arXiv subjects

Simon Rit

Publications and source records attributed to Simon Rit.

7 recordsLinked to original sources

arXiv2026

COBRA2026: a large-scale multicenter pelvic cone-beam computed tomography projection dataset

The COBRA2026 dataset is a large-scale, multicenter resource of raw radiotherapy cone-beam computed tomography (CBCT) acquisitions created for the development and evaluation of conventional and learning-based reconstruction and image-correction methods. It contains data from 867 patients undergoing pelvic radiotherapy at six European centers, acquired using Elekta and Varian imaging systems. For each case, the dataset includes raw projection data, acquisition geometry, calibration and correction information, clinically reconstructed CBCT images, and corresponding planning CT images. Vendor-specific files were anonymized and converted into open formats. Planning CT images were deformably registered to the daily CBCT anatomy, and matched projections were simulated using the corresponding acquisition geometry. All cases underwent visual quality control, and cases with substantial processing or registration errors were excluded. The approximately 950 GB dataset is divided into training, validation, and test sets containing 692, 52, and 123 cases, respectively. Projection stacks and volumetric images are provided as compressed MetaImage files, with geometry and metadata supplied in XML and YAML formats. COBRA2026 supports research on full- and sparse-view reconstruction, low-dose imaging, artifact and scatter correction, motion compensation, and synthetic CT generation. The dataset is released under the CC BY-NC 4.0 license, indexed on Zenodo (doi:10.5281/zenodo.21322350), and accompanied by openly available preprocessing and baseline reconstruction code. It also forms the basis of the COBRA2026 reconstruction challenge.

physics.med-ph↗

arXiv2026

Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstruction

The goal of this work is to reduce the effect of photon noise in dental cone-beam CT reconstruction. We consider an inverse problem formulation and develop a databased prior. To this end, we simulate fan-beam acquisitions and add photon noise to the projection data. The prior is obtained by training a gradient-step denoiser using reconstructed simulated acquisitions. The trained model is integrated into a plug-and-play gradient-step algorithm to reconstruct images from simulated projections. Experiments on synthetic data demonstrate the denoising capabilities of the trained model, while qualitative evaluations on real images showcase the algorithm's performance and generalization ability.

cs.AI↗

arXiv2024

Simulation of diffraction and scattering using the Wigner Distribution Function

X-ray phase-contrast imaging enhances soft tissue visualization by leveraging the phase shift of X-rays passing through materials. It permits to minimize radiation exposure due to high contrast, as well as high resolution imaging limited by the wavelength of the X-rays. Phase retrieval extracts the phase shift computationally, but simulated images fail to recreate low-frequency noise observed in experimental images. To this end, we propose a new method to simulate phase contrast images using the Wigner Distribution Function. This permits the simulation of wave and particle effects simultaneously and simulates images photon by photon. Here, we give a first demonstration of the method by simulating the Gaussian double-slit experiment. It has the potential for realistic simulation of low-dose imaging.

physics.optics↗

arXiv2023

Systematic Review on Learning-based Spectral CT

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

physics.med-ph↗

arXiv2022

Novel ion imaging concept based on time-of-flight measurements with low gain avalanche detectors

Treatment planning in ion beam therapy requires accurate knowledge of the relative stopping power (RSP) distribution within the patient. Currently, RSP maps are obtained via conventional x-ray computed tomography (CT) by converting the measured attenuation coefficients of photons into RSP values for ions. Alternatively, to avoid conversion errors that are inherent to this method, ion computed tomography (iCT) can be used since it allows determining the RSP directly. In typical iCT systems, which usually consist of a tracking system and a separate residual energy detector, the RSP is obtained by measuring the particle trajectory and the corresponding water equivalent path length (WEPL) of single ions travelling through the patient. Within this work, we explore a novel iCT approach which does not require a residual energy detector. Instead, the WEPL is estimated indirectly by determining the change in time of flight (TOF) due to the energy loss along the ion's path. For this purpose, we have created a Geant4 model of a TOF-iCT system based on low gain avalanche detectors (LGADs), which are fast 4D-tracking detectors that can measure the time of arrival and position of individual particles with high spatial and time precision. To assess the performance of this TOF-iCT concept, we determined the RSP resolution and accuracy for different system settings using the Catphan CTP404 sensitometry phantom. Within the set of investigated system parameters, the lower limit of the RSP accuracy was found at 0.91%, demonstrating the proof-of-principle of this novel TOF-iCT concept. The main advantage of using this approach is that it could potentially facilitate clinical integration due to its compact design, which, however, requires experimental verification and an improvement of the current WEPL calibration procedure.

physics.med-ph↗

arXiv2021

Relative stopping power precision in time-of-flight proton CT

Proton computed tomography (CT) is similar to x-ray CT but relies on protons rather than photons to form an image. In its most common operation mode, the measured quantity is the amount of energy that a proton has lost while traversing the imaged object from which a relative stopping power map can be obtained via tomographic reconstruction. To this end, a calorimeter which measures the energy deposited by protons downstream of the scanned object has been studied or implemented as energy detector in several proton CT prototypes. An alternative method is to measure the proton's residual velocity and thus its kinetic energy via the time of flight (TOF) between at least two sensor planes. In this work, we study the precision, i.e. image noise, which can be expected from TOF proton CT systems. We rely on physics models on the one hand and statistical models of the relevant uncertainties on the other to derive closed form expressions for the noise in projection images. The TOF measurement error scales with the distance between the TOF sensor planes and is reported as velocity error in ps/m. We use variance reconstruction to obtain noise maps of a water cylinder phantom given the scanner characteristics and additionally reconstruct noise maps for a calorimeter-based proton CT system as reference. We find that TOF proton CT with 30 ps/m velocity error reaches similar image noise as a calorimeter-based proton CT system with 1% energy error (1 sigma error). A TOF proton CT system with a 50 ps/m velocity error produces slightly less noise than a 2% calorimeter system. Noise in a reconstructed TOF proton CT image is spatially inhomogeneous with a marked increase towards the object periphery. This systematic study of image noise in TOF proton CT can serve as a guide for future developments of this alternative solution for estimating the residual energy of protons after the scanned object.

physics.med-ph↗

arXiv2021

Feasibility study of a proton CT system based on 4D-tracking and residual energy determination via time-of-flight

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 computed tomography (CT), which entails RSP inaccuracies due to conversion errors. A suitable method to improve the treatment plan accuracy is proton computed tomography (pCT). A typical pCT system consists of a tracking system and a separate residual energy (or range) detector to measure the RSP distribution directly. This paper introduces a novel pCT system based on a single detector technology, namely low gain avalanche detectors (LGADs). LGADs are fast 4D-tracking detectors, which can be used to simultaneously measure the particle position and time with precise timing and spatial resolution. In contrast to standard pCT systems, the residual energy is determined via a time-of-flight (TOF) measurement between different 4D-tracking stations. The design parameters for a realistic proton computed tomography system based on 4D-tracking detectors were studied and optimized using Monte Carlo simulations. The RSP accuracy and RSP resolution were measured inside the inserts of the CTP404 phantom to estimate the performance of the pCT system. After introducing a dedicated calibration procedure for the TOF calorimeter, RSP accuracies < 0.6 % could be achieved. Furthermore, the design parameters with the strongest impact on the RSP resolution were identified and a strategy to improve RSP resolution is proposed.

physics.med-ph↗