Image Reconstruction Approach Research Articles

Cone-Beam Computed Tomography: A User-Friendly, Practical Roadmap to the Planning and Execution of Every Rhinoplasty-A 5-Year Review.

Cone-beam computed tomography has recently rapidly developed worldwide as a versatile and convenient alternative to traditional computed tomography for imaging of the maxillofacial region. However, most surgeons performing rhinoplasty are surprisingly unfamiliar with it, in both the plastic surgery and ear, nose, and throat communities. The broad clinical experience of a single center over the past 5 years is reviewed. The many applications of cone-beam computed tomography to primary and secondary rhinoplasty are analyzed regarding septum, turbinates, nasal bones, skin thickness, and other issues. The importance of a paradigm shift from a two-dimensional to a three-dimensional approach in image reconstruction is demonstrated, together with the value of surface contour enhancement. Cone-beam computed tomography has a multitude of practical applications highly relevant to rhinoplasty. The surface image will strengthen aesthetic analysis, and the detailed preview of bony and functional anatomy will facilitate surgical planning. Cone-beam computed tomography serves as a roadmap to plan and execute rhinoplasties more predictably and efficiently. The availability of spatial views and accurate detail, together with the possibility of easy, accurate measuring, offers a plenitude of potential applications. Cone-beam computed tomography is a user-friendly, quick technique with abundant advantages in planning any rhinoplasty. It causes the patient no inconvenience and has very few, if any, drawbacks, with these being limited to radiation exposure and limited cost.

Joint Reconstruction of Conductivity and Velocity in Two-Phase Flows Using Electromagnetic Flow Tomography and Electrical Tomography: A Simulation Study

Characterization of two-phase flows is a common problem in the process industry. Due to complexity of flows, having accurate measurements of velocity fields, phase fractions, and the volumetric flow rates is challenging. Even though several approaches have been developed, accurate measurements of the flow quantities remain a challenge. Metering volumetric flow rate requires information on the local phase fraction and velocity field in the pipe cross section to be imaged. These data are commonly obtained using two individual measurement systems, the so-called dual-modality system. This article considers a dual-modality consisting of electromagnetic flow tomography (EMFT) and electrical tomography (ET) imaging, which provide information on the velocity field and electrical conductivity distribution, respectively; the combination of EMFT and ET reconstruction can be further used for inferring the volumetric flow rate. The aim of this article is to improve the accuracy of the EMFT and ET reconstructions—and the resulting flow rate estimate—by enhanced modeling of the unknown velocity and conductivity fields. More specifically, the proposed approach is based on modeling the joint statistics of the velocity and conductivity with a cross-covariance matrix which is based on a representative ensemble of velocity–conductivity image pairs obtained, for example, from fluid dynamical modeling or empirically. The cross-covariance matrix is incorporated in the joint reconstruction within the Bayesian inverse problems framework as an additional prior model. The proposed image reconstruction approach is tested with a set of numerical simulations. The results show that the joint reconstruction approach (JRA) with a cross-covariance model is capable of improving the accuracy of the estimates compared to the approaches that are currently in use.

A Pseudo-TOF Image Reconstruction Approach for Three-Gamma Small Animal Imaging

In this article, we propose a novel image reconstruction technique for three-gamma (3- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ {\gamma }$ </tex-math></inline-formula> ) imaging systems that aims at reaching high image quality with a low statistics. The proposed approach is based on the utilization of a ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ {\beta +}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\gamma }$ </tex-math></inline-formula> ) emitter and a liquid xenon (LXe) camera, the Xenon Medical Imaging System 2 (XEMIS2), that is currently being manufactured. The third gamma emitted is more energetic than the two 511-keV photons and interacts mostly through Compton scattering in LXE; therefore, we utilize the interactions’ position and the deposited energy to define a Compton cone that gives the direction from where the third gamma was emitted. The intersection point between the cone and the two coincidence photons’ lines of response (LOR) is the center of the probability distribution function (PDF), a resolution model similar to that of time-of-flight (TOF)-positron emission tomography (PET). The third gamma’s information is used to narrow the LOR portion employed during reconstruction. In this article, we show reconstruction results from simulation using highly realistic Geant4 Application for Emission Tomography (GATE) Monte Carlo simulation of the imaging device and a NEMA-like phantom.

