A CdTe real time X-ray imaging sensor and system

This paper discusses the development of a real-time high energy x-ray imaging system for use in dynamic fluoroscopy of aero gas turbines. In order to cover the range of subjects on gas turbines, over ten combinations of film and screen types are used. Three different types of x-ray imaging systems were considered for use: direct type intensifiers (cesium iodide phosphors), and indirect type intensifiers — Marconi “Marionette” and the Oude Delft “Delcalix.”

Previous work has demonstrated that Fourier imaging data will often possess multifold linear shift-invariant autoregression relationships. This autoregressive structure is useful because it enables missing data samples to be imputed as a linear combination of neighboring samples, and also implies that certain structured matrices formed from the data will have low rank characteristics. The latter observation has enabled a range of powerful structured low-rank matrix recovery techniques for reconstructing sparsely-sampled and/or low-quality data in Fourier imaging modalities like magnetic resonance imaging. In this work, we demonstrate theoretically and empirically that similar modeling principles also apply to sinogram data, and demonstrate how this can be leveraged to restore missing information from real high-resolution X-ray imaging data from an integrated circuit.

Investigation of molten fuel coolant interaction phenomena using real time X-ray imaging of simulated woods metal-water system

A prototype computerized tomography (CT) setup has been recently built at Nikhef in order to exploit the material resolved capabilities of Medipix based detectors in X-ray imaging. The CT scanner contains a Hamamatsu 90 kVp microfocus X-ray tube and an entirely remotely controllable sample alignment system. The complete setup is fully integrated with the detector operation software. Moreover the 120 frames/s RelaxD readout system [1] allows real time X-ray imaging of fast moving samples.In this work, the description of the setup is given and the firstresults obtained with Medipix2 [2] andTimepix [3] detectors are presented. They concerndetector calibration with fluorescence lines, CT reconstruction ofsmall biological and non-biological samples and material resolved 3Dmicro-imaging [4].

In this work, an attempt is made to induce porosity of varied levels in carbon fiber reinforced epoxy based polymer composite laminates fabricated using prepregs by varying the fabrication parameters such as applied vacuum, autoclave pressure and curing temperature. Different NDE tools have been utilized to evaluate the porosity content and correlate with measurable parameters of different NDE techniques. Primarily, ultrasonic imaging and real time digital X-ray imaging have been tried to obtain a measurable parameter which can represent or reflect the amount of porosity contained in the composite laminate. Also, effect of varied porosity content on mechanical properties of the CFRP composite materials is investigated through a series of experimental investigations. The outcome of the experimental approach has yielded interesting and encouraging trend as a first step towards developing an NDE tool for quantification of effect of varied porosity in the polymer composite materials.

The registration of intraoperative x-ray images with preoperative CT images is an important step in image-guided surgery. However, existing regression-based methods lack an interpretable and stable mechanism when fusing information from intraoperative images and preoperative CT volumes. In addition, existing feature extraction and fusion methods limit the accuracy of poseregression. The objective of this study is to develop a method that leverages both x-ray and computed tomography (CT) images to rapidly and robustly estimate an accurate initial registration within a broad search space. This approach integrates the strengths of learning-based registration with those of traditional registration methodologies, enabling the acquisition of registration outcomes across a wide search space at an acceleratedpace. We introduce a regression-based registration framework to address the aforementioned issues. We constrain the feature fusion process by training the network to reconstruct the high-dimensional feature representation vector of the preoperative CT volume in the embedding space from the input single-view x-ray, thereby enhancing the interpretability of feature extraction. Also, in order to promote the effective fusion and better extraction of local texture features and global information, we propose a lightweight cross-attention mechanism named lite cross-attention(LCAT). Besides, to meet the intraoperative requirements, we employ the intensity-based registration method CMA-ES to refine the result of pose regression. Our approach is verified on both real and simulated x-ray data. Experimental results show that compared with the existing learning-based registration methods, the median rotation error of our method can reach 1.9 and the median translation error can reach 5.6mm in the case of a large search range. When evaluated on 52 real x-ray images, we have a median rotation error of 1.6 and a median translation error of 3.8mm due to the smaller search range. We also verify the role of the LCAT and embedding reconstruction modules in our registration framework. If they are not used, our registration performance will be reduced to approximately random initialization results. During the experiments, our method demonstrates higher accuracy and larger capture range on both simulated images and real x-ray images compared to existing methods. The inspiring experimental results indicate the potential for future clinical application of ourmethod.

To bring a breakthrough in the observation of the gamma-ray universe, we are working on the development of Semiconductor Multi-Compton Telescope(SMCT). In the SMCT, all the energy, positional and timing resolution must be high to ensure high sensitivity. The imaging device based on the high resolution CdTe diode and Si, such as CdTe pixel detectors and Double-sided Si Strip Detectors (DSSDs), are promising candidates for the components of the SMCT. Here we report the results of a CdTe pixel detector connected with a low noise analog ASIC. We obtained an energy resolution of 2.5 keV(FWHM) at 122 keV, with a positional resolution of 2 mm. Performance of the first prototype Compton Camera using a DSSD and two CdTe pixel detectors is also described. We irradiated 100% linearly polarised 170 keV /spl gamma/-ray line to the system and obtained the polarimetric modulation factor of 43%.

