Reconstruction Artifacts Research Articles

Single-Slice XRF Mapping of Light Elements in Frozen-Hydrated Allium schoenoprasum via a Self-Absorption-Corrected Hyperspectral Tomographic Reconstruction Approach.

3D and 2D-cross-sectional X-ray fluorescence analysis of biological material is a powerful tool to image the distribution of elements and to understand and quantify metal homeostasis and the distribution of anthropogenic metals and nanoparticles with minimal preparation artifacts. Using tomograms recorded on cryogenically prepared leaves of Allium schoenoprasum, the cross-sectional distribution of physiologically relevant elements like calcium, potassium, manganese, and zinc could be tomographically reconstructed by peak fitting followed by a conventional maximum-likelihood algorithm with self-absorption correction to reveal the quantitative cross-sectional element distribution. If light elements such as S and P are located deep in the sample compared to the escape depth of their characteristic X-ray fluorescence lines, the quantitative reconstruction becomes inaccurate. As a consequence, noise is amplified to a magnitude where it might be misinterpreted as actual concentration. We show that a tomographic MCA hyperspectral reconstruction in combination with a self-absorption correction allows for fitting of the XRF spectra directly in real space, which significantly improves the qualitative and quantitative analysis of the light elements compared to the conventional method as noise and artifacts in the tomographic reconstruction are reduced. This reconstruction approach can substantially improve the quantitative analysis of trace elements as it allows the fitting of summed voxel spectra in anatomical regions of interest. The presented method can be applied to XRF 2D single-slice tomography data and 3D tomograms and is particularly relevant for, but not limited to, biological material in order to help retrieve self-absorption corrected quantitative reconstructions of the spatial distribution of light elements and ultra-trace-elements.

Quantitative structured illumination microscopy via a physical model-based background filtering algorithm reveals actin dynamics

Despite the prevalence of superresolution (SR) microscopy, quantitative live-cell SR imaging that maintains the completeness of delicate structures and the linearity of fluorescence signals remains an uncharted territory. Structured illumination microscopy (SIM) is the ideal tool for live-cell SR imaging. However, it suffers from an out-of-focus background that leads to reconstruction artifacts. Previous post hoc background suppression methods are prone to human bias, fail at densely labeled structures, and are nonlinear. Here, we propose a physical model-based Background Filtering method for living cell SR imaging combined with the 2D-SIM reconstruction procedure (BF-SIM). BF-SIM helps preserve intricate and weak structures down to sub-70 nm resolution while maintaining signal linearity, which allows for the discovery of dynamic actin structures that, to the best of our knowledge, have not been previously monitored.

Multislice Electron Tomography Using Four-Dimensional Scanning Transmission Electron Microscopy

Electron tomography offers useful three-dimensional (3D) structural information, which cannot be observed by two-dimensional imaging. By combining annular dark-field scanning transmission electron microscopy (ADF STEM) with aberration correction, the resolution of electron tomography has reached atomic resolution. However, tomography based on ADF STEM inherently suffers from several issues, including a high electron-dose requirement, poor contrast for light elements, and artifacts from image-contrast nonlinearity. Here, we develop an alternative method called multislice electron tomography (MSET) based on four-dimensional STEM tilt series. Our simulations show that multislice-based 3D reconstruction can effectively reduce undesirable reconstruction artifacts from the nonlinear contrast, allowing precise determination of atomic structures with improved sensitivity for low-Z elements, at considerably low electron-dose conditions. We expect that the MSET method can be applied to a wide variety of materials, including radiation-sensitive samples and materials containing light elements whose 3D atomic structures have never been fully elucidated due to electron-dose limitations or nonlinear imaging contrast.

Streak artifact suppressed back projection for sparse-view photoacoustic computed tomography.

The development of fast and accurate image reconstruction algorithms under constrained data acquisition conditions is important for photoacoustic computed tomography (PACT). Sparse-view measurements have been used to accelerate data acquisition and reduce system complexity; however, reconstructed images suffer from sparsity-induced streak artifacts. In this paper, a modified back-projection (BP) method termed anti-streak BP is proposed to suppress streak artifacts in sparse-view PACT reconstruction. During the reconstruction process, the anti-streak BP finds the back-projection terms contaminated by high-intensity sources with an outlier detection method. Then, the weights of the contaminated back-projection terms are adaptively adjusted to eliminate the effects of high-intensity sources. The proposed anti-streak BP method is compared with the conventional BP method on both simulation and in vivo data. The anti-streak BP method shows substantially fewer artifacts in the reconstructed images, and the streak index is 54% and 20% lower than that of the conventional BP method on simulation and in vivo data, when the transducer number N=128. The anti-streak BP method is a powerful improvement of the BP method with the ability of artifact suppression.

