Model-driven CT reconstruction algorithm for nano-resolution x-ray phase contrast imaging

The low-density imaging performance of a zone plate-based nano-resolution hard x-ray computed tomography(CT)system can be significantly improved by incorporating a grating-based Lau interferometer. Due to the diffraction, however,the acquired nano-resolution phase signal may suffer splitting problem, which impedes the direct reconstruction of phase contrast CT(nPCT) images. To overcome, a new model-driven nPCT image reconstruction algorithm is developed in this study. In it, the diffraction procedure is mathematically modeled into a matrix B, from which the projections without signal splitting can be generated invertedly. Furthermore, a penalized weighted least-square model with total variation(PWLSTV) is employed to denoise these projections, from which nPCT images with high accuracy are directly reconstructed.Numerical experiments demonstrate that this new algorithm is able to work with phase projections having any splitting distances. Moreover, results also reveal that nPCT images of higher signal-to-noise-ratio(SNR) could be reconstructed from projections having larger splitting distances. In summary, a novel model-driven nPCT image reconstruction algorithm with high accuracy and robustness is verified for the Lau interferometer-based hard x-ray nano-resolution phase contrast imaging.

Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-imagefree phase retrieval from single-shot hologram

Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.

Optical design of a novel near-infrared phase contrast imaging(NI-PCI)diagnostic on the HL-2A tokamak

The optical design of near-infrared phase contrast imaging(NI-PCI)diagnosis on HL-2A is introduced in this paper.This scheme benefits from the great progress of near-infrared laser technology and is a broadening of traditional phase contrast technology.This diagnostic can work as a keen tool to measure plasma wavenumber spectra by inferring string-integrated plasma density fluctuations.Design of both the front optical path which is the path before the laser transmitting into the tokamak plasma and the rear optics which is the path after the laser passing through the plasma is detailed.The 1550 nm laser is chosen as the probe beam and highprecision optical components are designed to fit the laser beam,in which a phase plate with a 194-nm-deep silver groove is the key.Compared with the conventional 10.6μm laser-based PCI system on HL-2A,NI-PCI significantly overcomes the unwanted phase scintillation effect and promotes the measurement capability of high-wavenumber turbulence with an increased maximal measurable wavenumber from 15 cm^(-1)to 32.6 cm^(-1).

Theory and verification of moiréfringes for x-ray three-phase grating interferometer

Dual-phase and three-phase grating x-ray interference is a promising new technique for grating-based x-ray differential phase contrast imaging.Dual-phase grating interferometers have been relatively completely studied and discussed.In this paper,the corresponding imaging fringe formula of the three-phase grating interferometer is provided.At the same time,the similarities and differences between the three-phase grating interferometer and the dual-phase grating interferometer are investigated and verified,and that the three-phase grating interferometer can produce large-period moiréfringes without using the analyzing grating is demonstrated experimentally.Finally,a simple method of designing three-phase grating and multi-grating imaging systems from geometric optics based on the thin-lens theory of gratings is presented.These theoretical formulas and experimental results provide optimization tools for designing three-phase grating interferometer systems.

Robust and Discriminative Feature Learning via Mutual Information Maximization for Object Detection in Aerial Images

Object detection in unmanned aerial vehicle(UAV)aerial images has become increasingly important in military and civil applications.General object detection models are not robust enough against interclass similarity and intraclass variability of small objects,and UAV-specific nuisances such as uncontrolledweather conditions.Unlike previous approaches focusing on high-level semantic information,we report the importance of underlying features to improve detection accuracy and robustness fromthe information-theoretic perspective.Specifically,we propose a robust and discriminative feature learning approach through mutual information maximization(RD-MIM),which can be integrated into numerous object detection methods for aerial images.Firstly,we present the rank sample mining method to reduce underlying feature differences between the natural image domain and the aerial image domain.Then,we design a momentum contrast learning strategy to make object features similar to the same category and dissimilar to different categories.Finally,we construct a transformer-based global attention mechanism to boost object location semantics by leveraging the high interrelation of different receptive fields.We conduct extensive experiments on the VisDrone and Unmanned Aerial Vehicle Benchmark Object Detection and Tracking(UAVDT)datasets to prove the effectiveness of the proposed method.The experimental results show that our approach brings considerable robustness gains to basic detectors and advanced detection methods,achieving relative growth rates of 51.0%and 39.4%in corruption robustness,respectively.Our code is available at https://github.com/cq100/RD-MIM(accessed on 2 August 2024).

