Ptychography is a powerful technique that combines scanning transmission X-ray microscopy (STXM) and coherent diffraction imaging (CDI). Thanks to the fluctuation of light and the development of phase retrieval algorithms, ptychography has achieved ultra-high imaging resolution. However, iterative solutions are time consuming. A cascaded residual network is used to recover the amplitude and phase information of the sample directly from the received diffraction patterns. With the powerful coding and decoding capabilities of the residual blocks, the correspondence between diffraction patterns and sample distribution information can be learned by the network. Experimental results show that the use of cascaded residual blocks enhances the reconstructive capability of the network.
X-ray tomographic imaging has become an important analytical tool with a wide range of applications. It is inevitable that noise is introduced in CT images, and noise reduction is necessary. To solve this problem, we considered to use the nonlocal property of similar block search and proposed a deep learning network based on similar block learning for noise reduction of micro CT short exposure time scanned images to improve the scanning efficiency while ensuring high quality imaging. The method uses the output of the nonlocal method as a data preprocessing algorithm by combining a nonlocal block matching algorithm with a convolutional neural network, and uses a residual channel attention mechanism to learn the features after feature extraction, which reduces noise while preserving image details. Experimental results show that the method can remove noise from CT images quickly and effectively, and compared with the classical CPCE noise reduction method, the method improves the PSNR index by 1.52 dB, which is consistent with the theoretical assumption.
The alignment of the acquired projections is quite necessary for accurate reconstruction of nano computer tomography (nano CT) due to thermal drift. In this paper, a method based on features outlier elimination (OE) is proposed to reduce the drift artifacts from the reconstruction slices, and a series of reference sparse projections are required. The rough alignment is realized after the extraction from the Speeded Up Robust Features (SURF) of both the original projections and the reference projections, of which the structure similarity (SSIM) is utilized to eliminate the outlier features. Then, the rest features are used for the further alignment for reconstruction. The simulation results show that the proposed method is more accurate and robust than image registration method based on entropy correlation coefficient (ECC) and traditional SURF. Scanning results of bamboo stick show that the proposed method can preserve the details of slices.
Computed Tomography (CT) is one of the essential techniques for non-destructive testing. The acquisition of accurate reconstructed images is the basis for the subsequent analytical processing tasks. This paper proposes a convolutional neural network-based CT reconstruction algorithm to generate reconstructed CT images directly from sinogram by the feature coding and decoding capability. The reconstruction of abdominal scanning data is carried out by this method, and the results show that we can quickly obtain corresponding reconstruction results. During the network training, we designed different data pre-processing methods. We analysed the role of each module in the network by visualizing the output features of each module. Finally, the role of different modules in feature extraction and image generation is further analysed. We found that the conversion from projection to image can be effectively achieved using only convolution operations. It is essential for reconstructing CT images using deep learning techniques.
A markerless projection drift alignment approach for X-ray nanotomography is presented. Drifts in projection from different angles are aligned by applying offsets calculated between successive images after acquisition time division, taking the advantage of the fact that the shorter the time, the less the drift. Involving neither iteration nor parameter selection, it can combine a number of existing image registration techniques and could be adopted for other tomographic imaging techniques. The application of this algorithm has been demonstrated in a laboratory X-ray nanotomography system using single photon detection, in which a standard Siemens star resolution target is initially captured for 2D evaluation and a bamboo stick is used for 3D imaging, leading to sharper image without blur and a much higher resolution.
Due to the limitation of scanning conditions and the emerging clinical application of local imaging, local tomography has become a research hotspot. Traditionally, completing the projection data through interpolation or spatial transformation iteration is popular to overcome the truncation artifact, such as iterative reconstruction algorithms. Recently, deep learning networks have been incorporated in dealing with such problems. Instead of global filtering on truncated data, the proposed work focuses on the full feature extraction using U-net as well as the usage of the redundancy between projection sinogram, by performing extrapolation of truncated projection data through data learning and then filter back projection local reconstruction with high efficiency. During the learning process, 3439 projections are selected as complete projection data , and the corresponding truncated data is simulated according to the actual truncation situation. Then, 150 of the truncated data are randomly selected as the test samples, while the rest 3289 of those as the training samples in the U-net. The output sinogram data is compared with the original complete data by calculating the L2 loss function of both. And the Adam optimizer is used to continuously optimize the parameters of the network. RMSE and NMAD are used to quantitatively evaluate the reconstruction effect. Experimental results show that the proposed method based on truncated data extrapolation network can obviously suppress the ring artifacts and compared with images directly reconstructed using truncated projection data, the RMSE is reduced by an average of 43.185%, and the NMAD is reduced by 44.24%.
