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.
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.
Limited-angle computed tomography (CT) image reconstruction is a challenging reconstruction problem in the fields of CT. With the development of deep learning, the generative adversarial network (GAN) perform well in image restoration by approximating the distribution of training sample data. In this paper, we proposed an effective GAN-based inpainting method to restore the missing sinogram data for limited-angle scanning. To estimate the missing data, we design the generator and discriminator of the patch-GAN and train the network to learn the data distribution of the sinogram. We obtain the reconstructed image from the restored sinogram by filtered back projection and simultaneous algebraic reconstruction technique with total variation. Experimental results show that serious artifacts caused by missing projection data can be reduced by the proposed method, and it is hopeful to solve the reconstruction problem of 60° limited scanning angle.
The improvement of computed tomography (CT) image resolution is beneficial to the subsequent medical diagnosis, but it is usually limited by the scanning devices and great expense. Convolutional neural network (CNN)- based methods have achieved promising ability in super-resolution. However, existing methods mainly focus on the super-resolution of reconstructed image and do not fully explored the approach of super-resolution from projectiondomain. In this paper, we studied the characteristic of projection and proposed a CNN-based super-resolution method to establish the mapping relationship of low- and high-resolution projection. The network label is high-resolution projection and the input is its corresponding interpolation data after down sampling. FDK algorithm is utilized for three-dimensional image reconstruction and one slice of reconstruction image is taken as an example to evaluate the performance of the proposed method. Qualitative and quantitative results show that the proposed method is potential to improve the resolution of projection and enables the reconstructed image with higher quality.
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.
With the development of technology, the traditional X-ray CT can’t meet the modern medical and industry needs for component distinguish and identification. This is due to the inconsistency of X-ray imaging system and reconstruction algorithm. In the current CT systems, X-ray spectrum produced by X-ray source is continuous in energy range determined by tube voltage and energy filter, and the attenuation coefficient of object is varied with the X-ray energy. So the distribution of X-ray energy spectrum plays an important role for beam-hardening correction, dual energy CT image reconstruction or dose calculation. However, due to high ill-condition and ill-posed feature of system equations of transmission measurement data, statistical fluctuations of X ray quantum and noise pollution, it is very hard to get stable and accurate spectrum estimation using existing methods. In this paper, a model-based X-ray energy spectrum estimation method from CT scanning data with energy spectrum filter is proposed. First, transmission measurement data were accurately acquired by CT scan and measurement using phantoms with different energy spectrum filter. Second, a physical meaningful X-ray tube spectrum model was established with weighted gaussian functions and priori information such as continuity of bremsstrahlung and specificity of characteristic emission and estimation information of average attenuation coefficient. The parameter in model was optimized to get the best estimation result for filtered spectrum. Finally, the original energy spectrum was reconstructed from filtered spectrum estimation with filter priori information. Experimental results demonstrate that the stability and accuracy of X ray energy spectrum estimation using the proposed method are improved significantly.
The sparse scanning imaging methods for x-ray CT is a promising approach to speed up scanning or reduce radiation dose to patients. The major problem for sparse parallel projections is hard to reconstruct high quality image. It suffers severe streak artifacts in reconstruction if the popular filtered back projection (FBP) method is employed. Although several total variation (TV) regularization based algorithms have been developed for sparse-view CT imaging, they still face challenges in both time consumption and computational complexity when the objective image is large. In this paper, a CT reconstruction algorithm, which is named INNG-TV (iterative next-neighbor regridding-total variation), based on extrapolation in frequency is proposed to improve the performance. We first convert data, which is sampled from parallel beam CT, into frequency domain by Fourier transform and linear interpolation. In the following process of iteration, the known data of projection in Fourier space keep constant, whereas the unknown data are estimated by INNG extrapolation. At the same time, prior knowledge and constrained optimization, such as non-negativity constraint and total variation regularization, are introduced to image reconstruction in image space. The numerical simulation results show that the proposed method has better performance in reconstruction quality than ART-TV (algebraic reconstruction technique-total variation). The proposed method not only demonstrates its superiority in time consumption, but also offers outstanding reconstruction quality for sparse-view scan, which makes it significant to sparse-view CT imaging.
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.
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.
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.
Linear scan Computed Tomography (LCT) has emerged as a promising technique in fields like industrial scanning and security inspection due to its straight-line source trajectory and high scanning speed. However, in practical applications of LCT, the ordinary algorithms suffer from serious artifacts owing to the limited-angle and insufficient data. In this paper, a new method which reconstructs image from partial Fourier data sampled in pseudo polar grid based on alternating direction anisometric total variation minimization has been proposed. The main idea is to reform the image reconstruction problem into solving an under-determined linear equation, and then reconstruct image by applying the popular total variation (TV) minimization to reform an unconstraint optimization by means of augmented Lagrange method and using the alternating minimization method of multiplier (ADMM) which contributes to the fast convergence. The proposed method is practical in the large-scale task of reconstruction due to its algorithmic simplicity and computational efficiency and reconstructs better images. The results of the numerical simulations and pseudo real data reconstructions from the linear scan validate that the proposed method is both efficient and accurate.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.