Computed tomography vs liver stiffness measurement and magnetic resonance imaging in evaluating esophageal varices in cirrhotic patients:A systematic review and meta-analysis

BACKGROUND Computed tomography(CT),liver stiffness measurement(LSM),and magnetic resonance imaging(MRI)are non-invasive diagnostic methods for esophageal varices(EV)and for the prediction of high-bleeding-risk EV(HREV)in cirrhotic patients.However,the clinical use of these methods is controversial.AIM To evaluate the accuracy of LSM,CT,and MRI in diagnosing EV and predicting HREV in cirrhotic patients.METHODS We performed literature searches in multiple databases,including Pub Med,Embase,Cochrane,CNKI,and Wanfang databases,for articles that evaluated the accuracy of LSM,CT,and MRI as candidates for the diagnosis of EV and prediction of HREV in cirrhotic patients.Summary sensitivity and specificity,positive likelihood ratio and negative likelihood ratio,diagnostic odds ratio,and the areas under the summary receiver operating characteristic curves were analyzed.The quality of the articles was assessed using the quality assessment of diagnostic accuracy studies-2 tool.Heterogeneity was examined by Q-statistic test and I2 index,and sources of heterogeneity were explored using metaregression and subgroup analysis.Publication bias was evaluated using Deek’s funnel plot.All statistical analyses were conducted using Stata12.0,Meta Disc1.4,and Rev Man5.3.RESULTS Overall,18,17,and 7 relevant articles on the accuracy of LSM,CT,and MRI in evaluating EV and HREV were retrieved.A significant heterogeneity was observed in all analyses(P<0.05).The areas under the summary receiver operating characteristic curves of LSM,CT,and MRI in diagnosing EV and predicting HREV were 0.86(95%confidence interval[CI]:0.83-0.89),0.91(95%CI:0.88-0.93),and 0.86(95%CI:0.83-0.89),and 0.85(95%CI:0.81-0.88),0.94(95%CI:0.91-0.96),and 0.83(95%CI:0.79-0.86),respectively,with sensitivities of 0.84(95%CI:0.78-0.89),0.91(95%CI:0.87-0.94),and 0.81(95%CI:0.76-0.86),and 0.81(95%CI:0.75-0.86),0.88(95%CI:0.82-0.92),and 0.80(95%CI:0.72-0.86),and specificities of 0.71(95%CI:0.60-0.80),0.75(95%CI:0.68-0.82),and 0.82(95%CI:0.70-0.89),and 0.73(95%CI:0.66-0.80),0.87(95%CI:0.81-0.92),and 0.72(95%CI:0.62-0.80),respectively.The corresponding positive likelihood ratios were 2.91,3.67,and 4.44,and 3.04,6.90,and2.83;the negative likelihood ratios were 0.22,0.12,and 0.23,and 0.26,0.14,and 0.28;the diagnostic odds ratios were 13.01,30.98,and 19.58,and 11.93,49.99,and 10.00.CT scanner is the source of heterogeneity.There was no significant difference in diagnostic threshold effects(P>0.05)or publication bias(P>0.05).CONCLUSION Based on the meta-analysis of observational studies,it is suggested that CT imaging,a non-invasive diagnostic method,is the best choice for the diagnosis of EV and prediction of HREV in cirrhotic patients compared with LSM and MRI.

Dynamic enhanced computed tomography imaging findings of an inflammatory fibroid polyp with massive fibrosis in the stomach

Inflammatory fibroid polyp(IFP) is a rare benign lesion of the gastrointestinal tract. We report a case of computed tomography(CT) imaging finding of a gastric IFP with massive fibrosis. CT scans showed thickening of submucosal layer with overlying mucosal hyperenhancement in the gastric antrum. The submucosal layer showed increased enhancement on delayed phase imaging. An antrectomy with gastroduodenostomy was performed because gastric cancer was suspected, particularly signet ring cell carcinoma. The histopathological diagnosis was an IFP with massive fibrosis. The authors suggest that when the submucosal layer of the gastric wall is markedly thickened with delayed enhancement and preservation of the mucosal layer, an IFP with massive fibrosis should be considered in the differential diagnosis.