Color Image Restoration Exploiting Inter-Channel Correlation With a 3-Stage CNN

Image restoration is a critical component of image processing pipelines and for low-level computer vision tasks. Conventional image restoration approaches are mostly based on hand-crafted image priors. The inter-channel correlation of color images is not fully exploited. Motivated by the special characteristics of the inter-channel correlation (higher correlation for red/green and green/blue channels than for red/blue) in color images and general characteristics (green channel always shows the best image quality among the three color components) of distorted color images, in this paper, a three-stage convolutional neural network (CNN) structure is proposed for color image restoration tasks. Since the green channel is found to have the best quality among all three channels, in the first stage, the network is designed to reconstruct the green component. Then, with the guidance of the reconstructed green channel from the first stage, the red and blue channels are reconstructed in the second stage with two parallel networks. Finally, the intermediate reconstructions from the previous stages are concatenated and further refined jointly. We demonstrate the capabilities of the proposed three-stage structure with three typical color image restoration tasks: color image demosaicking, color compression artifacts reduction, and real-world color image denoising. In addition, we integrate pixel-shuffle convolution into our scheme to improve the efficiency, and also introduce a quality-blind training strategy to simplify the training process for the compression artifacts reduction task. Extensive experimental results and analyses show that the proposed structure successfully exploits the spatial and inter-channel correlation of color images and outperforms the state-of-the-art image reconstruction approaches.

Alternation of inverse problem approach and deep learning for lens-free microscopy image reconstruction

A lens-free microscope is a simple imaging device performing in-line holographic measurements. In the absence of focusing optics, a reconstruction algorithm is used to retrieve the sample image by solving the inverse problem. This is usually performed by optimization algorithms relying on gradient computation. However the presence of local minima leads to unsatisfactory convergence when phase wrapping errors occur. This is particularly the case in large optical thickness samples, for example cells in suspension and cells undergoing mitosis. To date, the occurrence of phase wrapping errors in the holographic reconstruction limits the application of lens-free microscopy in live cell imaging. To overcome this issue, we propose a novel approach in which the reconstruction alternates between two approaches, an inverse problem optimization and deep learning. The computation starts with a first reconstruction guess of the cell sample image. The result is then fed into a neural network, which is trained to correct phase wrapping errors. The neural network prediction is next used as the initialization of a second and last reconstruction step, which corrects to a certain extent the neural network prediction errors. We demonstrate the applicability of this approach in solving the phase wrapping problem occurring with cells in suspension at large densities. This is a challenging sample that typically cannot be reconstructed without phase wrapping errors, when using inverse problem optimization alone.

Dual-Energy CT Angiography in Peripheral Arterial Occlusive Disease: Diagnostic Accuracy of Different Image Reconstruction Approaches

