Chest X-rays are a crucial diagnostic tool, but their interpretation can be time-consuming. Deep learning offers a promising solution, but requires large, well-annotated datasets, which are often limited by privacy issues and data access restrictions. Federated learning (FL) allows hospitals to collaboratively train deep learning models without data sharing, addressing these limitations. In this paper, we propose an FL model based on lightweight ConvNeXt architecture and Federated Averaging to analyze both performance and interpretability as the number of clients increases, while maintaining the same total amount of data in an independent identically distributed (IID) scenario for chest X-ray imaging. The results demonstrate superior performance of the FL models compared to the local one, and the interpretability analysis shows that FL models produce better alignment with ground truth compared to local models. However, both performance and interpretability decline as the number of clients increases. These findings highlight the need for strategies to mitigate performance degradation in high-client scenarios to improve the clinical applicability of FL-based systems.

Deep Learning approaches show promise for improving tomosynthesis reconstruction but require paired CT and tomosynthesis datasets that are difficult to obtain. This work presents ChestXsim, an open-source Python framework that enable the simulation of digital chest tomosynthesis (DCT) from chest CT data. Its modular design includes the preprocessing pipeline to adapt helical chest CT volumes to a standard tomosynthesis positioning and the simulation of polychromatic projections with noise modelling. Additionally, ChestXsim provides reconstruction techniques (FDK/SART) and leverages GPU acceleration through open-source ASTRA kernels. ChestXsim offers a fast and efficient method for simulating DCT from standard chest CT data, making it a valuable tool for generating paired datasets.

The field of view (FOV) in Cone Beam Computed tomography (CT) is limited by the detector size, often leading to truncation artifacts that obscure anatomical structures and compromise diagnostic accuracy. While several deep learning solutions have demonstrated success in extending the FOV, they primarily rely on spatial inpainting and do not fully exploit the frequency-domain characteristics of sinograms. Besides, some of the most successful solutions are slow as they work both in the projection and reconstruction domain or use heavy networks, hindering their implementation in real clinical practice. In this work we propose a new deep learning method that works in the sinogram domain solely to achieve accurate and efficient sinogram completion, reducing truncation artifacts while maintaining real-time clinical feasibility. The method is based on a recurrent network that incorporates Fast Fourier Convolution blocks at early feature extraction stages to enhance sinogram extrapolation by leveraging spectral domain information. The proposed method holds potential for improving CT image reconstruction quality, enhancing diagnostic accuracy, and expanding the applicability of C -arm CT systems in various medical fields.

In Cone Beam CT (CBCT) systems, each acquisition is carried out with a stationary bed. Therefore, the field of view is limited by the size of the detector. To overcome this limitation, it is common to perform successive acquisitions for different bed positions, which are subsequently combined to increase the field of view in the longitudinal direction. To avoid the appearance of double edges in the resulting volume, it is necessary to calculate the exact bed displacement, which has traditionally been obtained by prior geometric calibrations with calibration phantoms. This implies that the calibration must be repeated periodically to adapt to any changes that the equipment may undergo. As an alternative, in this work we propose the use of an automatic calibration algorithm capable of obtaining the misalignment parameters in real time, allowing to obtain multibed images in small and regular animal CBCT systems without the need of a previous calibration.

In computed tomography (CT) systems it is common to acquire several consecutive datasets for different bed positions, which are subsequently combined to enlarge the field of view in the longitudinal direction. For this combination, the geometric calibration of the bed motion is necessary to avoid double edges in the overlaped area. This calibration is performed periodically using calibration phantom with markers to guide parameter esti-mation. This work presents a novel correlation-based automatic bed stitching tool for CT that avoids the need of the calibration step. Our approach exploits the massive parallelism offered by GPUs and features an optimized memory model that allows large volumes to be stitched in near-real time. Evaluation in rodent studies demonstrates not only that the offered implementation is able to paste tomographic studies in reduced time, but also that it reduces the memory footprint.