Learning from Projection to Reconstruction: A Deep Learning Reconstruction Framework for Sparse-View Phase Contrast Computed Tomography via Dual-Domain Enhancement

Phase contrast computed tomography (PCCT) provides an effective non-destructive testing tool for weak absorption objects. Limited by the phase stepping principle and radiation dose requirement, sparse-view sampling is usually performed in PCCT, introducing severe artifacts in reconstruction. In this paper, we report a dual-domain (i.e., the projection sinogram domain and image domain) enhancement framework based on deep learning (DL) for PCCT with sparse-view projections. It consists of two convolutional neural networks (CNN) in dual domains and the phase contrast Radon inversion layer (PCRIL) to connect them. PCRIL can achieve PCCT reconstruction, and it allows the gradients to backpropagate from the image domain to the projection sinogram domain while training. Therefore, parameters of CNNs in dual domains are updated simultaneously. It could overcome the limitations that the enhancement in the image domain causes blurred images and the enhancement in the projection sinogram domain introduces unpredictable artifacts. Considering the grating-based PCCT as an example, the proposed framework is validated and demonstrated with experiments of the simulated datasets and experimental datasets. This work can generate high-quality PCCT images with given incomplete projections and has the potential to push the applications of PCCT techniques in the field of composite imaging and biomedical imaging.

Effect of a Patient-Specific Structural Prior Mask on Electrical Impedance Tomography Image Reconstructions.

Electrical Impedance Tomography (EIT) is a low-cost imaging method which reconstructs two-dimensional cross-sectional images, visualising the impedance change within the thorax. However, the reconstruction of an EIT image is an ill-posed inverse problem. In addition, blurring, anatomical alignment, and reconstruction artefacts can hinder the interpretation of EIT images. In this contribution, we introduce a patient-specific structural prior mask into the EIT reconstruction process, with the aim of improving image interpretability. Such a prior mask ensures that only conductivity changes within the lung regions are reconstructed. To evaluate the influence of the introduced structural prior mask, we conducted numerical simulations with two scopes in terms of their different ventilation statuses and varying atelectasis scales. Quantitative analysis, including the reconstruction error and figures of merit, was applied in the evaluation procedure. The results show that the morphological structures of the lungs introduced by the mask are preserved in the EIT reconstructions and the reconstruction artefacts are decreased, reducing the reconstruction error by 25.9% and 17.7%, respectively, in the two EIT algorithms included in this contribution. The use of the structural prior mask conclusively improves the interpretability of the EIT images, which could facilitate better diagnosis and decision-making in clinical settings.

Structured illumination-based super-resolution live-cell quantitative FRET imaging

Förster resonance energy transfer (FRET) microscopy provides unique insight into the functionality of biological systems via imaging the spatiotemporal interactions and functional state of proteins. Distinguishing FRET signals from sub-diffraction regions requires super-resolution (SR) FRET imaging, yet is challenging to achieve from living cells. Here, we present an SR FRET method named SIM-FRET that combines SR structured illumination microscopy (SIM) imaging and acceptor sensitized emission FRET imaging for live-cell quantitative SR FRET imaging. Leveraging the robust co-localization prior of donor and accepter during FRET, we devised a mask filtering approach to mitigate the impact of SIM reconstruction artifacts on quantitative FRET analysis. Compared to wide-field FRET imaging, SIM-FRET provides nearly twofold spatial resolution enhancement of FRET imaging at sub-second timescales and maintains the advantages of quantitative FRET analysis in vivo. We validate the resolution enhancement and quantitative analysis fidelity of SIM-FRET signals in both simulated FRET models and live-cell FRET-standard construct samples. Our method reveals the intricate structure of FRET signals, which are commonly distorted in conventional wide-field FRET imaging.

Accurate evaluation of self-heterodyne laser linewidth measurements using Wiener filters.

Self-heterodyne beat note measurements are widely used for the experimental characterization of the frequency noise power spectral density (FN-PSD) and the spectral linewidth of lasers. The measured data, however, must be corrected for the transfer function of the experimental setup in a post-processing routine. The standard approach disregards the detector noise and thereby induces reconstruction artifacts in the reconstructed FN-PSD. We introduce an improved post-processing routine based on a parametric Wiener filter that is free from reconstruction artifacts, provided a good estimate of the signal-to-noise ratio is supplied. Building on this potentially exact reconstruction, we develop a new method for intrinsic laser linewidth estimation that is aimed at deliberate suppression of unphysical reconstruction artifacts. Our method yields excellent results even in the presence of strong detector noise, where the intrinsic linewidth plateau is not even visible using the standard method. The approach is demonstrated for simulated time series from a stochastic laser model including 1/f-type noise.