To evaluate the diagnostic accuracy of five DE-CTA image reconstruction approaches for detection of lower extremity arterial stenosis using digital subtraction angiography as reference standard. One hundred and eleven patients (63 males; mean age, 75.0 ± 9.7 years) who underwent clinically indicated lower extremity DE-CTA were included in this IRB-approved, HIPAA-compliant retrospective study. Routine multiplanar reconstructions (MPR), curved MPR (cMPR), DE-bone-and-calcified-plaque-subtraction (DE-CS), maximum-intensity projections (MIP), and DE-CS MPR were visually assessed for stenoses > 50%. Automatic objective stenosis grading was implemented on cMPRs. The effect of vessel calcification and luminal contrast on diagnostic performance was evaluated. Sensitivity for stenosis detection was high (96.4%-98.6%) with no significant differences among reconstruction approaches. Specificity (74.9%-92.2%) and accuracy (86.9%-94.5%) varied significantly. Pronounced vessel wall calcification and inferior intraluminal contrast attenuation had no significant effect on sensitivity (calcification: p = 0.167 for MPR; 0.567 DE-CS MPR; 0.057 DE-CS MIP; 0.272 cMPR; 0.185 automatic grading; contrast attenuation: p = 1.000 for all reconstructions), but lead to reduced specificity in visual assessment (calcification: p = 0.002 for MPR; <0.001 DE-CS MPR, DE-CS MIP, and cMPR; contrast attenuation: p = 0.844 for MPR; 0.001 DE-CS MPR and DE-CS MIP; 0.420 cMPR). Routine MPR showed highest overall diagnostic performance. Regardless of image reconstruction approach, vessel calcification and intraluminal contrast attenuation, lower extremity DE-CTA possesses high sensitivity for detection of significant stenoses. Specificity and accuracy vary between reconstruction approaches, indicating the need for additional verification of potential stenotic lesions by use of MPR to reduce the number of unnecessary invasive DSAs due to false-positive CTA findings.

Prior-image-based Reconstruction for On-board CBCT Imaging: Application in Image-guided Radiotherapy for Head and Neck Cancer

On-board cone-beam CT (CBCT) imaging has been widely used to reduce set-up errors in head and neck cancer treated with IMRT/VMAT. However, its frequent use has raised concerns for excessive imaging dose and increased risk of developing secondary malignancies. This work investigates the feasibility of significantly reducing CBCT radiation dose by taking advantage of high-quality planning CT and a prior-image-based reconstruction approach. In image-guided radiotherapy for head and neck cancer, one standard protocol (Full-fan, 200 deg rotation, 100 kVp, 0.3 mAs/projection, 500 projections, 150 mAs in total) is employed for kV-CBCT scans. Since the high-quality planning CT scan is a rich source of patient-specific anatomical knowledge, it offers an opportunity to integrate the planning CT into on-board CBCT reconstruction for radiation dose reduction. Therefore, we studied a prior-image-based reconstruction approach for on-board CBCT imaging, which only requires partial projections and thus substantially reduces radiation dose. The prior-image-based reconstruction takes into consideration differences in attenuation coefficients (via histogram matching) and patient positioning (via affine registration) between the planning CT and on-board CBCT scans. Additionally, proper prior information control is applied to ensure the prior-image-based reconstruction does not introduce false features when there are anatomical changes between scans. An anthropomorphic head phantom and two head and neck patient datasets were used to validate the efficacy of the proposed approach. We extracted certain number of projections from the 500 projections to emulate different radiation dose levels. As we decrease the number of projections to as low as 100 (30 mAs equivalent, CTDIvol = 0.63 mGy), the standard FDK reconstructed images are degraded with severe noise and streak artifacts, and the conventional model-based iterative reconstruction generates images with inferior image quality, which could jeopardize the accuracy of patient set-up. In contrast, our proposed prior-image-based reconstruction can yield comparable image quality as the ground truth images reconstructed with the full projections, in terms of noise, resolution, and contrast-to-noise ratio. This indicates an 80% radiation dose reduction for on-board CBCT imaging by the proposed reconstruction scheme. The proposed prior-image-based reconstruction framework provides an opportunity to substantially reduce the radiation dose for on-board CBCT, without compromising image quality. This work offers a solution to reduce cumulative imaging dose as the use of CBCT for image-guided radiotherapy increases.