Terahertz Three-Dimensional Imaging Based on Computed Tomography with Photonics-Based Noise Source

Computed tomography has been proven to be useful for non-destructive inspection of structures and materials. We build a three-dimensional imaging system with the photonically generated incoherent noise source and the Schottky barrier diode detector in the terahertz frequency band （90–140GHz）. Based on the computed tomography technique, the three-dimensional image of a ceramic sample is reconstructed successfully by stacking the slices at different heights. The imaging results not only indicate the ability of terahertz wave in the non-invasive sensing and non-destructive inspection applications, but also prove the effectiveness and superiority of the uni-traveling-carrier photodiode as a terahertz source in the imaging applications.

Optimization method of Hadamard coding plate inγ‑ray computational ghost imaging

Owing to the constraints on the fabrication ofγ-ray coding plates with many pixels,few studies have been carried out onγ-ray computational ghost imaging.Thus,the development of coding plates with fewer pixels is essential to achieveγ-ray computational ghost imaging.Based on the regional similarity between Hadamard subcoding plates,this study presents an optimization method to reduce the number of pixels of Hadamard coding plates.First,a moving distance matrix was obtained to describe the regional similarity quantitatively.Second,based on the matrix,we used two ant colony optimization arrangement algorithms to maximize the reuse of pixels in the regional similarity area and obtain new compressed coding plates.With full sampling,these two algorithms improved the pixel utilization of the coding plate,and the compression ratio values were 54.2%and 58.9%,respectively.In addition,three undersampled sequences(the Harr,Russian dolls,and cake-cutting sequences)with different sampling rates were tested and discussed.With different sampling rates,our method reduced the number of pixels of all three sequences,especially for the Russian dolls and cake-cutting sequences.Therefore,our method can reduce the number of pixels,manufacturing cost,and difficulty of the coding plate,which is beneficial for the implementation and application ofγ-ray computational ghost imaging.

Coronary arterial bypass graft patency evaluated by multi-detector computed tomography and magnetic resonance imaging

The progression of atherosclerosis of the coronary artery does not stop after coronary arterial bypass grafting (CABG) surgery.……

Defogging computational ghost imaging via eliminating photon number fluctuation and a cycle generative adversarial network

Imaging through fluctuating scattering media such as fog is of challenge since it seriously degrades the image quality.We investigate how the image quality of computational ghost imaging is reduced by fluctuating fog and how to obtain a high-quality defogging ghost image. We show theoretically and experimentally that the photon number fluctuations introduced by fluctuating fog is the reason for ghost image degradation. An algorithm is proposed to process the signals collected by the computational ghost imaging device to eliminate photon number fluctuations of different measurement events. Thus, a high-quality defogging ghost image is reconstructed even though fog is evenly distributed on the optical path. A nearly 100% defogging ghost image is obtained by further using a cycle generative adversarial network to process the reconstructed defogging image.

Research on the model of high robustness computational optical imaging system

Computational optical imaging is an interdisciplinary subject integrating optics, mathematics, and information technology. It introduces information processing into optical imaging and combines it with intelligent computing, subverting the imaging mechanism of traditional optical imaging which only relies on orderly information transmission. To meet the high-precision requirements of traditional optical imaging for optical processing and adjustment, as well as to solve its problems of being sensitive to gravity and temperature in use, we establish an optical imaging system model from the perspective of computational optical imaging and studies how to design and solve the imaging consistency problem of optical system under the influence of gravity, thermal effect, stress, and other external environment to build a high robustness optical system. The results show that the high robustness interval of the optical system exists and can effectively reduce the sensitivity of the optical system to the disturbance of each link, thus realizing the high robustness of optical imaging.

