Resolution Of Reconstructed Images Research Articles

Illumination diversity in multiwavelength extreme ultraviolet ptychography

With the development of high harmonic generation (HHG), lensless extreme-ultraviolet (XUV) imaging at nanoscale resolution has become possible with table-top systems. Specifically, ptychographic phase retrieval using monochromatic XUV illumination exhibits extraordinary robustness and accuracy to computationally reconstruct the object and the illumination beam profile. In ptychography, using structured illumination has been shown to improve reconstruction robustness and image resolution by enhancing high spatial-frequency diffraction. However, broadband imaging has remained challenging, as the required multiwavelength algorithms become increasingly demanding. One major aspect is the ability to separate the available information into different physically meaningful states, such as different spectral components. Here, we show that introducing spatial diversity between spectral components of an HHG beam can significantly improve the reconstruction quality in multiwavelength XUV ptychography. We quantify the diversity in the polychromatic illumination by analyzing the diffraction patterns using established geometry- and information-theory-based dissimilarity metrics. We experimentally verify the major influence of diversity by comparing ptychography measurements using HHG beams with Gaussian and binary structured profiles as well as with beams carrying wavelength-dependent orbital angular momentum. Our results demonstrate how structured illumination acts in twofold by separating the spectral information in a single diffraction pattern while providing maximized added information with every new scan position. We anticipate our work to be a starting point for high-fidelity polychromatic imaging of next-generation nanostructured devices at XUV and soft-X-ray wavelengths.

Application of Bivariate Reproducing Kernel-Based Best Interpolation Method in Electrical Tomography.

Electrical Tomography (ET) technology is widely used in multiphase flow detection due to its advantages of low cost, visualization, fast response, non-radiation, and non-invasiveness. However, ill-posed solutions lead to low image reconstruction resolution, which limits its practical engineering applications. Although existing interpolation approximation algorithms can alleviate the effects of the ill-posed solutions to some extent, the imaging results remain suboptimal due to the limited approximation capability of these methods. This paper proposes a Bivariate Reproducing Kernel-Based Best Interpolation (BRKBI) method, which offers smaller approximation errors and clearer image reconstruction quality compared to existing methods. The effectiveness of the BRKBI method is validated through theoretical analysis and experimental comparisons.

Piezoresistive nanocomposite sensing using electrical impedance tomography and machine learning

Strain measurements have traditionally been accomplished by connecting many gauges to crucial spots on structures. Installing strain gauges can be costly and limited in providing ‎comprehensive data for large surface areas or complex structures, posing significant challenges. This work explores the use of piezoresistive nanocomposite sensors as a cost-effective and informative solution for wide surface area strain detection. Piezoresistive sensors are created using two methodologies: film-coated and pellet-fed 3D printing technique (PF3DP), with variable gauge factors achieved by using 6 and 10 wt% multi-wall carbon nanotubes (MWCNTs), respectively. An electrical impedance tomography (EIT) setup is utilized to analyze electrical changes with respect to strain and predict the conductivity distribution across the sensor’s surface. This study employs two efficient machine learning (ML) techniques, artificial neural networks (ANN) and convolutional neural networks (CNN), to analyze and compare image reconstruction resolution at different loads. The machine learning models are designed to reconstruct images quickly and reliably. The study demonstrates that the hybrid-ML model, which combines ANN and CNN models, achieves high-resolution image construction with minimal errors compared to outcomes obtained through EIT for the experimental measurements. Sensors created using 3D printing provide higher image resolution and data stability than film-coated sensors. This technology has the potential for accurate strain sensing and measuring the structural health of complex surfaces.

An RPC-PET brain scanner demonstrator: First results

We present first results from a Positron Emission Tomography (PET) scanner demonstrator based on Resistive Plate Chambers and specialized for brain imaging. The device features a 30 cm wide cubic field-of-view and each detector comprises 40 gas gaps with 3D location of the interaction point of the photon. Besides other imagery, we show that the reconstructed image resolution, as evaluated by a hot-rod phantom, is sub-millimetric, which is beyond the state-of-the-art of the standard PET technology for this application.