High-performance rapid MR parameter mapping using model-based deep adversarial learning

PurposeTo develop and evaluate a deep adversarial learning-based image reconstruction approach for rapid and efficient MR parameter mapping. MethodsThe proposed method provides an image reconstruction framework by combining the end-to-end convolutional neural network (CNN) mapping, adversarial learning, and MR physical models. The CNN performs direct image-to-parameter mapping by transforming a series of undersampled images directly into MR parameter maps. Adversarial learning is used to improve image sharpness and enable better texture restoration during the image-to-parameter conversion. An additional pathway concerning the MR signal model is added between the estimated parameter maps and undersampled k-space data to ensure the data consistency during network training. The proposed framework was evaluated on T2 mapping of the brain and the knee at an acceleration rate R = 8 and was compared with other state-of-the-art reconstruction methods. Global and regional quantitative assessments were performed to demonstrate the reconstruction performance of the proposed method. ResultsThe proposed adversarial learning approach achieved accurate T2 mapping up to R = 8 in brain and knee joint image datasets. Compared to conventional reconstruction approaches that exploit image sparsity and low-rankness, the proposed method yielded lower errors and higher similarity to the reference and better image sharpness in the T2 estimation. The quantitative metrics were normalized root mean square error of 3.6% for brain and 7.3% for knee, structural similarity index of 85.1% for brain and 83.2% for knee, and tenengrad measures of 9.2% for brain and 10.1% for the knee. The adversarial approach also achieved better performance for maintaining greater image texture and sharpness in comparison to the CNN approach without adversarial learning. ConclusionThe proposed framework by incorporating the efficient end-to-end CNN mapping, adversarial learning, and physical model enforced data consistency is a promising approach for rapid and efficient reconstruction of quantitative MR parameters.

Photothermal testing of composite materials: Virtual wave concept with prior information for parameter estimation and image reconstruction

In this paper, we propose a new parameter estimation and image reconstruction approach for the photothermal testing of composite materials. Therefore, the full multidimensional evaluation method, virtual wave concept, is extended to estimate the orthotropic thermal diffusivity tensor and to reconstruct the initial temperature distribution after a laser spot heating in an orthotropic material. We establish a formal relationship between the virtual speed of sound tensor and the thermal diffusivity tensor. Furthermore, we show how prior information in the form of positivity and sparsity can be incorporated in the regularization process to improve the solution of the inverse imaging problem. In a second step, the initial temperature distribution is reconstructed by applying ultrasonic imaging methods on the calculated 3D bimodal virtual wave field. This new approach is validated on simulation and experimental data of a unidirectional carbon fiber reinforced polymer. The information loss that results from entropy production during heat diffusion can be partly compensated by including prior information. This allows an accurate parameter estimation and a high-resolution image reconstruction.

Artificial intelligent classification of biomedical color image using quaternion discrete radial Tchebichef moments

In this work, we propose a new set of an intelligent classifier of biomedical color images using Quaternion Discrete Radial Tchebichef Moments (QRTM). This moment has shown very high robustness in terms of color image representation. Thus, we propose a new approach for the fast and accurate reconstruction of multi-level and color images. This approach based on the reconstruction of color images by applying matrix computation. In the second step, we propose a new method for the extraction of Quaternion Discrete Radial Tchebichef Moments invariant (QRTMI). This method based on the representation of the extraction of these invariants. The performance of the invariance of these moments under the three types of geometrical transformations (Rotation, translation, scale) are very important. Finally, we present a new model based on a Multilayer perceptron (MLP) for the classification of biomedical color images. The experimental results show that the QRTMI very powerful compared with radial Legendre - Fourier and Quaternion discrete radial Krawtchouk moments invariant for the biomedical colors images using the BreaKHis Database (Breast Tumor).

