483 research outputs found

    Investigation of neural network algorithm for in-situ X-ray tomography

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    X-ray tomography is an important non-destructive technique for materials structure analysis. In certain applications, especially during in-situ experiments, the constraints posed by the experimental conditions limit the image quality obtainable from the limited data acquired. Commonly used direct image reconstruction algorithms tend to produce images with insufficient accuracy when fed with limited data, while the more accurate iterative algorithms introduce the challenge of high computational cost. A proposed alternative is the use of machine learning to improve the image quality of direct algorithms. The Mixed-Scale Dense convolutional neural network algorithm (M-SDNet) was therefore utilized in this study to quantitatively investigate its effect in improving the image quality of image reconstructions using direct algorithms, for in-situ tomography. Results are shown for the effect of number of projections, threshold values, and resolution, for data acquired in laboratory conditions. The cavities present in the studied sample were the focus of the quantitative analysis, where parameters like number of cavities, sphericity, and volume fraction were tracked across the output images from using the M-SDNet algorithm. Two different training strategies of M-SDNet; segmentation training and regression training, were compared with the segmentation training proving to better at reproducing cavities in the output images. The reduction on the number of projections and the required scan time suggest that the Mixed-Scale Dense networks are able to significantly improve the accuracy of image reconstructions, and thus suitable to overcome the experimental constraints during in-situ tomograph

    Cycloidal CT with CNN-based sinogram completion and in-scan generation of training data

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    In x-ray computed tomography (CT), the achievable image resolution is typically limited by several pre-fixed characteristics of the x-ray source and detector. Structuring the x-ray beam using a mask with alternating opaque and transmitting septa can overcome this limit. However, the use of a mask imposes an undersampling problem: to obtain complete datasets, significant lateral sample stepping is needed in addition to the sample rotation, resulting in high x-ray doses and long acquisition times. Cycloidal CT, an alternative scanning scheme by which the sample is rotated and translated simultaneously, can provide high aperture-driven resolution without sample stepping, resulting in a lower radiation dose and faster scans. However, cycloidal sinograms are incomplete and must be restored before tomographic images can be computed. In this work, we demonstrate that high-quality images can be reconstructed by applying the recently proposed Mixed Scale Dense (MS-D) convolutional neural network (CNN) to this task. We also propose a novel training approach by which training data are acquired as part of each scan, thus removing the need for large sets of pre-existing reference data, the acquisition of which is often not practicable or possible. We present results for both simulated datasets and real-world data, showing that the combination of cycloidal CT and machine learning-based data recovery can lead to accurate high-resolution images at a limited dose

    Rapid and flexible high-resolution scanning enabled by cycloidal computed tomography and convolutional neural network (CNN) based data recovery

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    We have combined a recently developed imaging concept (“cycloidal computed tomography”) with convolutional neural network (CNN) based data recovery. The imaging concept is enabled by exploiting, in synergy, the benefits of probing the sample with a structured x-ray beam and applying a cycloidal acquisition scheme by which the sample is simultaneously rotated and laterally translated. The beam structuring provides a means of increasing the in-slice spatial resolution in tomographic images irrespective of the blur imposed by the x-ray source and detector, while the “roto-translation” sampling allows for rapid scanning. Data recovery based on the recently proposed Mixed-Scale Dense (MSD) CNN architecture enables an efficient reconstruction of high-quality, high-resolution images despite the fact that cycloidal computed tomography data are highly incomplete. In the following, we review the basic principles underpinning cycloidal computed tomography, introduce the CNN based data recovery method and discuss the benefit of combining both

    A scalable neural network architecture for self-supervised tomographic image reconstruction

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    We present a lightweight and scalable artificial neural network architecture which is used to reconstruct a tomographic image from a given sinogram. A self-supervised learning approach is used where the network iteratively generates an image that is then converted into a sinogram using the Radon transform; this new sinogram is then compared with the sinogram from the experimental dataset using a combined mean absolute error and structural similarity index measure loss function to update the weights of the network accordingly. We demonstrate that the network is able to reconstruct images that are larger than 1024 Ă— 1024. Furthermore, it is shown that the new network is able to reconstruct images of higher quality than conventional reconstruction algorithms, such as the filtered back projection and iterative algorithms (SART, SIRT, CGLS), when sinograms with angular undersampling are used. The network is tested with simulated data as well as experimental synchrotron X-ray micro-tomography and X-ray diffraction computed tomography data

    Noise2Inverse: Self-supervised deep convolutional denoising for tomography

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    Recovering a high-quality image from noisy indirect measurements is an important problem with many applications. For such inverse problems, supervised deep convolutional neural network (CNN)-based denoising methods have shown strong results, but the success of these supervised methods critically depends on the availability of a high-quality training dataset of similar measurements. For image denoising, methods are available that enable training without a separate training dataset by assuming that the noise in two different pixels is uncorrelated. However, this assumption does not hold for inverse problems, resulting in artifacts in the denoised images produced by existing methods. Here, we propose Noise2Inverse, a deep CNN-based denoising method for linear image reconstruction algorithms that does not require any additional clean or noisy data. Training a CNN-based denoiser is enabled by exploiting the noise model to compute multiple statistically independent reconstructions. We develop a theoretical framework which shows that such training indeed obtains a denoising CNN, assuming the measured noise is element-wise independent and zero-mean. On simulated CT datasets, Noise2Inverse demonstrates an improvement in peak signal-to-noise ratio and structural similarity index compared to state-of-the-art image denoising methods and conventional reconstruction methods, such as Total-Variation Minimization. We also demonstrate that the method is able to significantly reduce noise in challenging real-world experimental datasets.Comment: This paper appears in: IEEE Transactions on Computational Imaging On page(s): 1320-1335 Print ISSN: 2333-9403 Online ISSN: 2333-9403 Digital Object Identifier: 10.1109/TCI.2020.301964

    Deep learning based classification of dynamic processes in time-resolved X-ray tomographic microscopy

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    Time-resolved X-ray tomographic microscopy is an invaluable technique to investigate dynamic processes in 3D for extended time periods. Because of the limited signal-to-noise ratio caused by the short exposure times and sparse angular sampling frequency, obtaining quantitative information through post-processing remains challenging and requires intensive manual labor. This severely limits the accessible experimental parameter space and so, prevents fully exploiting the capabilities of the dedicated time-resolved X-ray tomographic stations. Though automatic approaches, often exploiting iterative reconstruction methods, are currently being developed, the required computational costs typically remain high. Here, we propose a highly efficient reconstruction and classification pipeline (SIRT-FBP-MS-D-DIFF) that combines an algebraic filter approximation and machine learning to significantly reduce the computational time. The dynamic features are reconstructed by standard filtered back-projection with an algebraic filter to approximate iterative reconstruction quality in a computationally efficient manner. The raw reconstructions are post-processed with a trained convolutional neural network to extract the dynamic features from the low signal-to-noise ratio reconstructions in a fully automatic manner. The capabilities of the proposed pipeline are demonstrated on three different dynamic fuel cell datasets, one exploited for training and two for testing without network retraining. The proposed approach enables automatic processing of several hundreds of datasets in a single day on a single GPU node readily available at most institutions, so extending the possibilities in future dynamic X-ray tomographic investigations
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