End-to-end computational design for an EUV solar corona multispectral imager with stray light suppression

An extreme ultraviolet solar corona multispectral imager can allow direct observation of high temperature coronal plasma,which is related to solar flares,coronal mass ejections and other significant coronal activities.This manuscript proposes a novel end-to-end computational design method for an extreme ultraviolet(EUV)solar corona multispectral imager operating at wavelengths near 100 nm,including a stray light suppression design and computational image recovery.To suppress the strong stray light from the solar disk,an outer opto-mechanical structure is designed to protect the imaging component of the system.Considering the low reflectivity(less than 70%)and strong-scattering(roughness)of existing extreme ultraviolet optical elements,the imaging component comprises only a primary mirror and a curved grating.A Lyot aperture is used to further suppress any residual stray light.Finally,a deep learning computational imaging method is used to correct the individual multi-wavelength images from the original recorded multi-slit data.In results and data,this can achieve a far-field angular resolution below 7",and spectral resolution below 0.05 nm.The field of view is±3 R_(☉)along the multi-slit moving direction,where R☉represents the radius of the solar disk.The ratio of the corona's stray light intensity to the solar center's irradiation intensity is less than 10-6 at the circle of 1.3 R_(☉).

Radiation Dose from CT-Scan of Childhood’s Head: Results of the First Ivorian Survey in a Single Study Site

Objectives: This study aims to evaluate the level of X-ray doses used in childhood’s head as Local Diagnostic Reference Levels (LDRLs) in computed tomography (CT) at a university hospital in Côte d’Ivoire. The Diagnostic Reference Level (DRL) have been set up and used to prevent unusually high radiation doses used in radiology departments and is therefore an optimization tool for practices and procedures in medical X-ray imaging for the radiation protection of patients. Methods: A prospective study of volume CT dose index (CTDIvol) and dose length product (DLP) was performed on images of childhood’s head obtained from a CT-scanner of 64 bars equipped with the tube current modulation capability and manufactured by Hitachi Medical System. 122 CT-scan data from 55 childhood’s head were analyzed. The scan data were stratified in four age groups: Results: The 75th percentile of CTDIvol and DLP (set as LDRL) obtained with respect to the stratified age groups are: 22.5 mGy and 452.5 mGy∙cm, 27.7 mGy and 690.6 mGy∙cm, 28 mGy and 722.4 mGy∙cm, 33.6 mGy and 736.8 mGy∙cm respectively. These outcome values increase with respect to the age of pediatric patients and are comparable to DRLs values obtained internationally. Conclusions: Obtaining good image quality while using low dose in children’s head computed tomography for radiation protection require to setup more surveys in Côte d’Ivoire for regional and national DRL. We proposed through this survey LDRLs in terms of CTDIvol and DLP, comparable to international DRLs values. This survey will be strengthened by additional surveys in order to obtain national DRLs for the radiation protection of the child patient in Côte d’Ivoire.

Artificial intelligence assisted light control and computational imaging through scattering media

Coherent optical control within or through scattering media via wavefront shaping has seen broad applications since its invention around 2007.Wavefront shaping is aimed at overcoming the strong scattering,featured by random interference,namely speckle patterns.This randomness occurs due to the refractive index inhomogeneity in complex media like biological tissue or the modal dispersion in multimode fiber,yet this randomness is actually deterministic and potentially can be time reversal or precompensated.Various wavefront shaping approaches,such as optical phase conjugation,iterative optimization,and transmission matrix measurement,have been developed to generate tight and intense optical delivery or high-resolution image of an optical object behind or within a scattering medium.The performance of these modula-tions,however,is far from satisfaction.Most recently,artifcial intelligence has brought new inspirations to this field,providing exciting hopes to tackle the challenges by mapping the input and output optical patterns and building a neuron network that inherently links them.In this paper,we survey the developments to date on this topic and briefly discuss our views on how to harness machine learning(deep learning in particular)for further advancements in the field.

An algorithm for computed tomography image reconstruction from limited-view projections

With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper develops an iterative algorithm for image reconstruction, which can fit the most cases. This method gives an image reconstruction flow with the difference image vector, which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough. Then the l1-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles. The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data. Moreover, all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed.

