuracy. Future directions include developing hybrid models, incorporating artificial intelligence, and establishing regulatory guidelines to promote their clinical adoption. Ultimately, these modeling strategies aim to enable precise, personalized RPT dosing, thereby improving therapeutic outcomes and safety.

Cancer remains a leading cause of death worldwide, necessitating personalized treatment approaches to improve outcomes. Theranostics, combining molecular-level imaging with targeted therapy, offers potential for precision oncology but requires optimized, patient-specific care plans. This paper investigates state-of-the-art data-driven decision support applications with a reinforcement learning focus in precision oncology. We review current applications, training environments, state-space representation, performance evaluation criteria, and measurement of risk and reward, highlighting key challenges. We propose a framework integrating data-driven modeling with reinforcement learning-based decision support to optimize radiopharmaceutical therapy dosing, addressing identified challenges and setting directions for future research. The framework leverages Neural Ordinary Differential Equations and Physics-Informed Neural Networks to enhance Physiologically Based Pharmacokinetic models while applying reinforcement learning algorithms to iteratively refine treatment policies based on patient-specific data.

The field of Clinical-Computational Nuclear Medicine is rapidly advancing, fueled by AI, tracer kinetic modeling, radiomics, and integrated informatics. These technologies improve imaging quality, automate lesion detection, and enable personalized radiopharmaceutical therapy through physiologically based pharmacokinetic (PBPK) modeling and voxel-level dosimetry. Workflow automation and Natural Language Processing (NLP) further enhance operational efficiency. However, successful implementation and adoption of these tools require clinical oversight to ensure accuracy, interpretability, and patient safety. This paper highlights key computational innovations and emphasizes the critical role of clinician-guided evaluation in shaping the future of precision imaging and therapy.

Targeted Radionuclide Therapy (TRT) is a modern strategy in radiation oncology that aims to administer a potent radiation dose specifically to cancer cells using cancer-targeting radiopharmaceuticals. Accurate radiation dose estimation tailored to individual patients is crucial. Deep learning, particularly with pre-therapy imaging, holds promise for personalizing TRT doses. However, current methods require large time series of SPECT imaging, which is hardly achievable in routine clinical practice, and thus raises issues of data availability. Our objective is to develop a semi-supervised learning (SSL) solution to personalize dosimetry using pre-therapy images. The aim is to develop an approach that achieves accurate results when PET/CT images are available, but are associated with only a few post-therapy dosimetry data provided by SPECT images. In this work, we introduce an SSL method using a pseudo-label generation approach for regression tasks inspired by the FixMatch framework. The feasibility of the proposed solution was preliminarily evaluated through an in-silico study using synthetic data and Monte Carlo simulation. Experimental results for organ dose prediction yielded prom

Targeted alpha-particle therapy (TAT) employs alpha-emitting radionuclides conjugated to tumor-targeting molecules to deliver localized radiation to cancer cells, showing great promise in treating metastatic cancers. Among these radionuclides, Actinium-225 ($^{225}$Ac, t$_{1/2}$ = 9.9 days) has emerged as a clinically promising candidate. Its decay chain generates four successive alpha emissions, resulting in highly localized and effective cytotoxic damage to cancer cells when delivered to tumor sites. However, the assumption of complete retention of $^{225}$Ac and its radioactive daughters at these target sites is often inaccurate. The nuclear recoil effect can lead to off-target distribution and unintended toxicity. Our results revealed distinct spectral differences between radiolabeled cells and reference samples, demonstrating [$^{225}$Ac]Ac-crown-TATE uptake by AR42J cells. Detection of $^{213}$Po, one of the $^{225}$Ac decay daughters, highlighted partial retention and release of decay products from cells, providing information on intracellular retention and daughter redistribution. Geant4 simulations confirmed the alignment of experimental data with theoretical models, valid

