CyberSec Research

Very high energy electrons (VHEE) in the 50-250 MeV range, delivered in short pulses at ultra-high dose rates, are proposed for clinical FLASH radiotherapy (RT) targeting deep-seated tumors. The clinical implementation of VHEE-FLASH RT requires online verification to optimize dose delivery. In this study we propose a novel online dose verification technique based on the detection of bremsstrahlung photons during VHEE interactions with matter. A polymethyl methacrylate (PMMA) phantom was simulated to evaluate the dose deposited by a VHEE beam and to optimise the system design to detect the bremsstralung radiation. Experimental validation was performed at the Beam Test Facility at Laboratori Nazionali di Frascati (Isituto Nazionale di Fisica Nucleare-INFN). A deep learning pipeline was developed to reconstruct the dose distribution in the phantom based on the bremsstrahlung radiation profile. Experimental results demonstrated successful detection of bremsstrahlung radiation emitted orthogonally to the beam axis. The deep learning model achieved accurate dose reconstruction based on the bremsstrahlung radiation profile with a discrepancy of less than 2 percent compared to the simulated dose distribution. This study confirms that bremsstrahlung detection provides a viable online verification for VHEE RT.

This study investigates the feasibility of tilt-series neutron tomography for analyzing rhizoboxes used in root-soil interaction studies. Traditional neutron imaging methods are limited by constrained root growth volumes and poor penetration in moist soil. Using a vertical acquisition axis, the tilt-series approach avoids constraints typical of laminography. This method allows simultaneous radiographic and tomographic data acquisition, enhancing time resolution and providing detailed insights into root networks and soil water content. Experiments involved scanning six-week-old maize plants in rhizoboxes filled with sand. Results show that tilt-series tomography can effectively reconstruct root networks despite some artifacts from the missing wedge. While the tilt-series tomographic data qualitatively reveal water distribution changes, radiographic data remain essential for quantitative analysis. This approach demonstrates the potential for dynamic root-soil interaction studies, offering a valuable tool for agricultural and environmental research by providing comprehensive insights into the rhizosphere.

Magnetic resonance imaging (MRI) is the gold standard imaging modality for numerous diagnostic tasks, yet its usefulness is tempered due to its high cost and infrastructural requirements. Low-cost very-low-field portable scanners offer new opportunities, while enabling imaging outside conventional MRI suites. However, achieving diagnostic-quality images in clinically acceptable scan times remains challenging with these systems. Therefore methods for improving the image quality while reducing the scan duration are highly desirable. Here, we investigate a physics-informed 3D deep unrolled network for the reconstruction of portable MR acquisitions. Our approach includes a novel network architecture that utilizes momentum-based acceleration and leverages complex conjugate symmetry of k-space for improved reconstruction performance. Comprehensive evaluations on emulated datasets as well as 47mT portable MRI acquisitions demonstrate the improved reconstruction quality of the proposed method compared to existing methods.

Very-low-field MRIs are becoming increasingly popular due to their portability and adaptability to different environments. They are being successfully used for various clinical applications, leading to a paradigm shift in the way imaging care is typically performed. The development of low-cost MRI scanner prototypes began a few years ago, with some interesting and promising open-source projects emerging in both hardware and software design. Using permanent magnets (PMs) to generate the static magnetic field B0 can substantially reduce the manufacturing cost of low-field scanners while achieving satisfactory homogeneity. This article focuses on characterizing magnet performance in terms of B0 spatial homogeneity. Specifically, it investigates its sensitivity to various factors and explores the reasons for discrepancies between numerical expectations and actual measurements on fabricated magnets. The analysis also examines the consequences of using different numerical model approximations, revisiting concepts most frequently used in other design contexts. While these assumptions simplify the numerical model and may improve its performance in terms of computational time, this paper demonstrates that they also impact the reliability of the obtained results.

Accurate visualization of interventional devices, such as medical needles, is essential for the safe and effective guidance of minimally invasive procedures. Ultrasound (US) imaging is widely used for needle guidance, but the two-dimensional nature of most clinical probes limits accurate three-dimensional (3D) localization, particularly of the needle tip. We present a novel system that integrates volumetric US imaging with 3D needle tracking by combining a fiber-optic hydrophone embedded in the needle and a sparse spiral US array. Real-time volumetric imaging was achieved using plane-wave techniques, while precise needle tip tracking was enabled through communication between the probe and hydrophone. The feasibility of the approach was demonstrated using a nerve block training phantom. This proof-of-concept system enables simultaneous volumetric anatomical imaging and 3D needle tip tracking, with strong potential to enhance the efficacy and safety of image-guided interventional procedures.