Computational Spectral Imaging Based on Compressive Sensing

Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing （CS）. The spectral and spatial infor- mation is simultaneously obtained using a fiber spectrometer and the spatial light modulation without mechanical scanning. The method allows high-speed, stable, and sub sampling acquisition of spectral data from specimens. The relationship between sampling rate and image quality is discussed and two CS algorithms are compared.

Optimization-based image reconstruction in x-ray computed tomography by sparsity exploitation of local continuity and nonlocal spatial self-similarity

The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography（CT） reconstruction. A method employing the image gradient sparsity is often used to reduce the sampling rate and is shown to remove the unwanted artifacts while preserve sharp edges, but may cause blocky or patchy artifacts.To eliminate this drawback, we propose a novel sparsity exploitation-based model for CT image reconstruction. In the presented model, the sparse representation and sparsity exploitation of both gradient and nonlocal gradient are investigated.The new model is shown to offer the potential for better results by introducing a similarity prior information of the image structure. Then, an effective alternating direction minimization algorithm is developed to optimize the objective function with a robust convergence result. Qualitative and quantitative evaluations have been carried out both on the simulation and real data in terms of accuracy and resolution properties. The results indicate that the proposed method can be applied for achieving better image-quality potential with the theoretically expected detailed feature preservation.

Compressed sensing sparse reconstruction for coherent field imaging

Return signal processing and reconstruction plays a pivotal role in coherent field imaging, having a significant in- fluence on the quality of the reconstructed image. To reduce the required samples and accelerate the sampling process, we propose a genuine sparse reconstruction scheme based on compressed sensing theory. By analyzing the sparsity of the received signal in the Fourier spectrum domain, we accomplish an effective random projection and then reconstruct the return signal from as little as 10% of traditional samples, finally acquiring the target image precisely. The results of the numerical simulations and practical experiments verify the correctness of the proposed method, providing an efficient processing approach for imaging fast-moving targets in the future.

High speed ghost imaging based on a heuristic algorithm and deep learning

We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new condensed overlapped matrices are then designed to shorten and optimize encoding of the overlapped patterns, which are shown to be much superior to the random matrices. In addition, we apply deep learning to image the target, and use the signal acquired by the bucket detector and corresponding real image to train the neural network. Detailed comparisons show that our new method can improve the imaging speed by as much as an order of magnitude, and improve the image quality as well.

Computational ghost imaging with deep compressed sensing

Computational ghost imaging(CGI)provides an elegant framework for indirect imaging,but its application has been restricted by low imaging performance.Herein,we propose a novel approach that significantly improves the imaging performance of CGI.In this scheme,we optimize the conventional CGI data processing algorithm by using a novel compressed sensing(CS)algorithm based on a deep convolution generative adversarial network(DCGAN).CS is used to process the data output by a conventional CGI device.The processed data are trained by a DCGAN to reconstruct the image.Qualitative and quantitative results show that this method significantly improves the quality of reconstructed images by jointly training a generator and the optimization process for reconstruction via meta-learning.Moreover,the background noise can be eliminated well by this method.

Lensless complex amplitude demodulation based on deep learning in holographic data storage

To increase the storage capacity in holographic data storage(HDS),the information to be stored is encoded into a complex amplitude.Fast and accurate retrieval of amplitude and phase from the reconstructed beam is necessary during data readout in HDS.In this study,we proposed a complex amplitude demodulation method based on deep learning from a single-shot diffraction intensity image and verified it by a non-interferometric lensless experiment demodulating four-level amplitude and four-level phase.By analyzing the correlation between the diffraction intensity features and the amplitude and phase encoding data pages,the inverse problem was decomposed into two backward operators denoted by two convolutional neural networks(CNNs)to demodulate amplitude and phase respectively.The experimental system is simple,stable,and robust,and it only needs a single diffraction image to realize the direct demodulation of both amplitude and phase.To our investigation,this is the first time in HDS that multilevel complex amplitude demodulation is achieved experimentally from one diffraction intensity image without iterations.