CT images are often affected by beam-hardening artifacts due to the polychromatic nature of the X-ray spectra. These artifacts appear in the image as cupping in homogeneous areas and as dark bands between dense regions such as bones. This paper proposes a simplified statistical reconstruction method for X-ray CT based on Poisson statistics that accounts for the non-linearities caused by beam hardening. The main advantages of the proposed method over previous algorithms are that it avoids the preliminary segmentation step, which can be tricky, especially for low-dose scans, and it does not require knowledge of the whole source spectrum, which is often unknown. Each voxel attenuation is modeled as a mixture of bone and soft tissue by defining density-dependent tissue fractions and maintaining one unknown per voxel. We approximate the energy-dependent attenuation corresponding to different combinations of bone and soft tissues, the so-called beam-hardening function, with the 1D function corresponding to water plus two parameters that can be tuned empirically. Results on both simulated data with Poisson sinogram noise and two rodent studies acquired with the ARGUS/CT system showed a beam hardening reduction (both cupping and dark bands) similar to analytical reconstruction followed by post-processing techniques but with reduced noise and streaks in cases with a low number of projections, as expected for statistical image reconstruction.

BACKGROUND: Standard cone-beam computed tomography (CBCT) involves the acquisition of at least 360 projections rotating through 360 degrees. Nevertheless, there are cases in which only a few projections can be taken in a limited angular span, such as during surgery, where rotation of the source-detector pair is limited to less than 180 degrees. Reconstruction of limited data with the conventional method proposed by Feldkamp, Davis and Kress (FDK) results in severe artifacts. Iterative methods may compensate for the lack of data by including additional prior information, although they imply a high computational burden and memory consumption. RESULTS: pixels) using partitioning strategies in forward- and back-projection operations. We evaluated the algorithm on small-animal data for different scenarios with different numbers of projections, angular span, and projection size. Reconstruction time varied linearly with the number of projections and quadratically with projection size but remained almost unchanged with angular span. Forward- and back-projection operations represent 60% of the total computational burden. CONCLUSION: Efficient implementation using parallel processing and large-memory management strategies together with GPU kernels enables the use of advanced reconstruction approaches which are needed in limited-data scenarios. Our GPU implementation showed a significant time reduction (up to 48 ×) compared to a CPU-only implementation, resulting in a total reconstruction time from several hours to few minutes.

The apparition of new paradigms, programming models, and languages that offer better programmability and better performance turns the implementation of current scientific applications into a less time-consuming task than years ago. One significant example of this trend is the MapReduce programming model and its implementation using Apache Spark. Nowadays, this programming model is mainly used for data analysis and machine learning applications, although it has been expanded to its usage in the HPC community. On the side of programming languages, Python has positioned itself as an alternative to other scientific programming languages, such as Matlab or Julia. In this work we explore the capabilities of Python and Apache Spark as partners in the implementation of the backprojection operator of a CT reconstruction application. We present two interesting approaches with two different types of architectures: a heterogeneous architecture including NVidia GPUs and a full performance CPU mode with the compatibility with C/C++ native source code. We experimentally demonstrate that current CPU-based implementations scale with the number of computational units.

Most small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. Image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp, Davis and Kress (FDK). Currently there is a strong need to speedup the reconstruction of X-Ray CT data in order to extend its clinical applications. The evolution of the semiconductor detector panels has resulted in an increase of detector elements density, which produces a higher amount of data to process. This work focuses on future high-resolution studies (density up to 4096 pixeles), in which multiple level of parallelism will be needed in the reconstruction. In addition, this paper addresses the future challenges of processing high-resolution images in many-core and distributed architectures. In our evaluation section we demonstrate that our solution is 17% faster than recent related works.

Most small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. Image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp et al. Currently there are a strong need to speed-up the reconstruction of XRay CT data in order to extend its clinical applications. We present an efficient modular implementation of an FDK-based reconstruction algorithm that takes advantage of the parallel computing capabilities and the efficient bilinear interpolation provided by general purpose graphic processing units (GPGPU). The proposed implementation of the algorithm is evaluated for a high-resolution micro-CT and achieves a speed-up of 46, while preserving the reconstructed image quality.