A Second-Order Image Denoising Model for Contrast Preservation

In this work,we propose a second-order model for image denoising by employing a novel potential function recently developed in Zhu(J Sci Comput 88:46,2021)for the design of a regularization term.Due to this new second-order derivative based regularizer,the model is able to alleviate the staircase effect and preserve image contrast.The augmented Lagrangian method(ALM)is utilized to minimize the associated functional and convergence analysis is established for the proposed algorithm.Numerical experiments are presented to demonstrate the features of the proposed model.

Laser speckle contrast imaging to predict the effect of temporary spinal cord stimulation in postherpetic neuralgia patients: A prospective observational study

Temporary spinal cord stimulation(tSCS)can effectively reduce the pain and severity of postherpetic neuralgia(PHN).However,there are no effective and objective methods for predicting the effects of tSCS on PHN.Laser speckle contrast imaging(LSCI)is frequently used in neurology to evaluate the effectiveness of treatment.To assess the accuracy of LSCI in predicting the impact of tSCS on PHN,14 adult patients receiving tSCS treatments for spinal nerve-innervated(C6-T2)PHN participated in this observational study.Visual analog scale(VAS)assessments and LSCI bloodflow images of the-ngers were recorded after the tSCS procedure.The results showed that the VAS scores of all patients decreased signi-cantly.Moreover,the bloodflow index(BFI)values were signi-cantly higher than they were before the procedure.Increased bloodflow and pain alleviation were positively correlated.The-ndings indicated that spinal nerve PHN(C6-T2)was signi-cantly reduced by tSCS.Pain alleviation by tSCS was positively correlated with increased bloodflow in the hand.The effect of tSCS on PHN may thus be predicted using an independent and consistent indicator such as LSCI.

Region-Aware Fashion Contrastive Learning for Unified Attribute Recognition and Composed Retrieval

Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing methods cannot recognize newly added attributes and may fail to capture region-level visual features.To address the aforementioned issues,a region-aware fashion contrastive language-image pre-training(RaF-CLIP)model was proposed.This model aligned cropped and segmented images with category and multiple fine-grained attribute texts,achieving the matching of fashion region and corresponding texts through contrastive learning.Clothing retrieval found suitable clothing based on the user-specified clothing categories and attributes,and to further improve the accuracy of retrieval,an attribute-guided composed network(AGCN)as an additional component on RaF-CLIP was introduced,specifically designed for composed image retrieval.This task aimed to modify the reference image based on textual expressions to retrieve the expected target.By adopting a transformer-based bidirectional attention and gating mechanism,it realized the fusion and selection of image features and attribute text features.Experimental results show that the proposed model achieves a mean precision of 0.6633 for attribute recognition tasks and a recall@10(recall@k is defined as the percentage of correct samples appearing in the top k retrieval results)of 39.18 for composed image retrieval task,satisfying user needs for freely searching for clothing through images and texts.

Near-Infrared Fluorescence Imaging Contrast Agents for Clinical Research: Limitations and Alternatives

Introduction: Near-infrared fluorescence imaging is a technique that will establish itself in the short term at the international level because it is recognized for its potential to improve the performance of surgical interventions, its moderate investment and operating costs and its portability. Although the technology is now mature, there is currently the problem of the availability of contrast agents to be injected IV. The aim of this methodology article is to propose an alternative solution to the need for contrast agents for clinical research, particularly in oncology. Methodology: They consist of coupling a fluorescent marker in the form of an NHS derivative, such as IR DYE manufactured in compliance with GMP, with therapeutic monoclonal antibodies having marketing authorization for molecular imaging. For a given antibody, the marking procedure must be the subject of a validation file on the final preparation filtered on a sterilizing membrane at 0.22 μm. Once the procedure has been validated, it would be unnecessary to repeat the tests before each clinical research examination. A check of the marking by thin-layer chromatography (TLC) and place it in a sample bank at +4˚C for 1 month of each injected formulation would be sufficient for additional tests if necessary. Conclusion: Molecular near-infrared fluorescence imaging is experiencing development, the process of which could be accelerated by greater availability of clinical contrast agents. Alternative solutions are therefore necessary to promote clinical research in this area. These methods must be shared to make it easier for researchers.