The Brazilian Multipurpose Reactor (RMB) was conceived to meet national needs for radioisotope production, materials irradiation testing, and neutron beam applications. In addition to its 30~MW pool-type reactor, the RMB complex will include additional facilities for radioisotope production and related applications. $^{161}Tb$ is a promising radionuclide for radiopharmaceutical therapy, offering decay properties similar to $^{177}Lu$ but with additional conversion and Auger electrons that enhance dose delivery to cancer cells. In light of this emerging radioisotope, this study explores the potential production of $^{161}Tb$ in the RMB through neutron irradiation of enriched $Gd_{2}O_{3}$ targets. Monte Carlo (MCNP) simulations provided detailed neutron flux distributions, which were used as input for ORIGEN calculations of isotope buildup. Assuming 10 mg targets enriched to 97.5% in $^{160}Gd$, a 40-day irradiation, and a thermal flux of $2 \cdot 10^{14}$ $n/cm^{2}s$, the results indicate that $^{161}Tb$ activity reaches approximately 4.5 GBq after 14 days ($\approx 450 GBq/g$), in agreement with data from other research reactors. Building on prior studies that demonstrated the RMB

Based on collisions between the 100 PW laser and 8 GeV superconducting linear accelerator constructing at the Shanghai hard X-ray free electron laser system (SHINE), the building of GeV-level $γ$-ray as well as positron beams are proposed according to particle-in-cell simulations. Key processes are considered involving the nonlinear inverse Compton scattering for $γ$-ray generation and the multiphoton Breit-Wheeler process for electron-positron pair production. Regardless of laser polarization, the simulations indicate that $γ$-ray beams achieve energy up to 8 GeV, brilliance around 10$^{27}$ photons/(s mm$^{2}$ mrad$^{2}$), and emittance as low as 0.1 mm mrad, while positron beams reach energy up to 7 GeV, brilliance around 4 $\times$ 10$^{24}$ positrons/(s mm$^{2}$ mrad$^{2}$), and emittance as low as 0.1 mm mrad. Various applications could benefit from the possible high-energy $γ$-ray and positron beams built at the SHINE facility, including fundamental physics of strong-field quantum electrodynamics theory validation, nuclear physics, radiopharmaceutical preparation, and imaging, etc.

Physiologically based pharmacokinetic (PBPK) models provide a mechanistic framework for simulating radiopharmaceutical kinetics and estimating patient-specific absorbed doses (ADs). PBPK models incorporate prior knowledge of patient physiology and drug-specific properties, which can enhance the models predictive performance. PBPK models can ultimately be used to predict treatment response and thereby enable theranostic digital twins (TDTs) for personalized treatment planning in radiopharmaceutical therapies (RPTs). To achieve this potential of precision RPT, however, the reliability of the underlying modeling, including the PBPK-based dosimetry, must be established through rigorous verification, validation, and uncertainty quantification (VVUQ). This review outlines the role of VVUQ in ensuring the credibility and clinical applicability of PBPK models in radiotheranostics. Key methodologies for PBPK model VVUQ are discussed, including goodness-of-fit (GOF) assessment, prediction evaluation, and uncertainty propagation.

PET imaging is widely employed for observing biological metabolic activities within the human body. However, numerous benign conditions can cause increased uptake of radiopharmaceuticals, confounding differentiation from malignant tumors. Several studies have indicated that dual-time PET imaging holds promise in distinguishing between malignant and benign tumor processes. Nevertheless, the hour-long distribution period of radiopharmaceuticals post-injection complicates the determination of optimal timing for the second scan, presenting challenges in both practical applications and research. Notably, we have identified that delay time PET imaging can be framed as an image-to-image conversion problem. Motivated by this insight, we propose a novel spatial-temporal guided diffusion transformer probabilistic model (st-DTPM) to solve dual-time PET imaging prediction problem. Specifically, this architecture leverages the U-net framework that integrates patch-wise features of CNN and pixel-wise relevance of Transformer to obtain local and global information. And then employs a conditional DDPM model for image synthesis. Furthermore, on spatial condition, we concatenate early scan PET image

Long-axial field-of-view (LAFOV) PET/CT has the potential to redefine the role of molecular imaging in theranostics by making multiparametric whole-body (MPWB) imaging and predictive dosimetry more clinically feasible. Compared to conventional PET systems, LAFOV scanners provide dramatic gains in sensitivity and coverage, allowing dynamic acquisitions, delayed imaging, and dual-tracer protocols within clinically feasible workflows. These advances supply the quantitative data required for physiologically based pharmacokinetic (PBPK) modeling and the creation of theranostic digital twins, supporting true personalization of radiopharmaceutical therapy.