Establishment of FAP-overexpressing Cells for FAP-targeted Theranostics

Objective Fibroblast activation protein(FAP)has been widely studied and exploited for its clinical applications.One of the difficulties in interpreting reports of FAP-targeted theranostics is due to the lack of accurate controls,making the results less specific and less confirmative.This study aimed to establish a pair of cell lines,in which one highly expresses FAP(HT1080-hFAP)and the other has no detectable FAP(HT1080-vec)as control,to accurately evaluate the specificity of the FAP-targeted theranostics in vitro and in vivo.Methods The cell lines of the experimental group(HT1080-hFAP)and no-load group(HT1080-vec)were obtained by molecular construction of the recombinant plasmid pIRES-hFAP.The expression of hFAP in HT1080 cells was detected by PCR,Western blotting and flow cytometry.CCK-8,Matrigel transwell invasion assay,scratch test,flow cytometry and immunofluorescence were used to verify the physiological function of FAP.The activities of human dipeptidyl peptidase(DPP)and human endopeptidase(EP)were detected by ELISA in HT1080-hFAP cells.PET imaging was performed in bilateral tumor-bearing nude mice models to evaluate the specificity of FAP.Results RT-PCR and Western blotting demonstrated the mRNA and protein expression of hFAP in HT1080-hFAP cells but not in HT1080-vec cells.Flow cytometry confirmed that nearly 95%of the HT1080-hFAP cells were FAP positive.The engineered hFAP on HT1080 cells had its ability to retain enzymatic activities and a variety of biological functions,including internalization,proliferation-,migration-,and invasion-promoting activities.The HT1080-hFAP xenografted tumors in nude mice bound and took up^(68)GA-FAPI-04 with superior selectivity.High image contrast and tumor-organ ratio were obtained by PET imaging.The HT1080-hFAP tumor retained the radiotracer for at least 60 min.Conclusion This pair of HT1080 cell lines was successfully established,making it feasible for accurate evaluation and visualization of therapeutic and diagnostic agents targeting the hFAP.

Tensorial tomographic Fourier ptychography with applications to muscle tissue imaging

We report tensorial tomographic Fourier ptychography(T^(2)oFu),a nonscanning label-free tomographic microscopy method for simultaneous imaging of quantitative phase and anisotropic specimen information in 3D.Built upon Fourier ptychography,a quantitative phase imaging technique,T^(2)oFu additionally highlights the vectorial nature of light.The imaging setup consists of a standard microscope equipped with an LED matrix,a polarization generator,and a polarization-sensitive camera.Permittivity tensors of anisotropic samples are computationally recovered from polarized intensity measurements across three dimensions.We demonstrate T^(2)oFu’s efficiency through volumetric reconstructions of refractive index,birefringence,and orientation for various validation samples,as well as tissue samples from muscle fibers and diseased heart tissue.Our reconstructions of healthy muscle fibers reveal their 3D fine-filament structures with consistent orientations.Additionally,we demonstrate reconstructions of a heart tissue sample that carries important polarization information for detecting cardiac amyloidosis.

Metasurface-based computational imaging:a review

Metasurface-based imaging has attracted considerable attention owing to its compactness,multifunctionality,and subwavelength coding capability.With the integration of computational imaging techniques,researchers have actively explored the extended capabilities of metasurfaces,enabling a wide range of imaging methods.We present an overview of the recent progress in metasurface-based imaging techniques,focusing on the perspective of computational imaging.Specifically,we categorize and review existing metasurface-based imaging into three main groups,including(i)conventional metasurface design employing canonical methods,(ii)computation introduced independently in either the imaging process or postprocessing,and(iii)an end-to-end computation-optimized imaging system based upon metasurfaces.We highlight the advantages and challenges associated with each computational metasurface-based imaging technique and discuss the potential and future prospects of the computational boosted metaimager.