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Traditional cosmic ray simulations make use of the Montecarlo method in a very naive way to randomise energy and direction for each simulated particle. The flux of cosmic rays is modelled as a rain coming from a plane above the object of…
Detailed pulmonary airway segmentation is a clinically important task for endobronchial intervention and treatment of peripheral located lung cancer lesions. Convolutional Neural Networks (CNNs) are promising tools for medical image…
We demonstrate dark-field x-ray microtomography in a compact, laboratory-based system capable of resolving attenuation, phase, and anisotropic scattering signals with micrometer-scale resolution across centimetre-scale samples. The method…
This study evaluates dynamic conformal arc (DCA) therapy as an alternative to volumetric modulated arc therapy (VMAT) for stereotactic body radiation therapy (SBRT) of lung cancer. The rationale for DCA is lower geometric complexity and…
Investigating the structure of matter at the nanoscale non destructively is a key capability enabled by X-ray imaging. One of the most powerful nano-imaging methods is X-ray ptychography, a coherent diffraction imaging technique that has…
In this work we present a novel 3D Monte Carlo photon transport program for simulation of multiple refractive scattering based on the refractive properties of X-rays in highly scattering media, like lung tissue. Multiple scattering reduces…
High-dose-rate brachytherapy is a tumor treatment method where a highly radioactive source is brought in close proximity to the tumor. In this paper we develop a simulated annealing algorithm to optimize the dwell times at preselected dwell…
The majority of current research in deep learning based image registration addresses inter-patient brain registration with moderate deformation magnitudes. The recent Learn2Reg medical registration benchmark has demonstrated that…
Although mechanical ventilation is a lifesaving intervention in the ICU, it has harmful side-effects, such as barotrauma and volutrauma. These harms can occur due to asynchronies. Asynchronies are defined as a mismatch between the…
Accurate 3D modeling of human organs is critical for constructing digital phantoms in virtual imaging trials. However, organs such as the large intestine remain particularly challenging due to their complex geometry and shape variability.…
The problem of a spatially discontinuous diffusion coefficient ($D(\boldsymbol x)$) is one that may be encountered in hydrogeologic systems due to natural geological features or as a consequence of numerical discretization of flow…
Diffusion-based image generators are promising priors for ill-posed inverse problems like sparse-view X-ray Computed Tomography (CT). As most studies consider synthetic data, it is not clear whether training data mismatch (``domain shift'')…
Optical sensing technologies are emerging technologies used in cancer surgeries to ensure the complete removal of cancerous tissue. While point-wise assessment has many potential applications, incorporating automated large area scanning…
Deep learning-based automated diagnosis of lung cancer has emerged as a crucial advancement that enables healthcare professionals to detect and initiate treatment earlier. However, these models require extensive training datasets with…
In living organisms, the natural motion caused by the heartbeat, breathing, or muscle movements leads to the deformation of tissue caused by translation and stretching of the tissue structure. This effect results in the displacement or…
Online dose calculations before radiation treatment have applications in dose delivery verification, plan adaptation, and treatment planning. We propose a novel CBCT imaging pipeline to enhance accuracy. Our approach aims to improve HU…
For dose calculations in ion beam therapy, it is vital to accurately determine the relative stopping power (RSP) distribution within the treated volume. Currently, RSP values are extrapolated from Hounsfield units (HU), measured with x-ray…
Algorithms based on Monte-Carlo sampling have been widely adapted in robotics and other areas of engineering due to their performance robustness. However, these sampling-based approaches have high computational requirements, making them…
Monte Carlo simulations are increasingly used in scintigraphic imaging to model imaging systems and to develop and assess tomographic reconstruction algorithms and correction methods for improved image quantitation. GATE (GEANT 4…
The distance transform (DT) and its many variations are ubiquitous tools for image processing and analysis. In many imaging scenarios, the images of interest are corrupted by noise. This has a strong negative impact on the accuracy of the…