The authors have previously described 2-D array transducers with up to several thousand elements operating at frequencies between 2.5 and 5.0 MHz for real time volumetric imaging. Lately, there has been interest in developing catheter based intracardiac imaging systems to aid in the precise tracking of anatomical features and intracardiac devices for improved diagnoses and therapies. The authors constructed several arrays for real time intracardiac volumetric imaging based upon 2 different designs; a 13/spl times/11=143 element 5.0 MHz 2-D array for side scanning applications, and a 10/spl times/10=100 element 7.0 MHz 2-D array for side scanning applications. The 5.0 MHz array fits into a 12 French (3.8 mm OD) catheter and the 7.0 MHz transducer is designed to fit into a 9 French (2.9 mm OD) catheter. The authors also constructed 2 transducers for transthoracic imaging; a 40/spl times/40 5.0 MHz 2-D array, and a 40/spl times/40 7.0 MHz 2-D array. The -6 dB fractional bandwidths for the transducers varied from 27% to 67%. All the transducers were constructed on a 6 layer polyimide interconnect. Both transthoracic and intracardiac volumetric images of ultrasound phantoms and animal models have been obtained using the Duke University real time volumetric imaging system which is capable of generating multiple planes at any desired angle and depth within a pyramidal volume.

As an approach to increased production of rape seed oil from Brassica napus L., Arabidopsis thaliana, a species from the same Brassicaceae family, was used to investigate transport behavior and distribution of matter in the plant body. In this study, sucrose, an initial metabolic product of photosynthesis, labeled with carbon-14 was used. The sucrose was applied to A. thaliana via the surface of a rosette leaf. Using the real time radioisotope imaging system we developed and an imaging plate (IP), images of whole or part of the sample were obtained. The sucrose assimilation products were accumulated in maturing tissue such as flowers and fruits, and in a joint part. From the comparison among branches and stems, it was indicated that there were different patterns of demand and distribution of sucrose assimilation products depending on the tissue and its growing stage. This might be caused by either morphological reason such as diameter and location of the sieve tube, or genetic factors such as an activity of a membrane transport protein. Because of self-absorption of carpels, it was difficult to observe the accumulation of carbon-14 in the seeds inside the fruits; however, an IP image of a frozen section of a fruit revealed that carbon-14 transport to seeds was higher than that of carpels. These methods will help us gain insight into matter transport and strategies to improve the production of rape seed oil.

In percutaneous coronary intervention (PCI), cardiologists must study two different X-ray image sources: a fluoroscopic image and an angiogram. Manipulating a guidewire while alternately monitoring the two separate images on separate screens requires a deep understanding of the anatomy of coronary vessels and substantial training. We propose 2D/2D spatiotemporal image registration of the two images in a single image in order to provide cardiologists with enhanced visual guidance in PCI. The proposed 2D/2D spatiotemporal registration method uses a cross-correlation of two ECG series in each image to temporally synchronize two separate images and register an angiographic image onto the fluoroscopic image. A guidewire centerline is then extracted from the fluoroscopic image in real time, and the alignment of the centerline with vessel outlines of the chosen angiographic image is optimized using the iterative closest point algorithm for spatial registration. A proof-of-concept evaluation with a phantom coronary vessel model with engineering students showed an error reduction rate greater than 74% on wrong insertion to nontarget branches compared to the non-registration method and more than 47% reduction in the task completion time in performing guidewire manipulation for very difficult tasks. Evaluation with a small number of experienced doctors shows a potentially significant reduction in both task completion time and error rate for difficult tasks. The total registration time with real procedure X-ray (angiographic and fluoroscopic) images takes[Formula: see text]60ms, which is within the fluoroscopic image acquisition rate of 15Hz. By providing cardiologists with better visual guidance in PCI, the proposed spatiotemporal image registration method is shown to be useful in advancing the guidewire to the coronary vessel branches, especially those difficult to insert into.

The possibility of real time x-ray images conversion into images carried by visible coherent light is considered. The converting component which represents a spatial light modulator based on liquid crystal photoconductor structure is proposed. The possibilities of the coherent optical processing using such a conversion are considered relatively to nondestructive testing and material science applications.

EXACT: an Expert System for Real Time X-Ray Image Analysis and Classification of Weldings

In this paper, we propose a deep neural network (DNN)-based volumetric image reconstruction framework, enabling an end-to-end volume inference from a 2D X-ray image. The proposed framework is built upon the vector quantised-variational autoencoders of the 2D X-ray images and the 3D cone-beam CT (CBCT) images. We present a bi-directional cross-modal transition module between the discrete latent variables of the 2D and 3D images. In the X-ray-to-CBCT pass, the transition module takes the feature maps of the X-ray images as an input and outputs the discrete volumetric embedding, which is combined with the codebook in the embedding space for the reconstruction of CBCT images. On the other hand, the CBCT-to-X-ray pass realizes the DNN-based volume rendering. Given real clinically-obtained X-ray images without paired volumetric images, we devise an unsupervised learning scheme to optimize the discrete embedding transition module. Our approach utilizes the quantised latent representation and the cross-modality transition module to infer volumetric images. The proposed method has been applied to the lateral X-ray-based CBCT image reconstruction in clinical orthodontics, achieving performance improvements over compared methods.

Introduction: The early automatic diagnosis of the novel coronavirus (COVID-19) disease could be very helpful to reduce its spread around the world. In this study, we revisit the identification of COVID-19 from chest X-ray images using deep learning. Methods: We collected a relatively large COVID-19 dataset—compared with previous studies—containing 309 real COVID-19 chest X-ray images. We also prepared 2,000 chest X-ray images of pneumonia cases and 1,000 images of healthy controls. Deep transfer learning was used to detect abnormalities in our image dataset. We fine-tuned three, pre-trained convolutional neural network (CNN) models on a training dataset: DenseNet 121, NASNetLarge, and NASNet-Mobile. Results: The evaluation of our models on a test dataset showed that these models achieved an average sensitivity rate of approximately 99.45% and an average specificity rate of approximately 99.5%. Conclusion: A larger dataset of COVID-19 X-ray images could lead to more accurate and reliable identification of COVID-19 infections using deep transfer learning. However, the clinical diagnosis of COVID-19 disease is always necessary.

Support value based stent-graft marker detection