A thresholding based iterative reconstruction method for limited-angle tomography data

Limited-angle computed tomography is often imposed by in-situ experiments combining tomography with sample environments. The missing projection data causes artifacts in the tomographic reconstruction. We demonstrate that the correction of these numerical artifacts can be achieved by restoring the missing projections using an iterative reconstruction scheme. The reconstruction is regularized using segmentation, and thresholds determined from the histogram of reconstructed gray levels. The missing projections are simulated by forward projection and incorporated into the original measured dataset to give a complete angular span. This scheme typically converges within a few iterations. Results are presented for several measurements using parallel-beam synchrotron X-ray tomography and 165 degrees of valid projection data. A simple numerical simulation is used to verify the validity of the experimental results.

Seeing the whole picture: Methods for getting the most from micro X-ray computed tomography of TRISO nuclear fuel particles

Tristructural isotropic (TRISO) coated fuel particles are a nuclear fuel form under extensive study for use in advanced nuclear reactor concepts. TRISO fuels are subjected to high temperature neutron irradiations and then examined to assess their performance by determining fission product retention and studying morphological changes. Micro X-ray computed tomography is one method of nondestructively studying the effects of TRISO performance. This work addresses the need for image processing to remove X-ray tomographic reconstruction artifacts that prevent the study of TRISO features, as the TRISO particles’ high Z kernel can introduce metal artifacts that degrade the image quality in the surrounding low Z coating layers. These metal artifacts were reduced by imaging the TRISO particles with both high- and low-energy X-rays and applying a mask to the radiographs obtained with low-energy X-rays to digitally remove the dense fuel kernel region. These masked radiographs were then used to produce a tomographic reconstruction which was combined with the tomographic reconstruction of the high-energy data. This enabled the relatively-low-density TRISO buffer layer to be examined in more detail, providing information on irradiation induced dimensional changes of the coatings. This methodology, which helps see the full picture of a TRISO particle, is not limited to nuclear fuels but can be applied to systems that contain highly attenuating material surrounded by less dense materials.

Rapid, artifact-reduced, image reconstruction for super-resolution structured illumination microscopy

Super-resolution structured illumination microscopy (SR-SIM) is finding increasing application in biomedical research due to its superior ability to visualize subcellular dynamics in living cells. However, during image reconstruction artifacts can be introduced and when coupled with time-consuming postprocessing procedures, limits this technique from becoming a routine imaging tool for biologists. To address these issues, an accelerated, artifact-reduced reconstruction algorithm termed joint space frequency reconstruction-based artifact reduction algorithm (JSFR-AR-SIM) was developed by integrating a high-speed reconstruction framework with a high-fidelity optimization approach designed tosuppress the sidelobe artifact. Consequently, JSFR-AR-SIM produces high-quality, super-resolution images with minimal artifacts, and thereconstruction speed is increased. We anticipate this algorithm tofacilitate SR-SIM becoming a routine tool in biomedical laboratories.

Virtual-scanning light-field microscopy for robust snapshot high-resolution volumetric imaging

High-speed three-dimensional (3D) intravital imaging in animals is useful for studying transient subcellular interactions and functions in health and disease. Light-field microscopy (LFM) provides a computational solution for snapshot 3D imaging with low phototoxicity but is restricted by low resolution and reconstruction artifacts induced by optical aberrations, motion and noise. Here, we propose virtual-scanning LFM (VsLFM), a physics-based deep learning framework to increase the resolution of LFM up to the diffraction limit within a snapshot. By constructing a 40 GB high-resolution scanning LFM dataset across different species, we exploit physical priors between phase-correlated angular views to address the frequency aliasing problem. This enables us to bypass hardware scanning and associated motion artifacts. Here, we show that VsLFM achieves ultrafast 3D imaging of diverse processes such as the beating heart in embryonic zebrafish, voltage activity in Drosophila brains and neutrophil migration in the mouse liver at up to 500 volumes per second.

Understanding aliasing effects and their removal in SPEN MRI: A k-space perspective.