Efficient particle sorting in microfluidic systems is vital for advancements in biomedical diagnostics and industrial applications. This study numerically investigates particle migration and passive sorting in symmetric serpentine microchannels, leveraging inertial and centrifugal forces for label-free, high-throughput separation. Using a two-dimensional numerical model, particle dynamics were analyzed across varying flow rates, diameter ratios (1.2, 1.5, and 2), and channel configurations. The optimized serpentine geometry achieved particle separation efficiencies exceeding 95% and throughput greater than 99%.A novel scaling framework was developed to predict the minimum number of channel loops required for efficient sorting. Additionally, the robustness of the proposed scaling framework is demonstrated by its consistency with findings from previous studies, which exhibit the same trend as predicted by the scaling laws, underscoring the universality and reliability of the model. Additionally, the study revealed the significant influence of density ratio ({\alpha}) on sorting efficiency, where higher {\alpha} values enhanced separation through amplified hydrodynamic forces. Optimal flow rates tailored to particle sizes were identified, enabling the formation of focused particle streaks for precise sorting. However, efficiency declined beyond these thresholds due to particle entrapment in micro-vortices or boundary layers. This work provides valuable insights and design principles for developing compact, cost-effective microfluidic systems, with broad applications in biomedical fields like cell sorting and pathogen detection, as well as industrial processes requiring precise particle handlin

We consider an inverse problem governed by the Westervelt equation with linear diffusivity and quadratic-type nonlinearity. The objective of this problem is to recover all the coefficients of this nonlinear partial differential equation. We show that, by constructing complex-valued time-periodic solutions excited from the boundary time-harmonically at a sufficiently high frequency, knowledge of the first- and second-harmonic Cauchy data at the boundary is sufficient to simultaneously determine the wave speed, diffusivity and nonlinearity in the interior of the domain of interest.

Cryosurgery employs a safe and relatively simple technique of exposure and is an advantageous and highly rated method. For its effective application, it is necessary to control both the volume of the expanding freezing zone and volumetric thermal field dynamics. The aim of this study was to perform a thermal imaging study of freezing and thawing in a model system (gel phantom) to predict the dynamics of the freezing zone during cryodestruction of biological tissues in vivo. Here, the thermal imager is an effective tool for demonstrating the surface temperature distribution. We have studied how the observed infrared image relates to the distribution and change of the thermal field in depth. For this purpose, we created test measuring equipment for simultaneous analysis of the dynamics of thermal fields on the surface, video recording of freezing and thawing on the surface as well as in the depth of the gel phantom, measuring the temperature at any given point in the depth and modeling in the zone of low-temperature exposure of vessels with different blood flow parameters. It was revealed that with a modeled vessel in the low-temperature exposure zone, the surface thermal fields deformed and they gained the shape of butterfly wings. Our experimental study in a gel phantom is supported by numerical calculations, demonstrating how the freezing zone and thermal isotherms on the surface and in depth evolve under real conditions, thereby providing a basis for assessing the cryoeffect time and intensity in practice. Key words: cryoapplication; freezing; thawing; temperature field dynamics; infrared thermography; gel phantom; testing measuring equipment; vessel simulation.

Cerebral autoregulation (CA) is a fundamental mechanism that modulates cerebrovascular resistance, primarily by regulating the diameter of small cerebral vessels to maintain stable cerebral blood flow (CBF) in response to fluctuations in systemic arterial pressure. However, the clinical understanding of CA remains limited due to the intricate structure of the cerebral vasculature and the challenges in accurately quantifying the hemodynamic and physiological parameters that govern this autoregulatory process. Method: In this study, we introduced a novel numerical algorithm that employs three partial differential equations and one ordinary differential equation to capture both the spatial and temporal distributions of key CA-driving factors, including the arterial pressure (P) and the partial pressures of oxygen (PO_2) and carbon dioxide (PCO_2) within the cerebral vasculature, together with a Windkessel model in turn to regulate the CBF based on the calculated P, PO_2, and PCO_2. This algorithm was sequentially integrated with our previously developed personalized 0D-1D multi-dimensional model to account for the patient-specific effects. Results: The integrated framework was rigorously validated using two independent datasets, demonstrating its high reliability and accuracy in capturing the regulatory effects of CA on CBF across a range of physiological conditions. Conclusion: This work significantly advances our understanding of CA and provides a promising foundation for developing hemodynamic-based therapeutic strategies aimed at improving clinical outcomes in patients with cerebrovascular disorders.