E2SRI: Learning to Super-Resolve Intensity Images From Events

An event camera reports per-pixel intensity differences as an asynchronous stream of events with low latency, high dynamic range (HDR), and low power consumption. This stream of sparse/dense events limits the direct use of well-known computer vision applications for event cameras. Further applications of event streams to vision tasks that are sensitive to image quality issues, such as spatial resolution and blur, e.g., object detection, would benefit from a higher resolution of image reconstruction. Moreover, despite the recent advances in spatial resolution in event camera hardware, the majority of commercially available event cameras still have relatively low spatial resolutions when compared to conventional cameras. We propose an end-to-end recurrent network to reconstruct high-resolution, HDR, and temporally consistent grayscale or color frames directly from the event stream, and extend it to generate temporally consistent videos. We evaluate our algorithm on real-world and simulated sequences and verify that it reconstructs fine details of the scene, outperforming previous methods in quantitative quality measures. We further investigate how to (1) incorporate active pixel sensor frames (produced by an event camera) and events together in a complementary setting and (2) reconstruct images iteratively to create an even higher quality and resolution in the images.

Rock CT Image Super-Resolution Using Residual Dual-Channel Attention Generative Adversarial Network

Because of its benefits in terms of high speed, non-destructiveness, and three-dimensionality, as well as ease of integration with computer simulation, computed tomography (CT) technology is widely applied in reservoir geology research. However, rock imaging is restricted by the device used as there is not a win–win for both the image receptive field and corresponding resolution. Convolutional neural network-based super-resolution reconstruction has become a hot topic in improving the performance of CT images. With the help of a convolution kernel, it can effectively extract characteristics and ignore disturbance information. The dismal truth is that convolutional neural networks still have numerous issues, particularly unclear texture details. To address these challenges, a generative adversarial network (RDCA-SRGAN) was designed to improve rock CT image resolution using the combination of residual learning and a dual-channel attention mechanism. Specifically, our generator employs residual attention to extract additional features; similarly, the discriminator builds on dual-channel attention and residual learning to distinguish generated contextual information and decrease computational consumption. Quantitative and qualitative analyses demonstrate that the proposed model is superior to earlier advanced frameworks and is capable to constructure visually indistinguishable high-frequency details. The quantitative analysis shows our model contributes the highest value of structural similarity, enriching the more detailed texture information. From the qualitative analysis, in enlarged details of the reconstructed images, the edges of the images generated by the RDCA-SRGAN can be shown to be clearer and sharper. Our model not only performs well in subtle coal cracks but also enriches more dissolved carbonate and carbon minerals. The RDCA-SRGAN has substantially enhanced the reconstructed image resolution and our model has great potential to be used in geomorphological study and exploration.

Real-time physical compression computational ghost imaging based on array spatial light field modulation and deep learning

Ghost imaging has great application potential in remote sensing observation and biomedical due to the use of a single pixel detector for imaging, which makes it easy to imaging in low light conditions and imperfect spectral regions of the camera. However, the higher the reconstructed image resolution (the more pixels), the larger the number of measurements are required, which makes it difficult to realize the real-time imaging required in the application. Therefore, a new real-time computational ghost imaging method based on deep learning technology and array spatial light field modulation is proposed, which can realize high-quality imaging with high magnification down sampling. Furthermore, the physical compression imaging of objects is realized by adjusting the array light field composed of several identical sub-light fields, and the original image of the object is successfully obtained from the compressed image by deep learning. The resulting compressed image is realized by the physical means of array light field regulation, and allows the far-field transmission of physical compressed image (or use the image compression algorithm for deep compression of the physical compressed image) with low storage data to reduce the transmission data, and then use deep learning to obtain the original image. We implement the proposed method in numerical simulation and real-time imaging experiments of multifold down sampling and physical compressed (eg, 4x, 9x, 16x), and demonstrate its validity and extensibility. This makes the method have a great potential application and prospect in the application scenarios of earth observation imaging and Limited space-time transmission window.

Hybrid Single Image Super-Resolution Algorithm for Medical Images

High-quality medical microscopic images used for diseases detection are expensive and difficult to store. Therefore, low-resolution images are favorable due to their low storage space and ease of sharing, where the images can be enlarged when needed using Super-Resolution (SR) techniques. However, it is important to maintain the shape and size of the medical images while enlarging them. One of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image resolution. Consequently, this paper suggests a multi-SR and classification framework based on Generative Adversarial Network (GAN) to generate high-resolution images with higher quality and finer details to reduce blurring. The proposed framework comprises five GAN models: Enhanced SR Generative Adversarial Networks (ESRGAN), Enhanced deep SR GAN (EDSRGAN), Sub-Pixel-GAN, SRGAN, and Efficient Wider Activation-B GAN (WDSR-b-GAN). To train the proposed models, we have employed images from the famous BreakHis dataset and enlarged them by 4× and 16× upscale factors with the ground truth of the size of 256 × 256 × 3. Moreover, several evaluation metrics like Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), Multiscale Structural Similarity Index (MS-SSIM), and histogram are applied to make comprehensive and objective comparisons to determine the best methods in terms of efficiency, training time, and storage space. The obtained results reveal the superiority of the proposed models over traditional and benchmark models in terms of color and texture restoration and detection by achieving an accuracy of 99.7433%.