Temporal Huber Regularization for DCE-MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to study microvascular structure and tissue perfusion. In DCE-MRI, a bolus of gadolinium-based contrast agent is injected into the blood stream and spatiotemporal changes induced by the contrast agent flow are estimated from a time series of MRI data. Sufficient time resolution can often only be obtained by using an imaging protocol which produces undersampled data for each image in the time series. This has lead to the popularity of compressed sensing-based image reconstruction approaches, where all the images in the time series are reconstructed simultaneously, and temporal coupling between the images is introduced into the problem by a sparsity promoting regularization functional. We propose the use of Huber penalty for temporal regularization in DCE-MRI, and compare it to total variation, total generalized variation and smoothness-based temporal regularization models. We also study the effect of spatial regularization to the reconstruction and compare the reconstruction accuracy with different temporal resolutions due to varying undersampling. The approaches are tested using simulated and experimental radial golden angle DCE-MRI data from a rat brain specimen. The results indicate that Huber regularization produces similar reconstruction accuracy with the total variation-based models, but the computation times are significantly faster.

A machine learning framework for enhancing digital experiences in cultural heritage

PurposeDigital tools have been used to document cultural heritage with high-quality imaging and metadata. However, some of the historical assets are totally or partially unlabeled and some are physically damaged, which decreases their attractiveness and induces loss of value. This paper introduces a new framework that aims at tackling the cultural data enrichment challenge using machine learning.Design/methodology/approachThis framework focuses on the automatic annotation and metadata completion through new deep learning classification and annotation methods. It also addresses issues related to physically damaged heritage objects through a new image reconstruction approach based on supervised and unsupervised learning.FindingsThe authors evaluate approaches on a data set of cultural objects collected from various cultural institutions around the world. For annotation and classification part of this study, the authors proposed and implemented a hierarchical multimodal classifier that improves the quality of annotation and increases the accuracy of the model, thanks to the introduction of multitask multimodal learning. Regarding cultural data visual reconstruction, the proposed clustering-based method, which combines supervised and unsupervised learning is found to yield better quality completion than existing inpainting frameworks.Originality/valueThis research work is original in sense that it proposes new approaches for the cultural data enrichment, and to the authors’ knowledge, none of the existing enrichment approaches focus on providing an integrated framework based on machine learning to solve current challenges in cultural heritage. These challenges, which are identified by the authors are related to metadata annotation and visual reconstruction.

Investigating low-delay deep learning-based cultural image reconstruction

Numerous cultural assets host a great historical and moral value, but due to their degradation, this value is heavily affected as their attractiveness is lost. One of the solutions that most heritage organizations and museums currently choose is to leverage the knowledge of art and history experts in addition to curators to recover and restore the damaged assets. This process is labor-intensive, expensive and more often results in just an assumption over the damaged or missing region. In this work, we tackle the issue of completing missing regions in artwork through advanced deep learning and image reconstruction (inpainting) techniques. Following our analysis of different image completion and reconstruction approaches, we noticed that these methods suffer from various limitations such as lengthy processing times and hard generalization when trained with multiple visual contexts. Most of the existing learning-based image completion and reconstruction techniques are trained on large datasets with the objective of retrieving the original data distribution of the training samples. However, this distribution becomes more complex when the training data is diverse making the training process difficult and the reconstruction inefficient. Through this paper, we present a clustering-based low-delay image completion and reconstruction approach which combines supervised and unsupervised learning to address the highlighted issues. We compare our technique to the current state of the art using a real-world dataset of artwork collected from various cultural institutions. Our approach is evaluated using statistical methods and a surveyed audience to better interpret our results objectively and subjectively.

Learning from irregularly sampled data for endomicroscopy super-resolution: a comparative study of sparse and dense approaches