Low dose computed tomography (LDCT) has attracted considerable interest in medical imaging fields. Reducing tube current intensity and decrease the exposure time are the two main ways in clinic applications. Nevertheless, the resulting statistical noise will seriously degrade CT image quality for diagnosis. To make full use of the original projection data as well as further improving the small dataset processing ability of U-net, this study aimed to investigate a low dose X-ray CT image denoising method via U-net in projection domain (PDI U-net). Meanwhile, in view of avoiding the excessive smoothing of the small structures, the inception module is introduced in the encoding stage of the network. And different convolution kernel operations of 1×1, 3×3, and 5×5 are used in parallel to obtain multi-scale image features, increasing the depth and width of the network while reducing the parameters. Furthermore, the shortcut connection is utilized to transfer the low-level area local detail information to the high-level area. By merging it with the global information of the high-level area, the proposed network can maintain the image details while de-noising. The experimental results show that the method proposed can significantly improve the image quality with clear feature edges and close visual appearance to the reference high dose CT images. Compared with LDCT and Residual Encoder-Decoder Convolutional Neural Network (RED-CNN), the peak signal to noise radio (PSNR) is improved by 9.02dB and 2.74dB, and the structural similarity (SSIM) is improved by 0.43 and 0.07, respectively.
Computed tomography (CT) has been extensively used in nondestructive testing, medical diagnosis, etc. In the field of modern medicine, metal implants are widely used in people's daily life, and the serious artifacts in CT reconstruction images caused by metal implants cannot be ignored. Sinogram contains the most realistic projection information of patients. Processing in the sinogram domain directly can make the effective information maximum extent preserved. In this paper, we propose a novel method based on full convolutional network (FCN) for metal artifact reduction in the sinogram domain. The networks we introduced use the complete sinogram data to learn a mapping function to correct the metal-corrupted sinogram data. The network takes the metal-corrupted sinogram as the input and takes the artifact-free sinogram as the target. Compared with the existing deep learning-based CT artifact reduction methods, our work just uses the sinogram information to correct the metal artifacts. The proposed network can process images of different sizes. Our initial results on a simulated dataset to demonstrate the potential effectiveness of this new approach to suppressing artifacts.
To investigate the effects of x ray tube setting on image quality in industrial computed tomography, an experimental characterization with constant tube powers has been reported in this paper. A series of CT scans for a QRM Medium-Contrast-Phantom were performed with a constant tube power of 40W and other scanning parameters, varying tube voltages from 80kV to 125kV and tube currents from 320μA to 500μA. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measured on the reconstructed images indicated that increasing the tube voltage can improve the SNR, as well as the CNR in high density areas. While in low density regions in the phantom, higher CNR resulted from lower voltage or higher tube current. Furthermore, a custom-made aluminum cylinder is scanned several times for the assessment of the CT spatial resolution, similarly keeping a constant tube power and variable tube voltages and currents. According to the obtained modulation transfer function (MTF)1/10 values, defined as the spatial frequency corresponding to a contrast loss of 10 %, it is found that using the same tube power, the tube voltage has a greater impact on improving the CT spatial resolution.
Metal objects inside the field of view would introduce severe artifacts in x-ray CT images, which would severely degrade the quality of CT data and bring huge difficulties for subsequent image processing and analysis. Correction of metal artifacts has become a hot and difficult issue in X-ray CT. In recent years, deep learning has rapidly gained attention for employment on image processing. In this study, we introduce a Fully Convolutional Networks (FCNs) into the MAR in image domain. The network reduces metal artifacts by learning an end-to-end mapping of images from metal-corrupted CT images to their corresponding artifact-free ground truth. The network takes the metal-corrupted CT images as the input and takes the artifact-free images as the target. The convolution layers extract features from the input images and map them to the target images, and the deconvolution layers use these features to build the predicted outputs. Experimental results demonstrate that the proposed method can well reduce metal artifacts of CT images, and take a shorter time to process the images than traditional method.
In X-ray computed tomography (CT), variability in tube voltage and current setting may affect the image quality. Based on an industrial X-ray micro-CT scanner, this paper will investigate the impact of the X-ray tube setting on image quality of the projection images as well as the reconstruction results with various voltage and current choices in the CT experiments. Fresh corn is initially selected as an experimental sample in 6 different series of measurements. We set the tube current at 130μA, 200μA, 270μA while keeping the tube voltage and other acquisition parameters constant, and then keep the tube current constant while varying the tube voltage at 70kV and 100kV, respectively. For evaluation both the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) are calculated as image quality criteria for each set of the projected images and reconstructed images. The results indicate that increasing the tube current and voltage can both improve the SNR and CNR. Furthermore, the tube voltage has more impact on the improvements. At the same time, the variations on image quality of reconstruction images keeps the same pace with that of the projection images. The reliability of the conclusion will be further explored experimentally using aircraft blades in CT nondestructive testing.