Optical image compression and encryption transmission-based ondeep learning and ghost imaging

This paper proposes an optical image compression and encryption transmission based on deep learning and ghost imaging, which uses ghost imaging as a point-to-face transmission mode to reduce the influence of chaotic medium and turbulence on the communication channel, and combine with the deep learning to improve the reconstructed image resolution. In this method, first, the image is preprocessed by JPEG to obtain a compressed image, and the compressed image is transmitted as an image to be transmitted, which can improve the transmission rate; then, the ghost imaging is used for transmission, which can improve the anti-interference ability and transmission security of the transmission; finally, the problem of poor image quality after ghost-imaging transmission, using deep learning for reconstruction, which can improve the resolution of the image. In the simulation experiment, feasibility analysis, safety analysis, and robustness analysis were carried out, respectively, and the experimental results were objectively evaluated using correlation methods such as peak signal-to-noise ratio, mutual information, and histogram. The experimental results show that the proposed method can provide a feasible and secure optical communication image transmission method and realize high-resolution transmission image reconstruction, which has guiding significance for optical communication.

Effect of source blur on digital breast tomosynthesis reconstruction.

Most digital breast tomosynthesis (DBT) reconstruction methods neglect the blurring of the projection views caused by the finite size or motion of the x-ray focal spot. This paper studies the effect of source blur on the spatial resolution of reconstructed DBT using analytical calculation and simulation, and compares the influence of source blur over a range of blurred source sizes. Mathematically derived formulas describe the point spread function (PSF) of source blur on the detector plane as a function of the spatial locations of the finite-sized source and the object. By using the available technical parameters of some clinical DBT systems, we estimated the effective source sizes over a range of exposure time and DBT scan geometries. We used the CatSim simulation tool (GE Global Research, NY) to generate digital phantoms containing line pairs and beads at different locations and imaged with sources of four different sizes covering the range of potential source blur. By analyzing the relative contrasts of the test objects in the reconstructed images, we studied the effect of the source blur on the spatial resolution of DBT. Furthermore, we simulated a detector that rotated in synchrony with the source about the rotation center and calculated the spatial distribution of the blurring distance in the imaged volume to estimate its influence on source blur. Calculations demonstrate that the PSF is highly shift-variant, making it challenging to accurately implement during reconstruction. The results of the simulated phantoms demonstrated that a typical finite-sized focal spot (~0.3mm) will not affect the reconstructed image resolution if the x-ray tube is stationary during data acquisition. If the x-ray tube moves during exposure, the extra blur due to the source motion may degrade image resolution, depending on the effective size of the source along the direction of the motion. A detector that rotates in synchrony with the source does not reduce the influence of source blur substantially. This study demonstrates that the extra source blur due to the motion of the x-ray tube during image acquisition substantially degrades the reconstructed image resolution. This effect cannot be alleviated by rotating the detector in synchrony with the source. The simulation results suggest that there are potential benefits of modeling the source blur in image reconstruction for DBT systems using continuous-motion acquisition mode.

A simulation study of a fan-beam time-of-flight fast-neutron tomography system

A fast neutron imaging system, consisting of a deuterium-tritium neutron generator, an associated alpha-particle detector and an arc-shaped array of individual neutron detectors is simulated for fan-beam fast neutron tomography purpose. In this system, the associated particle and time of flight techniques are used to separate transmission neutrons from scattered neutrons by electronically collimating and time tagging the source neutrons. To evaluate factors affecting the system’s spatial resolution and image quality, a series of simulations were performed with a GEANT4 based Monte Carlo program. The parameters include neutron detector resolution, system coincidence resolving time and reconstruction algorithm. The simulation results indicate that a reconstructed image resolution of about 2.1 mm can be obtained with an array of two hundred 4 mm resolution neutron detectors. With the aid of electronic collimation and time tagged based event filtering, the reconstructed image quality was significantly improved. Among iterative reconstruction algorithms tested, the Simultaneous Algebraic Reconstruction Technique-Total Variation algorithm showed better overall performance than others.

