3D Radio and X-Ray Modeling and Data Analysis Software: Revealing Flare Complexity

We have undertaken a major enhancement of our IDL-based simulation tools developed earlier for modeling microwave and X-ray emission. The object-based architecture provides an interactive graphical user interface that allows the user to import photospheric magnetic field maps and perform magnetic field extrapolations to almost instantly generate 3D magnetic field models, to investigate the magnetic topology of these models by interactively creating magnetic field lines and associated magnetic flux tubes, to populate the flux tubes with user-defined nonuniform thermal plasma and anisotropic, nonuniform, nonthermal electron distributions; to investigate the spatial and spectral properties of radio and X-ray emission calculated from the model, and to compare the model-derived images and spectra with observational data. The application integrates shared-object libraries containing fast gyrosynchrotron emission codes developed in FORTRAN and C++, soft and hard X-ray codes developed in IDL, a FORTRAN-based potential-field extrapolation routine and an IDL-based linear force free field extrapolation routine. The interactive interface allows users to add any user-defined radiation code that adheres to our interface standards, as well as user-defined magnetic field extrapolation routines. Here we use this tool to analyze a simple single-loop flare and use the model to constrain the 3D structure of the magnetic flaring loop and 3D spatial distribution of the fast electrons inside this loop. We iteratively compute multi-frequency microwave and multi-energy X-ray images from realistic magnetic fluxtubes obtained from an extrapolation of a magnetogram taken prior to the flare, and compare them with imaging data obtained by SDO, NoRH, and RHESSI instruments. We use this event to illustrate use of the tool for general interpretation of solar flares to address disparate problems in solar physics.Comment: 12 pages, 11 figures, ApJ accepte

LASER RANGE IMAGING FOR ON-LINE MAPPING OF 3D IMAGES TO PSEUDO-X-RAY IMAGES FOR POULTRY BONE FRAGMENT DETECTION

A laser ranging image system was developed for on-line high-resolution 3D shape recovery of poultry fillets. The range imaging system in conjunction with X-ray imaging was used to provide synergistic imaging detection of bone fragments in poultry fillets. In this research, two 5 mW diode lasers coupled with two CCD cameras were used to produce 3D information based on structured lights and triangulation. A laser scattering phenomenon on meat tissues was studied when calculating the object thickness. To obtain the accurate 3D information, the cameras were calibrated to correct for camera distortions. For pixel registrations of the X-ray and laser 3D images, the range imaging system was calibrated, and noises and signal variations in the X-ray and laser 3D images were analyzed. Furthermore, the relationship between the X-ray absorption and 3D thickness of fillets was obtained, and a mapping function based on this relationship was applied to convert the fillet 3D images into the pseudo-X-ray images. For the on-line system implementation, the imaging hardware and software engineering issues, including the data flow optimization and the operating system task scheduling, were also studied. Based on the experimental on-line test, the range imaging system developed was able to scan poultry fillets at a speed of 0.2 m/sec at a resolution of 0.8(X) x 0.7(Y) x 0.7(Z) mm3. The results of this study have shown great potential for non-invasive detection of hazardous materials in boneless poultry meat with uneven thickness

Towards real-time 6D pose estimation of objects in single-view cone-beam X-ray

Deep learning-based pose estimation algorithms can successfully estimate the pose of objects in an image, especially in the field of color images. 6D Object pose estimation based on deep learning models for X-ray images often use custom architectures that employ extensive CAD models and simulated data for training purposes. Recent RGB-based methods opt to solve pose estimation problems using small datasets, making them more attractive for the X-ray domain where medical data is scarcely available. We refine an existing RGB-based model (SingleShotPose) to estimate the 6D pose of a marked cube from grayscale X-ray images by creating a generic solution trained on only real X-ray data and adjusted for X-ray acquisition geometry. The model regresses 2D control points and calculates the pose through 2D/3D correspondences using Perspective-n-Point(PnP), allowing a single trained model to be used across all supporting cone-beam-based X-ray geometries. Since modern X-ray systems continuously adjust acquisition parameters during a procedure, it is essential for such a pose estimation network to consider these parameters in order to be deployed successfully and find a real use case. With a 5-cm/5-degree accuracy of 93% and an average 3D rotation error of 2.2 degrees, the results of the proposed approach are comparable with state-of-the-art alternatives, while requiring significantly less real training examples and being applicable in real-time applications.Comment: Published at SPIE Medical Imaging 202

An Innovative Concept of 3D X-Ray Imaging Systems for Painless Breast Cancer Detection

Breast cancer is a life-threatening disease and considered one of the most common forms of cancer among women worldwide. Early and accurate detection with mass screening programmes helps improve a woman’s chances for successful treatment. The current and the most effective technique used for screening and diagnosis of breast cancer is the X-ray mammography. The photon transport detection of such technique is mostly based on a forward scattering mechanism as well as makes use of attenuation and penetration coefficients. The painful compression and the double X-ray exposure of both patients’ breasts carried out during the imaging process remain unavoidable. In addition, the conventional 2D mammography has two major limitations: sensitivity in detecting breast cancers (~ <80%) and the high recall rate (~10%). It suffers from certain limitations, most important of which is tissue overlap and false diagnoses arising thereof. To overcome this and as an alternative, a new 3D imaging method for breast cancer screening and diagnosis, namely, tomosynthesis, has recently been used. In such method, a limited number of low-dose 2D projection images of a patient are used to reconstruct the 3D tissue information. Tomosynthesis systems incorporate an X-ray source that moves over a certain angle to acquire images. This tube motion is a major limitation because it degrades image quality, increases the scan time and causes prolonged patient discomfort. Therefore, the goal of this work was to overcome all of the above limitations by developing an innovative proof of concept for painless 3D X-ray mammography to be hopefully used as a screening and as diagnostic methods for breast cancer detection by utilizing the scattered X-ray photon information. Most imaging modalities required a wide spectrum of capabilities, which span biomedical sciences, physical sciences and clinical medicine; thus, the ongoing methodology aims to establish a collaborative cross-disciplinary research engaging together with scientists in universities and clinicians in hospitals. Consequently, we hope that this work provides the potential to score some successes in clinical imaging science. In order to do this and since it is generally not possible or feasible to use real components to build and optimize a system repeatedly, a Monte Carlo simulation was used. The first phase focused on realistic computer simulation of the proposed imaging system to find the optimum setup as well as to aid in the analysis of the effect of various factors on the system performance. Thus, the main focus was on 3D mammography imaging simulation setup. Five main steps have been carefully checked and successfully produced: (a) the production of X-ray radiation or source after careful and detailed physics check. This includes the interaction between the X-ray photons and the object (the 3D breast phantom) that is used on scan as well as the detector system and its associated electronics modelled. (b) Next is the realistic modelling of anthropomorphic breast phantoms to check if the effectiveness of prediction of the simulation is successfully achieved. A computer simulation model is developed to estimate the radiation dose to the breast that would be incurred using mammography. Mono-energetic normalized glandular dose coefficients, DgN(E), were computed for energies 11–120 keV using breast phantoms of various sizes and compositions