X-ray cone-beam computed tomography, featuring high precision and fast-imaging speed, has been widely used in industrial non-destructive testing applications for the three dimensional visualization of internal structures. Due to mechanical imperfections, geometric calibrations are imperative to high quality image reconstruction. Currently, the twoball phantom-based calibration procedures exploiting the projection trajectories of the phantoms are the most commonly used approach for the estimation of the geometrical parameters and the calibration of CT system. However, an additional scan needs to be performed, even after each object acquisition when lack of system reproducibility, leading to multiplied calibration times. The emphasis of this paper is to optimize the process of acquisition in cone-beam CT imaging with minimal time, based on the understanding of the determination of the ball position in typical phantom-based geometric calibration algorithms. An applicable condition of the calibration algorithm for simultaneously scanning objects and calibration phantoms is proposed and demonstrated, which is that the minimum projection value of the scanned object needs to be at least 100 counts higher than those of the calibration phantom, with consideration of the system noise. The CT experiments are based on a laboratory industrial cone-beam CT system with a micro-focus x-ray tube (Thales Hawkeye 130) and a flat panel detector (Thales Pixium RF4343). Objects imaged are chosen with a wide projection value range, from low-Z watermelon seeds and high-Z materials, including a standard Micro CT Bar Pattern Phantom (QRM) for image quality assessment. In these experiments, objects, as well as two-ball phantoms, both placed in the field of view without overlapping in the vertical direction, are projected over 360 degrees, instead of scanning the calibration phantoms separately. Hence, the true geometrical relationship is resolved utilizing the two-ball algorithm. Both simulation and experimental results confirm that the calculated geometrical parameters will not be affected by the objects as long as their projection value difference meeting the requirements above. And the reconstruction image quality is almost the same with those by an independent calibration. Compared to the traditional application of the phantombased geometrical calibration method, the novel approach presented in this paper has obvious advantages from an imaging perspective, saving acquisition time and eliminating the undesired influence from the operation staff for the same cost.
X-ray computed tomography (CT) has been extensively applied in industrial non-destructive testing (NDT). However, in practical applications, the X-ray beam polychromaticity often results in beam hardening problems for image reconstruction. The beam hardening artifacts, which manifested as cupping, streaks and flares, not only debase the image quality, but also disturb the subsequent analyses. Unfortunately, conventional CT scanning requires that the scanned object is completely covered by the field of view (FOV), the state-of-art beam hardening correction methods only consider the ideal scanning configuration, and often suffer problems for interior tomography due to the projection truncation. Aiming at this problem, this paper proposed a beam hardening correction method based on radon inversion transform for interior tomography. Experimental results show that, compared to the conventional correction algorithms, the proposed approach has achieved excellent performance in both beam hardening artifacts reduction and truncation artifacts suppression. Therefore, the presented method has vitally theoretic and practicable meaning in artifacts correction of industrial CT.
KEYWORDS: X-ray computed tomography, X-rays, Optical filters, Signal attenuation, Photons, Metals, Monte Carlo methods, Aluminum, Mass attenuation coefficient, Copper
Beam hardening artifact is common in X-ray computed tomography (X-CT). Using the metal sheet as a filter to preferentially attenuate low-energy photons is a simple and effective way for beam hardening artifact correction. However, generally it requires a large quantity of experiments to compare the filter material and thickness, which is lack of guidance of theory. In this paper, a novel filter design method for beam hardening correction, especially for middle energy X-CT, is presented. First, the spectrum of X-ray source under a certain tube voltage is estimated by Monte Carlo (MC) simulation or other simulation methods. Next, according to the X-ray mass attenuation coefficients of the object material, the energy range to be retained can be roughly determined in which the attenuation coefficients change slowly. Then, the spectra filtering performance with different filter materials and thicknesses can be calculated using the X-ray mass attenuation coefficients of each filter material and the simulated primitive spectrum. After that, the mean energy ratio (MER) of post-filter mean energy to pre-filter mean energy is obtained. Finally, based on the spectrum filtering performance and MER of the metal material, a suitable filter strategy is easily selected. Experimental results show that, the proposed method is simple and effective on beam hardening correction as well as increasing the image quality.