To characterize the mechanism of formation and the removal of aliasing artifacts and edge ghosts in spatiotemporally encoded (SPEN) MRI within a k-space theoretical framework. SPEN's quadratic phase modulation can be described in k-space by a convolution matrix whose coefficients derive from Fourier relations. This k-space model allows us to pose SPEN's reconstruction as a deconvolution process from which aliasing and edge ghost artifacts can be quantified by estimating the difference between a full sampling and reconstructions resulting from undersampled SPEN data. Aliasing artifacts in SPEN MRI reconstructions can be traced to image contributions corresponding to high-frequency k-space signals. The k-space picture provides the spatial displacements, phase offsets, and linear amplitude modulations associated to these artifacts, as well as routes to removing these from the reconstruction results. These new ways to estimate the artifact priors were applied to reduce SPEN reconstruction artifacts on simulated, phantom, and human brain MRI data. A k-space description of SPEN's reconstruction helps to better understand the signal characteristics of this MRI technique, and to improve the quality of its resulting images.

Compact, Hybrid Light-Sheet and Fourier Light-Field Microscopy with a Single Objective for High-Speed Volumetric Imaging In Vivo.

Volumetric imaging of biodynamics at high spatiotemporal resolutions in vivo is vital in biomedical studies, in which Fourier light field microscopy (FLFM) is a promising technique. However, the commonly used wide-field illumination strategy in FLFM introduces intense out of depth-of-field background, which not only degrades the image quality, but also introduces reconstruction artifacts. Employing light sheet illumination is an effective way to alleviate the background and reduce photobleaching in light-field microscopy. Unfortunately, the introduction of light-sheet illumination often requires an extra objective and precise alignment, which increases the system complexity. Here, we propose the compact, hybrid light-sheet and FLFM (CLS-FLFM), which uses only a single objective to achieve both light-sheet illumination and Fourier light-field imaging simultaneously. With a micromirror under the objective, we focus the light sheet, which ensures selective-volume-illumination, on the imaging plane of the FLFM to perform volumetric imaging. We demonstrate the superior performance of CLS-FLFM in inhibiting background in both structural and dynamical imaging of larval zebrafish in vivo. We envision that CLS-FLFM finds wide applications in high-speed, background-inhibited volumetric imaging of biodynamics in vivo.

Global total focusing method through digital twin and robotic automation for ultrasonic phased array inspection of complex components

This paper tackles the challenging ultrasonic inspection of parts with complex geometries by proposing a novel global TFM (gTFM) approach. In this method, a digital twin is used to optimize and control the inspection, which consists of the probe being scanned around a complex part using a robotic arm, finally rendering a gTFM image by combining all acquisitions. To verify its validity, an aerospace disk forging mock-up, with side-drilled holes (SDH), was used. The mock-up geometry comprised multiple concave and convex surfaces. A comparison of different scan plans showed that adapting the probe position to the surface profile improved the imaging performance. Furthermore, by using a typical constant probe standoff, strong reconstruction artifacts were generated, inevitably rendering the inspection unreliable. On the other hand, by using an optimal scan plan, the gTFM image showed a sharp image of the SDHs. Compared to the other strategies, the optimal scan plan achieved a 40% increase in the mean contrast-to-noise ratio (CNR), a 70% reduction in the position error (only 0.1 mm), and a reduction of 33% in the array performance indicator (API). Inspection coverage was achieved efficiently, depicting the complete cross-section of the specimen using only 7 probe positions.

Deep learning for improving PET/CT attenuation correction by elastic registration of anatomical data.

For PET/CT, the CT transmission data are used to correct the PET emission data for attenuation. However, subject motion between the consecutive scans can cause problems for the PET reconstruction. A method to match the CT to the PET would reduce resulting artifacts in the reconstructed images. This work presents a deep learning technique for inter-modality, elastic registration of PET/CT images for improving PET attenuation correction (AC). The feasibility of the technique is demonstrated for two applications: general whole-body (WB) imaging and cardiac myocardial perfusion imaging (MPI), with a specific focus on respiratory and gross voluntary motion. A convolutional neural network (CNN) was developed and trained for the registration task, comprising two distinct modules: a feature extractor and a displacement vector field (DVF) regressor. It took as input a non-attenuation-corrected PET/CT image pair and returned the relative DVF between them-it was trained in a supervised fashion using simulated inter-image motion. The 3D motion fields produced by the network were used to resample the CT image volumes, elastically warping them to spatially match the corresponding PET distributions. Performance of the algorithm was evaluated in different independent sets of WB clinical subject data: for recovering deliberate misregistrations imposed in motion-free PET/CT pairs and for improving reconstruction artifacts in cases with actual subject motion. The efficacy of this technique is also demonstrated for improving PET AC in cardiac MPI applications. A single registration network was found to be capable of handling a variety of PET tracers. It demonstrated state-of-the-art performance in the PET/CT registration task and was able to significantly reduce the effects of simulated motion imposed in motion-free, clinical data. Registering the CT to the PET distribution was also found to reduce various types of AC artifacts in the reconstructed PET images of subjects with actual motion. In particular, liver uniformity was improved in the subjects with significant observable respiratory motion. For MPI, the proposed approach yielded advantages for correcting artifacts in myocardial activity quantification and potentially for reducing the rate of the associated diagnostic errors. This study demonstrated the feasibility of using deep learning for registering the anatomical image to improve AC in clinical PET/CT reconstruction. Most notably, this improved common respiratory artifacts occurring near the lung/liver border, misalignment artifacts due to gross voluntary motion, and quantification errors in cardiac PET imaging.