Choquet Integral-Based Fusion of Multiple Patterns for Improving EIT Spatial Resolution

Electrical impedance tomography (EIT) is a promising visualization technology with advantages of low cost, nonradiation, and rapid response. The most used EIT pattern leads to relatively limited measurements by which the EIT spatial resolution is low. However, multiple EIT patterns can generate more measurements, thus fusing multiple EIT patterns is helpful to improve the EIT spatial resolution. But the most challenging task of fusing multiple EIT patterns is how to correctly reflect the interaction among them. In this paper, eight typical EIT patterns are employed to obtain more measurements, and Choquet integral method is used to represent the interaction among EIT patterns. Consequently, the optimal combination of measurements from eight EIT patterns is selected to reconstruct the EIT image. Simulations and experiments were carried out and the results indicated that the proposed method can effectively improve the spatial resolution of reconstruction images.

First imaging results of a bubble-assisted Liquid Hole Multiplier with SiPM readout in liquid xenon

First imaging results in liquid xenon of a Liquid Hole Multiplier (LHM) coupled to a quad-Silicon Photomultiplier (SiPM) array are presented. Ionization electrons deposited in the noble liquid by 5.5 MeV alpha particles, are collected into the holes of a Thick Gas Electron Multiplier (THGEM) electrode having a xenon gas bubble trapped underneath. They drift through the liquid-gas interface, inducing electroluminescence within the bubble. The resulting photons are detected with a Hamamatsu VUV4 quad-SiPM array—providing the deposited energy with a charge-only RMS resolution of 6.6%. The image reconstruction resolution was estimated to be ∼200 μm (RMS).

Compressive holography with resolution improvement and lensless adjustable magnification

In lensless digital holography the resolving power is limited by factors such as the finite size of recorded area, pixel dimensions and sampling rate of digital acquisition system. These factors acts as a filter that discards high-frequency components with useful information about the analyzed object. In the present work, we propose a method to overcome these limitations to proving the resolution recovery in digital in-line holography by utilizing adjustable magnification in a compressive sensing framework. Here, we model the sensed field as low-pass filtered and projected on a Fresnel base with adjustable magnification and use sparse regularization in an inverse problem approach to image reconstruction. Our method enhances image reconstruction resolution from a single recorded hologram with magnification independent of parameters such as propagation distance and wavelength of light, which enables to be used in applications like multi-wavelength digital holography. In addition, this technique offers high noise immunity and eliminates typical problems of holographically reconstructed images such as twin image and speckle. Therefore, we demonstrate by simulations and experimentally results that our approach significantly improves the quality of reconstructed images when compared with conventional techniques.

Compton PET: a simulation study for a PET module with novel geometry and machine learning for position decoding

This paper describes a simulation study of a positron emission tomography (PET) detector module that can reconstruct the kinematics of Compton scattering within the scintillator. We used a layer structure, with which we could recover the positions and energies for the multiple interactions of a gamma ray in the different layers. Using the Compton scattering formalism, the sequence of interactions can be estimated. The true first interaction position extracted in the Compton scattering will help minimize the degradation of the reconstructed image resolution caused by intercrystal scatter events. Because of the layer structure, this module also has readily available user-defined resolution for the depth of interaction. The semi-monolithic crystals enable high light collection efficiency and an energy resolution of ∼10% has been achieved in the simulation. We used machine learning to decode the gamma ray interaction locations, achieving an average spatial resolution of 0.40 mm. Our proposed detector design provides a pathway to increase the sensitivity of a system without affecting other key performance features.

Calibration Software for Quantitative PET/CT Imaging Using Pocket Phantoms.

Multicenter clinical trials that use positron emission tomography (PET) imaging frequently rely on stable bias in imaging biomarkers to assess drug effectiveness. Many well-documented factors cause variability in PET intensity values. Two of the largest scanner-dependent errors are scanner calibration and reconstructed image resolution variations. For clinical trials, an increase in measurement error significantly increases the number of patient scans needed. We aim to provide a robust quality assurance system using portable PET/computed tomography “pocket” phantoms and automated image analysis algorithms with the goal of reducing PET measurement variability. A set of the “pocket” phantoms was scanned with patients, affixed to the underside of a patient bed. Our software analyzed the obtained images and estimated the image parameters. The analysis consisted of 2 steps, automated phantom detection and estimation of PET image resolution and global bias. Performance of the algorithm was tested under variations in image bias, resolution, noise, and errors in the expected sphere size. A web-based application was implemented to deploy the image analysis pipeline in a cloud-based infrastructure to support multicenter data acquisition, under Software-as-a-Service (SaaS) model. The automated detection algorithm localized the phantom reliably. Simulation results showed stable behavior when image properties and input parameters were varied. The PET “pocket” phantom has the potential to reduce and/or check for standardized uptake value measurement errors.

Analysis on the Effect of Sensor Views in Image Reconstruction Produced by Optical Tomography System Using Charge-Coupled Device.