PurposeProbe-based confocal laser endomicroscopy (pCLE) enables performing an optical biopsy via a probe. pCLE probes consist of multiple optical fibres arranged in a bundle, which taken together generate signals in an irregularly sampled pattern. Current pCLE reconstruction is based on interpolating irregular signals onto an over-sampled Cartesian grid, using a naive linear interpolation. It was shown that convolutional neural networks (CNNs) could improve pCLE image quality. Yet classical CNNs may be suboptimal in regard to irregular data.MethodsWe compare pCLE reconstruction and super-resolution (SR) methods taking irregularly sampled or reconstructed pCLE images as input. We also propose to embed a Nadaraya–Watson (NW) kernel regression into the CNN framework as a novel trainable CNN layer. We design deep learning architectures allowing for reconstructing high-quality pCLE images directly from the irregularly sampled input data. We created synthetic sparse pCLE images to evaluate our methodology.ResultsThe results were validated through an image quality assessment based on a combination of the following metrics: peak signal-to-noise ratio and the structural similarity index. Our analysis indicates that both dense and sparse CNNs outperform the reconstruction method currently used in the clinic.ConclusionThe main contributions of our study are a comparison of sparse and dense approach in pCLE image reconstruction. We also implement trainable generalised NW kernel regression as a novel sparse approach. We also generated synthetic data for training pCLE SR.

An Image Reconstruction Algorithm with Experimental Validation for Electrical Impedance Tomography Imaging Applications

In the Non-Invasive Bio Impedance Technique (NIBIT) a low current pulse with high frequency inserted between two electrodes of the object while measuring voltages from the other remaining electrodes with respect to the reference electrode. The electrode arrangement is defined in the form of cylindrical shapes of the surface of the phantom. After these arrangements, it inserts the current pulse and measures the voltages according to the selected data acquisition method of bio impedance. The presented algorithm analyzes and defines each obtained data sample from the used phantoms and also allows Image Reconstruction (IR) based on developed Graphical User interface (GUI) on MATLAB. The used IR approach is based on Tikhonov regularization and FEM. The FEM and Tikhonov regularization are mathematical approaches that deal with Forward Problem (FP) and Inverse Problem (IP) of images. In our approach, the FP solution is identified first in order to reconstruct the conductivity distribution through the EIT inverse solution. Thereafter, This FP is solved through the known current pulse of a given conductivity medium. Likewise, the IP is identified and solved through the boundary potential of the object. The end of the obtained result provides a comparable result for the used phantom according to its internal structure. This proposed technique is still reliable despite having some standardization issue according to the procedure.

Non-iterative image reconstruction from sparse magnetic resonance imaging radial data without priors

The state-of-the-art approaches for image reconstruction using under-sampled k-space data are compressed sensing based. They are iterative algorithms that optimize objective functions with spatial and/or temporal constraints. This paper proposes a non-iterative algorithm to estimate the un-measured data and then to reconstruct the image with the efficient filtered backprojection algorithm. The feasibility of the proposed method is demonstrated with a patient magnetic resonance imaging study. The proposed method is also compared with the state-of-the-art iterative compressed-sensing image reconstruction method using the total-variation optimization norm.

Editorial for Special Issue “Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms”

Microwave radar imaging plays a key role in several civilian and defense applications, such as security, surveillance, diagnostics and monitoring in civil engineering and cultural heritage, environment observation, with particular emphasis on disasters and crisis management, where it is required to remotely sense the area of interest in a timely, safe and effective way. To address these constraints, a technological opportunity is offered by radar systems mounted onboard smart and flexible platforms, such as ground-based ones, airplanes, helicopters, drones, unmanned aerial and ground vehicles (UAV and UGV). For this reason, radar imaging based on data collected by such platforms is gaining interest in the remote sensing community. However, a full exploitation of smart and flexible radar systems requires the development and use of image formation techniques and reconstruction approaches able to exploit and properly deal with non-conventional data acquisition configurations. The other main issue is related to the need to operate in challenging environments, and still deliver high target detection, localization and tracking. These environments include through the wall imaging, rugged terrain and rough surface/subsurface. In these cases, one seeks mitigation of the adverse effects of clutter and multipath via the implementation of effective signal processing strategies and electromagnetic modeling.