Improving millimeter-wave imaging quality using the vortex phase method

This paper investigates a new vortex wave imaging approach to improve the imaging quality of small metal targets of size less than 1.5 mm.Antennas with different spiral phase plates are designed to efficiently transmit vortex beams with orbital angular momentums(OAMs).By analyzing the OAM spectrum of the target,it was discovered that the predominant reflection contains a particular OAM mode that carries abundant azimuthal information.This can be explained by the OAM selectivity of the target and the guidance of the vortex transmitting beam.A simple reflection vortex imaging system was designed to capture the phase information.Measurement results show that the high image contrast reaches 14.9%,which is twice as high as that of the imaging without OAM.Both of simulations and experiments demonstrate that the vortex phase imaging approach proposed in this paper can effectively improve the imaging quality at 80 GHz.This approach is suitable for other millimeter wave imaging systems and is helpful to improve the resolution in anti-terrorism security checks.

Is it a normal phenomenon for pediatric patients to have brain leptomeningeal contrast enhancement on 3-tesla magnetic resonance imaging?

Determining whether sevoflurane sedation in children leads to“pseudo”prominent leptomeningeal contrast enhancement(pLMCE)on 3 Tesla magnetic resonance imaging will help reduce overdiagnosis by radiologists and clarify the pathophysiological changes of pLMCE.

Contrastive Learning for Blind Super-Resolution via A Distortion-Specific Network

Previous deep learning-based super-resolution(SR)methods rely on the assumption that the degradation process is predefined(e.g.,bicubic downsampling).Thus,their performance would suffer from deterioration if the real degradation is not consistent with the assumption.To deal with real-world scenarios,existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme.However,degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors.In this paper,we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples,respectively.Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space.Furthermore,instead of estimating the degradation,we extract global statistical prior information to capture the character of the distortion.Considering the coupling between the degradation and the low-resolution image,we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions.We term our distortion-specific network with contrastive regularization as CRDNet.The extensive experiments on synthetic and realworld scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.

Binary Phase Shaping and SHG Contrast Ratios

Finding binary sequences with Large SHG ratios is very important in the field of ultrafast science, biomedical optics, high-resolution microscopy and label-free imaging. In this paper, we have demonstrated the relation between the SHG contrast ratio and the traditional Merit Factor values. And in the light from known results in Merit Factor Problems, we have shown that Legendre Sequences or Jacobi Sequences, are still the best candidates to obtain binary sequences with large SHG contrast ratios. The authors also discussed the SHG behaviors on some sequences obtained from cyclotomic classes over the finite field GF (2l) .

A new method for information retrieval in two-dimensional grating-based X-ray phase contrast imaging

Grating-based X-ray phase contrast imaging has been demonstrated to he an extremely powerful phase-sensitive imaging technique. By using two-dimensional （2D） gratings, the observable contrast is extended to two refraction directions. Recently, we have developed a novel reverse-projection （RP） method, which is capable of retrieving the object information efficiently with one-dimensional （1D） grating-based phase contrast imaging. In this contribution, we present its extension to the 2D grating-based X-ray phase contrast imaging, named the two-dimensional reverse- projection （2D-RP） method, for information retrieval. The method takes into account the nonlinear contributions of two refraction directions and allows the retrieval of the absorption, the horizontal and the vertical refraction images. The obtained information can be used for the reconstruction of the three-dimensionak phase gradient field, and for an improved phase map retrieval and reconstruction. Numerical experiments are carried out, and the results confirm the validity of the 2D-RP method.