The backprojection-filtration (BPF) algorithm has become a good solution for local reconstruction in cone-beam computed tomography (CBCT). However, the reconstruction speed of BPF is a severe limitation for clinical applications. The selective-backprojection filtration (S-BPF) algorithm is developed to improve the parallel performance of BPF by selective backprojection. Furthermore, the general-purpose graphics processing unit (GP-GPU) is a popular tool for accelerating the reconstruction. Much work has been performed aiming for the optimization of the cone-beam back-projection. As the cone-beam back-projection process becomes faster, the data transportation holds a much bigger time proportion in the reconstruction than before. This paper focuses on minimizing the total time in the reconstruction with the S-BPF algorithm by hiding the data transportation among hard disk, CPU and GPU. And based on the analysis of the S-BPF algorithm, some strategies are implemented: (1) the asynchronous calls are used to overlap the implemention of CPU and GPU, (2) an innovative strategy is applied to obtain the DBP image to hide the transport time effectively, (3) two streams for data transportation and calculation are synchronized by the cudaEvent in the inverse of finite Hilbert transform on GPU. Our main contribution is a smart reconstruction of the S-BPF algorithm with GPU’s continuous calculation and no data transportation time cost. a 5123 volume is reconstructed in less than 0.7 second on a single Tesla-based K20 GPU from 182 views projection with 5122 pixel per projection. The time cost of our implementation is about a half of that without the overlap behavior.
A powerful volume X-ray tomography system has been designed and constructed to provide an universal tool for the three-dimensional nondestructive testing and investigation of industrial components, automotive, electronics, aerospace components, new materials, etc. The combined system is equipped with two commercial X-ray sources, sharing one flat panel detector of 400mm×400mm. The standard focus 450kV high-energy x-ray source is optimized for complex and high density components such as castings, engine blocks and turbine blades. And the microfocus 225kV x-ray source is to meet the demands of micro-resolution characterization applications. Thus the system’s penetration capability allows to scan large objects up to 200mm thick dense materials, and the resolution capability can meet the demands of 20μm microstructure inspection. A high precision 6-axis manipulator system is fitted, capable of offset scanning mode in large field of view requirements. All the components are housed in a room with barium sulphate cement. On the other hand, the presented system expands the scope of applications such as dual energy research and testing. In this paper, the design and implemention of the flexible system is described, as well as the preliminary tomographic imaging results of an automobile engine block.
Three-dimensional observation for the integrated circuit is of potential interest to an improved understanding of the formation of embedded voids in the copper interconnects, which has become major reliability concern in achieving highperformance microprocessors. Nano-scale line width requires the imaging technique with a high spatial resolution as well as penetration through several microns of silicon to maintain the sample integrity. The resolution of Optical microscopy is not enough and the electron microscopy requires invasive sample cross-sectioning, not permitting the in situ identification. The utilization of non-destructive imaging using 3D x-ray microscopy offers the needed resolution and penetration ability without significant damage. In this paper, the ability to image tomographically voids in copper interconnects and the seven metallization layers are demonstrated with bright contrast and a sub-50nm resolution on 8keV BSRF X-ray microscope. The sample is specifically prepared for this initial experiment, with a diameter of ~10.3μm and a thickness of 15.7μm. In the future experiment we are attempting to image the sample in its original state with only the backside silicon substrate removed, realizing the more non-destructive observation.
An imaging Thomson scattering system has been designed and built to perform the spatial-resolution-oriented plasma
electron temperature and density measurements, which incorporates a second generation image intensifier and an
EMCCD as a detection system. In general, the characteristic of weak scattering of radiation is the most concern in
Thomson scattering systems. Therefore, it's quite essential for the initial system design to avoid further loss of the
amount of radiant power transferred from the source to the detector, and to perform the detection capability verification
based on the designed setup. This paper will focus on three points. Firstly, The key design parameters including
magnification and f number of the collection lens, the diameter and NA of the fiber, the entrance and exit slit area and f
number of the spectrometer are designed interactively to maximize light throughput, with also beam quality taken into
account. Then, the system setup is described and the expected photon number per pulse per scattering length is
calculated. Finally, from the comparison between the measured radiation photons of a standard lamp and the calculated
photons based on the designed condition, with both spatial binning and EM gain performed, the capability of the
detection system is verified.
KEYWORDS: Electron multiplying charge coupled devices, Signal to noise ratio, Image intensifiers, Interference (communication), Signal detection, Sensors, Image resolution, Thomson scattering, Signal processing, Microchannel plates
In order to unite the merits of ICCD (intensified charge coupled device) and EMCCD (electron multiplying charge
coupled device) for detection of the weak signal in the high resolution Thomson scattering system from strong radiation
background, a second generation image intensifier, lens coupled with an EMCCD are used together as a detector. The
signal photon flux is so low in the actual measurement situation that the gain of the I.I., on-chip multiplication gain of
EMCCD and on-chip binning scheme might all need to be utilized to enhance the detection capability or to set lower
demands for other devices. At the same time, however, these amplification processes bring unexpected noise in addition
to the detector noise itself, which will further degrade the signal to noise ratio (SNR). This paper will focus on three
points. Firstly, the three gain methods, including MCP gain, EM gain and binning are theoretically described. Secondly,
the amount of increase in signal counts based on this detector combination is experimentally investigated at various gain
settings, as well as the total noise. Finally, a gain selection disciplines aiming to obtain an optimum SNR is generalized
according to the comparison between test results and theory.
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