A family portrait of disk inner rims around Herbig Ae/Be stars

Context. The innermost astronomical unit (au) in protoplanetary disks is a key region for stellar and planet formation, as exoplanet searches have shown a large occurrence of close-in planets that are located within the first au around their host star. Aims. We aim to reveal the morphology of the disk inner rim using near-infrared interferometric observations with milli-arcsecond resolution provided by near-infrared multitelescope interferometry. Methods. We provide model-independent reconstructed images of 15 objects selected from the Herbig AeBe survey carried out with PIONIER at the Very Large Telescope Interferometer, using the semi-parametric approach for image reconstruction of chromatic objects. We propose a set of methods to reconstruct and analyze the images in a consistent way. Results. We find that 40% of the systems (6/15) are centrosymmetric at the angular resolution of the observations. For the rest of the objects, we find evidence for asymmetric emission due to moderate-to-strong inclination of a disk-like structure for ~30% of the objects (5/15) and noncentrosymmetric morphology due to a nonaxisymmetric and possibly variable environment (4/15, ~27%). Among the systems with a disk-like structure, 20% (3/15) show a resolved dust-free cavity. Finally, we do not detect extended emission beyond the inner rim. Conclusions. The image reconstruction process is a powerful tool to reveal complex disk inner rim morphologies, which is complementary to the fit of geometrical models. At the angular resolution reached by near-infrared interferometric observations, most of the images are compatible with a centrally peaked emission (no cavity). For the most resolved targets, image reconstruction reveals morphologies that cannot be reproduced by generic parametric models (e.g., perturbed inner rims or complex brightness distributions). Moreover, the nonaxisymmetric disks show that the spatial resolution probed by optical interferometers makes the observations of the near-infrared emission (inside a few au) sensitive to temporal evolution with a time-scale down to a few weeks. The evidence of nonaxisymmetric emission that cannot be explained by simple inclination and radiative transfer effects requires alternative explanations, such as a warping of the inner disks. Interferometric observations can therefore be used to follow the evolution of the asymmetry of those disks at an au or sub-au scale.

Image reconstruction with low-rankness and self-consistency of k-space data in parallel MRI.

Parallel magnetic resonance imaging has served as an effective and widely adopted technique for accelerating data collection. The advent of sparse sampling offers aggressive acceleration, allowing flexible sampling and better reconstruction. Nevertheless, faithfully reconstructing the image from limited data still poses a challenging task. Recent low-rank reconstruction methods are superior in providing high-quality images. Nevertheless, none of them employ the routinely acquired calibration data to improve image quality in parallel magnetic resonance imaging. In this work, an image reconstruction approach named STDLR-SPIRiT is proposed to explore the simultaneous two-directional low-rankness (STDLR) in the k-space data and to mine the data correlation from multiple receiver coils with the iterative self-consistent parallel imaging reconstruction (SPIRiT). The reconstruction problem is then solved with a singular value decomposition-free numerical algorithm. Experimental results of phantom and brain imaging data show that the proposed method outperforms the state-of-the-art methods in terms of suppressing artifacts and achieving the lowest error. Moreover, the proposed method exhibits robust reconstruction even when the auto-calibration signals are limited in parallel imaging. Overall the proposed method can be exploited to achieve better image quality for accelerated parallel magnetic resonance imaging.

A Novel Image Reconstruction Strategy for ECT: Combining Two Algorithms With a Graph Cut Method

Image reconstruction plays a key role in the application of electrical capacitance tomography (ECT). Although many different algorithms have been developed in the past, it is often difficult to obtain satisfactory images in all imaging regions by the use of a single algorithm due to the soft-field nature of ECT. This motivated us to develop a novel ECT image reconstruction strategy, in which a high-quality image can be obtained by combining the images reconstructed by two different algorithms via a graph cut method. By doing so, it is possible to retain the advantage of each algorithm for specified imaging regions and improve the quality of the whole image. This strategy was verified both numerically and experimentally for two widely used ECT reconstruction algorithms, i.e., linear backprojection and Tikhonov regularization. The results for stationary objects as well as gas–solid fluidized beds demonstrated that this graph-cut-based combination strategy presents a promising approach for ECT image reconstruction.