Optical tomography (OPT) is a method to capture a cross-sectional image based on the data obtained by sensors, distributed around the periphery of the analyzed system. This system is based on the measurement of the final light attenuation or absorption of radiation after crossing the measured objects. The number of sensor views will affect the results of image reconstruction, where the high number of sensor views per projection will give a high image quality. This research presents an application of charge-coupled device linear sensor and laser diode in an OPT system. Experiments in detecting solid and transparent objects in crystal clear water were conducted. Two numbers of sensors views, 160 and 320 views are evaluated in this research in reconstructing the images. The image reconstruction algorithms used were filtered images of linear back projection algorithms. Analysis on comparing the simulation and experiments image results shows that, with 320 image views giving less area error than 160 views. This suggests that high image view resulted in the high resolution of image reconstruction.

Simultaneous Estimation of Bias and Resolution in PET Images With a Long-Lived "Pocket" Phantom System.

A challenge in multicenter trials that use quantitative positron emission tomography (PET) imaging is the often unknown variability in PET image values, typically measured as standardized uptake values, introduced by intersite differences in global and resolution-dependent biases. We present a method for the simultaneous monitoring of scanner calibration and reconstructed image resolution on a per-scan basis using a PET/computed tomography (CT) “pocket” phantom. We use simulation and phantom studies to optimize the design and construction of the PET/CT pocket phantom (120 × 30 × 30 mm). We then evaluate the performance of the PET/CT pocket phantom and accompanying software used alongside an anthropomorphic phantom when known variations in global bias (±20%, ±40%) and resolution (3-, 6-, and 12-mm postreconstruction filters) are introduced. The resulting prototype PET/CT pocket phantom design uses 3 long-lived sources (15-mm diameter) containing germanium-68 and a CT contrast agent in an epoxy matrix. Activity concentrations varied from 30 to 190 kBq/mL. The pocket phantom software can accurately estimate global bias and can detect changes in resolution in measured phantom images. The pocket phantom is small enough to be scanned with patients and can potentially be used on a per-scan basis for quality assurance for clinical trials and quantitative PET imaging in general. Further studies are being performed to evaluate its performance under variations in clinical conditions that occur in practice.

Ultrafast X-ray tomographic imaging of multiphase flow in bubble columns - Part 1: Image processing and reconstruction comparison

Ultrafast X-ray tomography is a recently developed imaging technique for multiphase flows. As with conventional X-ray tomography it involves reconstruction of images from X-ray projection data. If used for multiphase flow measurements, it moreover needs to be complemented with automated image processing algorithms for the extraction of flow features, such as gas holdup profiles, bubble/particle size distributions or disperse phase velocities. So far, image reconstruction is carried out with the standard filtered back-projection technique, which is fast but may not be optimal in the presence of noisy or corrupted data. As the latter is a frequent issue, search for optimal image reconstruction and data processing algorithms is continuously ongoing. This paper serves as a foundation of the image reconstruction and processing framework for the application to multiphase flow. A description is given of the procedure from reconstruction to thresholding to property extraction of ultrafast X-ray tomographic images. Two reconstruction techniques, FBP and SART are employed on the basis of phantom measurements. Each technique is evaluated separately, for FBP regarding the choice of filter and for SART regarding the termination criteria. Image reconstruction resolution, computational costs and sensitivity to the threshold value are investigated. Based on the analysis, FBP with the Ram-Lak filter is selected for image processing purposes. Furthermore, it is shown that from experiments with moving objects, there is fair agreement between measurements and the phantom dimensions. The described imaging process can be applied to different attenuation materials, simulating gas-solid and gas-liquid properties.

Axial Movement Parameter Study on Preclinical Slit-Slat SPECT

One challenge to discrete detector slit-slat SPECT is that the system’s resolution and sensitivity vary significantly in the axial direction. The objective of this work was to reduce the variation by introducing axial movement and to determine the corresponding parameters, i.e. step size (D) and total length (L). We calculated the system response matrix of the slit-slat SPECT system and evaluated the system’s axial-direction resolution and sensitivity as functions of axial positions in the field of view. A series of scan protocols with combined rotational and axial movements, that is, helical scans, were applied to simulated phantom studies to select the preferred L and D values. Introducing axial movement effectively improved the system’s axial-direction sensitivity uniformity and reconstructed image resolution in both axial direction and in transverse plane, and it also reduced the artifacts in the transverse plane. The preferred choice of L is the cycle of the periodic pattern formed by the slat collimator’s and detector crystals. As about the value of D, a smaller D value is preferred for better uniformity as well as resolution improvement but practical constraints would apply.