Medicine is evolving beyond therapy largely predicated on anatomical information and towards incorporating patient-specific molecular biomarkers of disease for more accurate diagnosis and effective treatment. The complementary combination of hyperpolarization by spin-lock induced crossing signal amplification by reversible exchange (SLIC SABRE) and low field magnetic resonance imaging (MRI) can enable accessible metabolic imaging to advance personalized medicine. Hyperpolarized 13C-enriched pyruvate has demonstrated utility for MRI of metabolism in cancer, heart disease and neurodegenerative disorders but has been restricted from widespread clinical adoption by a lack of access to affordable technology. Parahydrogen-based polarization techniques, paired with low-cost high-performance MRI at millitesla fields, offer a means of broadening the reach of metabolic imaging. Here we show results demonstrating in situ hyperpolarization of pyruvate at 6.5 mT by SLIC SABRE, followed by immediate readout without field cycling or sample shuttling. We achieve 13C signal enhancements several million times above thermal equilibrium at 6.5 mT, corresponding to polarization levels of approximately 3%. Leveraging this enhancement, we perform 13C MRI and acquire NMR spectra with resolution sufficient to distinguish chemical shifts between pyruvate isotopomers. These results show a viable pathway towards accessible metabolic imaging with hyperpolarized 13C MRI at ultra-low field.

Diffusion/score-based models have recently emerged as powerful generative priors for solving inverse problems, including accelerated MRI reconstruction. While their flexibility allows decoupling the measurement model from the learned prior, their performance heavily depends on carefully tuned data fidelity weights, especially under fast sampling schedules with few denoising steps. Existing approaches often rely on heuristics or fixed weights, which fail to generalize across varying measurement conditions and irregular timestep schedules. In this work, we propose Zero-shot Adaptive Diffusion Sampling (ZADS), a test-time optimization method that adaptively tunes fidelity weights across arbitrary noise schedules without requiring retraining of the diffusion prior. ZADS treats the denoising process as a fixed unrolled sampler and optimizes fidelity weights in a self-supervised manner using only undersampled measurements. Experiments on the fastMRI knee dataset demonstrate that ZADS consistently outperforms both traditional compressed sensing and recent diffusion-based methods, showcasing its ability to deliver high-fidelity reconstructions across varying noise schedules and acquisition settings.

Purpose: To study the relationship between soccer heading and the risk of mild traumatic brain injury (mTBI), we previously developed an instrumented headband and data processing scheme to measure the angular head kinematics of soccer headers. Laboratory evaluation of the headband on an anthropomorphic test device showed good agreement with a reference sensor for soccer ball impacts to the front of the head. In this study, we evaluate the headband in measuring the full head kinematics of soccer headers in the field. Methods: The headband was evaluated under typical soccer heading scenarios (throw-ins, goal-kicks, and corner-kicks) on a human subject. The measured time history and peak kinematics from the headband were compared with those from an instrumented mouthpiece, which is a widely accepted method for measuring head kinematics in the field. Results: The time history agreement (CORA scores) between the headband and the mouthpiece ranged from 'fair' to 'excellent', with the highest agreement for angular velocities (0.79 \pm 0.08) and translational accelerations (0.73 \pm 0.05) and lowest for angular accelerations (0.67 \pm 0.06). A Bland-Altman analysis of the peak kinematics from the headband and mouthpiece found the mean bias to be 40.9% (of the maximum mouthpiece reading) for the angular velocity, 16.6% for the translational acceleration, and-14.1% for the angular acceleration. Conclusion: The field evaluation of the instrumented headband showed reasonable agreement with the mouthpiece for some kinematic measures and impact conditions. Future work should focus on improving the headband performance across all kinematic measures.

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.

Objective. Proton beams enable localized dose delivery. Accurate range estimation is essential, but planning still relies on X-ray CT, which introduces uncertainty in stopping power and range. Proton CT measures water equivalent thickness directly but suffers resolution loss from multiple Coulomb scattering. We develop a data driven method that reconstructs water equivalent path length (WEPL) maps from energy resolved proton radiographs, bypassing intermediate reconstructions. Approach. We present a machine learning pipeline for WEPL from high dimensional radiographs. Data were generated with the TOPAS Monte Carlo toolkit, modeling a clinical nozzle and a patient CT. Proton energies spanned 70-230 MeV across 72 projection angles. Principal component analysis reduced input dimensionality while preserving signal. A conditional GAN with gradient penalty was trained for WEPL prediction using a composite loss (adversarial, MSE, SSIM, perceptual) to balance sharpness, accuracy, and stability. Main results. The model reached a mean relative WEPL deviation of 2.5 percent, an SSIM of 0.97, and a proton radiography gamma index passing rate of 97.1 percent (2 percent delta WEPL, 3 mm distance-to-agreement) on a simulated head phantom. Results indicate high spatial fidelity and strong structural agreement. Significance. WEPL can be mapped directly from proton radiographs with deep learning while avoiding intermediate steps. The method mitigates limits of analytic techniques and may improve treatment planning. Future work will tune the number of PCA components, include detector response, explore low dose settings, and extend multi angle data toward full proton CT reconstruction; it is compatible with clinical workflows.