Dynamic Positron Emission Tomography (dPET) imaging and Time-Activity Curve (TAC) analyses are essential for understanding and quantifying the biodistribution of radiopharmaceuticals over time and space. Traditional compartmental modeling, while foundational, commonly struggles to fully capture the complexities of biological systems, including non-linear dynamics and variability. This study introduces an innovative data-driven neural network-based framework, inspired by Reaction Diffusion systems, designed to address these limitations. Our approach, which adaptively fits TACs from dPET, enables the direct calibration of diffusion coefficients and reaction terms from observed data, offering significant improvements in predictive accuracy and robustness over traditional methods, especially in complex biological scenarios. By more accurately modeling the spatio-temporal dynamics of radiopharmaceuticals, our method advances modeling of pharmacokinetic and pharmacodynamic processes, enabling new possibilities in quantitative nuclear medicine.

Targeted Radionuclide Therapy (TRT) is a well-established technique for cancer treatment. In this approach, radionuclides are bound to specific drugs that selectively transport them to the tumor site. Within the ISOLPHARM project, a radiopharmaceutical for TRT based on the innovative radionuclide Ag-111 is currently under development. Ag-111 has a half-life of 7.45 days and decays by emitting both electrons and gamma-rays. The emission of gamma-rays, predominantly at an energy of 342 keV, enables the visualization of Ag-111 using a gamma camera. In this work, we describe a Monte Carlo simulation developed to optimize the design parameters of such an imaging device. The simulation is based on the Geant4 toolkit, which accurately models the interactions between particles and matter. The estimated spatial resolution and sensitivity of the system are approximately 4 mm and 19 cps/MBq, respectively. The simulated device is able to resolve lesions with a lesion-to-background activity ratio of 4:1 under in-vivo-like conditions. These results indicate that the proposed gamma camera can provide cost-effective imaging capabilities for preclinical radiopharmaceutical studies.

This study compares two statistical approaches to image reconstruction in single-photon emission computed tomography (SPECT). We evaluated the widely used Ordered Subset Expectation Maximization (OSEM) algorithm and the newer Maximum a Posteriori approach with Entropy prior (MAP-Ent) approach in the context of quantifying radiopharmaceutical uptake in pathological lesions. Numerical experiments were performed using a digital twin of the standardized NEMA IEC phantom, which contains six spheres of varying diameters to simulate lesions. Quantitative accuracy was assessed using the maximum recovery coefficient (RCmax), defined as the ratio of the reconstructed maximum activity to the true value. The study shows that OSEM exhibits unstable convergence during iterations, leading to noise and edge artifacts in lesion images. Post-filtering stabilizes the reconstruction and ensures convergence, producing RCmax-size curves that could be used as correction factors in clinical evaluations. However, this approach significantly underestimates uptake in small lesions and may even lead to the complete loss of small lesions on reconstructed images. In contrast, MAP-Ent demonstrates fundamentally

This article reviews the evolving field of radiobiology, emphasizing the need for advanced multiscale, mechanistic models to optimize radiopharmaceutical therapies (RPT). While the traditional linear-quadratic (LQ) model underpins external beam radiation therapy (EBRT), RPT's unique biological and spatial complexities demand new approaches. First-principles simulations of DNA damage, repair, and multicellular responses are crucial for understanding therapeutic efficacy and toxicity. The integration of these models into personalized, digital twin frameworks promises transformative clinical applications, but progress depends on deep mechanistic insights, experimental validation, and balancing model complexity with practicality for clinical use.