CNN-Transformer结合对比学习的高光谱与LiDAR数据协同分类

针对高光谱图像(hyperspectral images,HSI)与LiDAR数据多模态分类任务中的跨模态信息表达和特征对齐等问题,提出一种基于对比学习CNN-Transformer高光谱和LiDAR数据协同分类网络(Contrastive Learning based CNNTransformer Network,CLCT-Net)。CLCT-Net通过由ConvNeXt V2 Block构成的共有特征提取模块,获得不同模态间的共性特征,解决异构传感器数据之间语义对齐的问题。构建了包含空间-通道分支和光谱上下文分支的双分支HSI编码器,以及结合频域自注意力机制的LiDAR编码器,以获取更丰富的特征表示。利用集成对比学习进行分类,进一步提升多模态数据协同分类的精度。在Houston 2013和Trento数据集上的实验结果表明,相较于其他高光谱图像和Li‐DAR数据分类模型,本文所提模型获得了更高的地物分类精度,分别达到了92.01%和98.90%,实现了跨模态数据特征的深度挖掘和协同提取。

Double contrast-enhanced two-dimensional and three-dimensional ultrasonography for evaluation of gastric lesions

AIM： To investigate the value of two-dimensional （2D） and three-dimensional （3D） double contrast-enhanced ultrasonography （DCUS） imaging for evaluation of gas- tric lesions. METHODS： 2D and 3D DCUS imaging with both oral and intravenous administrations of contrast agents was used to assess gastroscopiclly-confirmed gastric lesions in 46 patients with benign and malignant diseases. Initially, liquid-based ultrasound contrast agent （Xinzhang） was given orally at dose of 500-600 mL for conventional ultrasound examination of the gastric lesions, and then a microbubble-based contrast agent （SonoVue） was injected intravenously at dose of 1.2-2.4 mL in bolus fashion to assess the perfusion pattern of the lesions using contrast imaging modes. The parameters derived from time-intensity curves including the arrival time （AT）, time to peak （lq-P）, peak intensity （PI） and en- hanced intensity （EI） were measured on the 2D DCUS imaging. 3D DCUS of the lesions was acquired to dem- onstrate the value of this imaging mode. RESULTS： There were 22 cases with benign lesions including chronic gastritis （n= 5）, gastric ulcer （n = 9）, gastric polyps （n = 3）, gastric stromal tumors （n = 5）, and 24 cases with malignant lesions including gastric cancer （n = 20）, gastric cardia carcinoma （n = 3） and post-operative recurrent gastric cancer （n = 1） in the study, The oral contrast-enhanced ultrasonography （CEUS） imaging of the stomach clearly demonstrated the anatomy of the stomach and morphologic features of gastric lesions, With optimal scanning window and imaging display under oral CEUS, intravenous CEUS clearly showed the perfusion of gastric lesions with various characteristic manifestations. Both 2D and 3D DCUS images clearly demonstrated normal gastric wall as a three-layer structure, from the inside out, hypere- choic mucosa, hypoechoic muscularis and hyperechoic serosa, respectively. There were statistical significant differences of AT （8.68±2.06 vs 10.43 ±2.75, P = 0.017）, PI （34.64 ± 6.63 vs 29.58 ± 8.22, P = 0.023） and EI （29.72 ± 6.69 vs 22.66 ± 7.01, P = 0.001） between malignant lesions and normal gastric wall. However, no differences of AT, PI and EI between benign lesions and normal gastric wall tissue were found. 3D DCUS could intuitively display morphological features and vascularities of the lesions with multiplanar and volume views. 3D DCUS imaging provided comprehen- sive information complementary to 2D imaging. The crater or wellhead appearances and feeding vessels as well as distorted nourishing vasculature of gastric carcinoma were better seen with 3D imaging than 2D imaging. CONCLUSION： DCUS imaging can simultaneouslydisplay the anatomic and perfusion features of gastric lesions. 3D DCUS can provide additional information to 2D DCUS for evaluation of gastric lesions.

Evaluation of quantitative contrast harmonic imaging to assess malignancy of liver tumors:A prospective controlled two-center study