The diffusion MRI Neurite Exchange Imaging model offers a promising framework for probing gray matter microstructure by estimating parameters such as compartment sizes, diffusivities, and inter-compartmental water exchange time. However, existing protocols require long scan times. This study proposes a reduced acquisition scheme for the Connectome 2.0 scanner that preserves model accuracy while substantially shortening scan duration. We developed a data-driven framework using explainable artificial intelligence with a guided recursive feature elimination strategy to identify an optimal 8-feature subset from a 15-feature protocol. The performance of this optimized protocol was validated in vivo and benchmarked against the full acquisition and alternative reduction strategies. Parameter accuracy, preservation of anatomical contrast, and test-retest reproducibility were assessed. The reduced protocol yielded parameter estimates and cortical maps comparable to the full protocol, with low estimation errors in synthetic data and minimal impact on test-retest variability. Compared to theory-driven and heuristic reduction schemes, the optimized protocol demonstrated superior robustness, reducing the deviation in water exchange time estimates by over two-fold. In conclusion, this hybrid optimization framework enables viable imaging of neurite exchange in 14 minutes without loss of parameter fidelity. This approach supports the broader application of exchange-sensitive diffusion magnetic resonance imaging in neuroscience and clinical research, and offers a generalizable method for designing efficient acquisition protocols in biophysical parameter mapping.

Background and Objective: Hemodynamic analysis of blood flow through arteries and veins is critical for diagnosing cardiovascular diseases, such as aneurysms and stenoses, and for investigating cardiovascular parameters, such as turbulence and wall shear stress. For subject-specific analyses, the anatomy and blood flow of the subject can be captured non-invasively using structural and 4D Magnetic Resonance Imaging (MRI). Computational Fluid Dynamics (CFD), on the other hand, can be used to generate blood flow simulations by solving the Navier-Stokes equations. To generate and analyze subject-specific blood flow simulations, MRI and CFD have to be brought together. Methods: We present an interactive, customizable, and user-oriented visual analysis tool that assists researchers in both medicine and numerical analysis. Our open-source tool is applicable to domains such as CFD and MRI, and it facilitates the analysis of simulation results and medical data, especially in hemodynamic studies. It enables the creation of simulation ensembles with a high variety of parameters. Furthermore, it allows for the visual and analytical examination of simulations and measurements through 2D embeddings of the similarity space. Results: To demonstrate the effectiveness of our tool, we applied it to three real-world use cases, showcasing its ability to configure simulation ensembles and analyse blood flow dynamics. We evaluated our example cases together with MRI and CFD experts to further enhance features and increase the usability. Conclusions: By combining the strengths of both CFD and MRI, our tool provides a more comprehensive understanding of hemodynamic parameters, facilitating more accurate analysis of hemodynamic biomarkers.

Particle therapy is a well-established clinical treatment of tumors and so far, more than one hundred centers are in operation around the world. High accuracy on position and dose rate in beam monitoring are major cornerstones of clinical success in particle therapy. A high voltage CMOS (HV-CMOS) monolithic active pixel sensor was developed for this beam monitoring system. The HV-CMOS technology has demonstrated many advantages over other technologies in dealing with radiation tolerance. This HV-CMOS sensor was produced with a 180 nm commercial technology on high-resistivity substrate. The HitPix sensor features several specific design details for operation in a high-intensity ion beam environment: hit-counting pixels, on-sensor projection calculation, radiation tolerant design and frame-based readout. It has a 9775 $\mu$m $\times$ 10110 $\mu$m sensor area with 200 $\mu$m $\times$ 200 $\mu$m pixel size. The new HitPix3 has a number of improvements, including a modified in-pixel amplifier, on-sensor calculation of beam profile projection in two dimensions, and in-pixel threshold tuning capacity. The functionality of these features was confirmed in laboratory tests of unirradiated HitPix3 sensors, making the HitPix3 an important step in developing a sensor for use in ion beam monitoring.

Purpose: To develop and evaluate a wearable wireless resonator glasses design that enhances eye MRI signal-to-noise ratio (SNR) without compromising whole-brain image quality at 7 T. Methods: The device integrates two detunable LC loop resonators into a lightweight, 3D-printed frame positioned near the eyes. The resonators passively couple to a standard 2Tx/32Rx head coil without hardware modifications. Bench tests assessed tuning, isolation, and detuning performance. B1$^+$ maps were measured in a head/shoulder phantom, and SNR maps were obtained in both phantom and in vivo experiments. Results: Bench measurements confirmed accurate tuning, strong inter-element isolation, and effective passive detuning. Phantom B1$^+$ mapping showed negligible differences between configurations with and without the resonators. Phantom and in vivo imaging demonstrated up to about a 3-fold SNR gain in the eye region, with no measurable SNR loss in the brain. Conclusion: The wireless resonator glasses provide a low-cost, easy-to-use solution that improves ocular SNR while preserving whole-brain image quality, enabling both dedicated eye MRI and simultaneous eye-brain imaging at ultrahigh field.