Low-rank flat-field correction for artifact reduction in spectral computed tomography

Spectral computed tomography has received considerable interest in recent years since spectral measurements contain much richer information about the object of interest. In spectral computed tomography, we are interested in the energy channel-wise reconstructions of the object. However, such reconstructions suffer from a low signal-to-noise ratio and share the challenges of conventional low-dose computed tomography such as ring artifacts. Ring artifacts arise from errors in the flat fields and can significantly degrade the quality of the reconstruction. We propose an extended flat-field model that exploits high correlation in the spectral flat fields to reduce ring artifacts in channel-wise reconstructions. The extended model relies on the assumption that the spectral flat fields can be well-approximated by a low-rank matrix. Our proposed model works directly on the spectral flat fields and can be combined with any existing reconstruction model, e.g. filtered back projection and iterative methods. The proposed model is validated on a neutron data set. The results show that our method successfully diminishes ring artifacts and improves the quality of the reconstructions. Moreover, the results indicate that our method is robust; it only needs a single spectral flat-field image, whereas existing methods need multiple spectral flat-field images to reach a similar level of ring reduction.

4DCT is long overdue for improvement.

4DCT is long overdue for improvement.

A machine learning supported sinogram interpolation method for X-ray computed tomography

The time of an X-ray Computed Tomography (XCT) measurement is directly affected by the number of acquired X-ray projections. In general, a large number of X-ray projections, hence a large acquisition time is required to obtain a qualitative reconstruction and successive feature analysis. To expand the applicability of XCT towards low-end parts we propose a novel sinogram interpolation method that incorporates the object rotation. The method combines a forward projection model of the XCT system and a deep learning regression model to generate intermediate X-ray projections for XCT scans with a reduced number of projections. Thereby, the accompanying reconstruction artefacts can be reduced while preserving a lower acquisition time. The method is validated on simulated projections of the 3D Shepp-Logan phantom and simulated projections of real 3D printed components using the ASTRA-toolbox. Within the reconstructions, less streaking artefacts are observed and an increased contrast between different features is obtained without introducing rotational artefacts.

Nonlinear Beamforming Based on Amplitude Coherence Applied to Ultrasonic Imaging of Coarse-Grained Steels

Abstract This paper deals with ultrasonic imaging in a nondestructive evaluation (NDE) context. In particular, we are focused on the inspection of coarse-grained steels having a heterogeneous composition that creates structural noise in the ultrasonic signals and images. The standard way to beamform the acquired ultrasonic data is by delay-and-sum (DAS). This method is fast but suffers from low signal-to-noise ratio (SNR) for coarse-grained steel inspection. In this paper, we propose to adapt a coherence-based beamformer called pDAS from the medical imaging community. pDAS beamforming is based on DAS structure but includes p-root and p-power before and after summations, respectively. It results in an enhancement of the coherent summation of signals that improves both resolution and contrast. Coherence-based beamformers are known to enhance information whose acoustic response correlates with geometrical information, that is why they decrease grating lobes and side lobes, specular echoes, reconstruction artifacts, and noise due to multiple scattering. In this paper, the pDAS beamformer is proposed for two common acquisition schemes employed in NDE that are plane wave imaging (PWI) and full matrix capture (FMC). The beamformers have been efficiently implemented for parallel computing on graphics processing unit (GPU) in a context of real-time imaging and fast part scanning in NDE. First, experimental results are presented from an austenitic-ferritic sample from the power generation industry that contains side drilled holes (SDH) with diameter 0.4 mm at several depths. pDAS (for p from two to three) shows improvements in terms of SNR and resolution compared to standard DAS, both in PWI and FMC modalities. We also show that the computation cost of pDAS is equivalent to DAS. A real application on a sample containing a fatigue crack connected to the backwall is exposed. We show that pDAS beamformer can enhance crack response compared to grains, but it also decreases unwanted information such as backwall specular echoes and reconstruction artifacts.