AIM: To establish the extent to which contrast enhancement with SonoVue in combination with quantitative evaluation of contrast-medium dynamics facilitates the detection of hepatic tumors. METHODS: One hundred patients with histologically confirmed malignant or benign hepatic tumor (maximum size 5 cm) were analyzed. Contrast-enhanced ultrasound (bolus injection 2.5 mL SonoVue) was carried out with intermittent breath-holding technique using a multifrequency transducer (2.5-4 MHz). Native vascularization was analyzed with power Doppler. The contrast-enhanced dynamic ultrasound investigation was carried out with contrast harmonic imaging in true detection mode during the arterial,portal venous and late phases. Mechanical index was set at 0.15. Perfusion analysis was performed by post-processing of the raw data time intensity curve (TIC) analysis. The cut-off of the gray value differences between tumor and normal liver tissue was established using Receiver Operating Characteristic (ROC) analysis 64-line multi-slice computed tomography served as reference method in all cases. Magnetic resonance tomography was used additionally in 19 cases. RESULTS: One hundred patients with 59 malignant (43 colon,5 breast,2 endocrine metastases,7 hepatocellular carcinomas and 2 kidney cancers) and 41 benign (15 hemangiomas,7 focal nodular hyperplasias,5 complicated cysts,2 abscesses and 12 circumscribed fatty changes) tumors were included. The late venous phase proved to be the most sensitive for classification of the tumor type. Fifty-eight of the 59 malignant tumors were classified as true positive,and one as false negative. This resulted in a sensitivity of 98.3%. Of the 41 benign tumors,37 were classified as true negative and 4 as false negative,which corresponds to a specificity of 90.2%. Altogether,95.0% of the diagnoses were classified as correct on the basis of the histological classification. No investigator-dependency (P = 0.23) was noted. CONCLUSION: The results show the possibility of accurate prediction of malignancy of hepatic tumors with a positive prognostic value of 93.5% using advanced contrast-enhanced ultrasound. Contrast enhancement with SonoVue in combination with quantitative evaluation of contrast-medium dynamics is a valuable tool to discriminate hepatic tumors.

A new method of detecting interferogram in differential phase-contrast imaging system based on special structured x-ray scintillator screen

An x-ray scintillator screen with a special structure, functioning as detector and analyser grating, was proposed for collecting the interferogram of differential phase contrast imaging without absorption grating and difficulty of fabrication by a state of the art technique. On the basis of phase grating diffraction, a detecting model of the scintillator screen was built for analysing the phase and absorption information of objects. According to the analysis, a new method of phase retrievals based on two-images and the optimal structure of screen were presented.

Ultrasonic Scattered Field Distribution of One and Two Cylindrical Solids with Phased Array Technique

The scattered fields of plane waves in a solid from a cylinder or sphere are critical in determining its acoustic characteristics as well as in engineering applications. This paper investigates the scattered field distributions of different incident waves created by elastic cylinders embedded in an elastic isotropic medium. Scattered waves, including longitudinal and transverse waves both inside and outside the cylinder, are described with specific modalities under an incident plane wave. A model with a scatterer embedded in a structural steel matrix and filled with aluminum is developed for comparison with the theoretical solution. The frequency of the plane wave ranged from 235 kHz to 2348 kHz, which corresponds to scaling factors from 0.5 to 5. Scattered field distributions in matrix materials blocked by an elastic cylindrical solid have been obtained by simulation or calculated using existing parameters. The simulation results are in good agreement with the theoretical solution, which supports the correctness of the simulation analysis. Furthermore, ultrasonic phased arrays are used to study scattered fields by changing the characteristics of the incident wave. On this foundation, a partial preliminary study of the scattered field distribution of double cylinders in a solid has been carried out, and the scattered field distribution at a given distance has been found to exhibit particular behaviors at different moments. Further studies on directivities and scattered fields are expected to improve the quantification of scattered images in isotropic solid materials by the phased array technique.

融合序列变分Transformer与对比学习的多样化图像描述生成

近年来,基于Transformer的图像描述生成模型取得了显著的性能提升。然而,现有方法性能严重依赖预定义的指标或交叉熵损失,难以生成自然而多样的描述语句。引入一种序列变分Transformer模型,用于多样化图像描述生成。通过最大化图像模态内互信息的变分下界,缓解模式坍塌问题。最大化图像-文本多模态间的互信息,将序列变分Transformer模型与对比学习无缝集成,从而进一步增强序列变分编码器的表征学习能力,促进多样化描述的生成。在MSCOCO标准数据集上进行了定量和定性实验,在随机生成100个描述语句时,与当前最优结果相比,准确性指标CIDEr(consensus-based image description evaluation)提升了5.5%,多样性指标Div-2(2-gram diversity)提升了10.5%。