Imaging Scheme Research Articles

Denoised single-pixel imaging in a Fourier acquisition mode.

The degradation of imaging quality caused by environmental noise during Fourier single pixel imaging (FSPI) is a big problem. In this paper, we propose simple and efficient denoised single-pixel imaging schemes by using linear filters to reduce the impact. Two filters, such as an average filter and Gaussian filter, are employed, and their corresponding schemes are named SCH-A and SCH-G, respectively. The experimental and simulation results show that both schemes can effectively reduce the impact of environmental noise and have greater robustness in comparison with those using the deringing SPI and conventional SPI. Compared with SCH-A, the reconstructed image by SCH-G keeps more details and edges. With the increase of template size, SCH-A and SCH-G have better filtering effects with the same variance. Meanwhile, the larger the template size is, the better filtering effect SCH-A has. This conclusion is also applicable to SCH-G under the same variance.

Abstract 4164: Hybrid photoacoustic imaging and fast ultrasound localization microscopy to probe the tumor microenvironment

Abstract Photoacoustic (PA) imaging can map the physiological conditions of tissues and track the biodistribution of contrast agents. Ultrasound localization microscopy (ULM) with microbubbles provides deep-tissue super-resolution blood vessel images and blood velocity maps. The integration of these techniques offers a potential tool for oncological applications, such as visualizing tumor hypoxia and vasculature. Additionally, because PA imaging and ULM can share the same ultrasound detector and data-acquisition system, PA images can seamlessly fuse with ULM images without requiring post co-registration processing. However, the acquisition time of ULM is considerably longer (several minutes per frame) compared to PA imaging (10 milliseconds to 100 milliseconds per frame), limiting the simultaneous acquisition of the two imaging modalities. To address this issue, we developed a deep learning model based on U-Net to accelerate the frame rate of ULM using information from sparse microbubble locations and power Doppler images. By enabling fast ULM acquisition, we developed an interleaved PA/fast ULM imaging technique to simultaneously capture photoacoustic and ULM images. Our experiments demonstrated up to a 16 times improvement in acquisition speed with U-Net, utilizing in vivo mouse brain and skin tumor data. The hybrid imaging sequence achieved super-resolution vascular and tissue oxygenation imaging with less than 2 seconds of acquisition time per frame in vivo. The implementation of this rapid dual imaging scheme enables the longitudinal monitoring of 3D tumor microvasculature and hypoxia conditions using a common linear ultrasound transducer. We monitored tumor growth of epidermoid carcinoma (A431) in a mouse model from Day 7 to Day 17 of tumor implantation, and the results reveal a significant decrease in tumor vasculature density and oxygen saturation as the tumor grew larger over time. The results indicate a positive relationship between oxygen saturation and vessel density. Our findings showcase the potential of this innovative technique for advancing deep tissue, high-resolution, multi-biological parameters imaging in oncological research. Citation Format: Shensheng Zhao, Sayantani Basu, Roy H. Campbell, Yang Zhao, Yun-Sheng Chen. Hybrid photoacoustic imaging and fast ultrasound localization microscopy to probe the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4164.

Data Hiding and Authentication Scheme for Medical Images Using Double POB

To protect the security of medical images and to improve the embedding ability of data in encrypted medical images, this paper proposes a permutation ordered binary (POB) number system-based hiding and authentication scheme for medical images, which includes three parts: image preprocessing, double hiding, and information extraction and lossless recovery. In the image preprocessing and double hiding phase, firstly, the region of significance (ROS) of the original medical image is segmented into a region of interest (ROI) and a region of non-interest (RONI). Then, the bit plane of the ROI and RONI are separated and cross-reorganization to obtain two new Share images. After the two new Share images are compressed, the images are encrypted to generate two encrypted shares. Finally, the embedding of secret data and attaching of authentication bits in each of these two encrypted shares was performed using the POB algorithm. In the information extraction and lossless recovery phase, the POBN algorithm is first used to extract the authentication bits to realize image tamper detection; then, the embedded secret message is extracted, and the original medical image is recovered. The method proposed in this research performs better in data embedding and lossless recovery, as demonstrated by experiments.

Invisibility enables super-visibility in electromagnetic imaging

This paper is concerned with the inverse electromagnetic scattering problem for anisotropic media. We use the interior resonant modes to develop an inverse scattering scheme for imaging the scatterer. The whole procedure consists of three phases. First, we determine the interior Maxwell transmission eigenvalues of the scatterer from a family of far-field data by the mechanism of the linear sampling method. Next, we determine the corresponding transmission eigenfunctions by solving a constrained optimization problem. Finally, based on both global and local geometric properties of the transmission eigenfunctions, we design an imaging functional which can be used to determine the shape of the medium scatterer. We provide rigorous theoretical basis for our method. Numerical experiments verify the effectiveness, better accuracy and super-resolution results of the proposed scheme.

Salt-and-pepper Noise Reduction for Medical Images Based on Image Fusion.

During the collection process, the prostate capsule is prone to introduce salt and pepper noise due to gastrointestinal peristalsis, which will affect the precision of subsequent object detection. A cascade optimization scheme for image denoising based on image fusion was proposed to improve the peak signal-to-noise ratio(PSNR) and contour protection performance of heterogeneous medical images after image denoising. Anisotropic diffusion fusion (ADF) was used to decompose the images denoised by adaptive median filter, non-local adaptive median filter and artificial neural network to generate the base layer and detail layer, which were fused by weighted average and Karhunen-Loeve Transform respectively. Finally, the image was reconstructed by linear superposition. Compared with the traditional denoising method, the image denoised by this method has a higher PSNR while maintaining the image edge contour. Using the denoised dataset for object detection, the detection precision of the obtained model is higher.

A lossless compression and encryption scheme for sequence images based on 2D-CTCCM, MDFSM and STP

A lossless compression and encryption scheme for sequence images based on 2D-CTCCM, MDFSM and STP

A novel stratification scheme combined with internal arteries in CT imaging for guiding postoperative adjuvant transarterial chemoembolization in hepatocellular carcinoma: a retrospective cohort study.

Although postoperative adjuvant transarterial chemoembolization (PA-TACE) improves survival outcomes in a subset of patients with resected hepatocellular carcinoma (HCC), the lack of reliable biomarkers for patient selection remains a significant challenge. The present study aimed to evaluate whether computed tomography imaging can provide more value for predicting benefits from PA-TACE and to establish a new scheme for guiding PA-TACE benefits. In this retrospective study, patients with HCC who had undergone preoperative contrast-enhanced computed tomography and curative hepatectomy were evaluated. Inverse probability of treatment weight was performed to balance the difference of baseline characteristics. Cox models were used to test the interaction among PA-TACE, imaging features, and pathological indicators. An HCC imaging and pathological classification (HIPC) scheme incorporating these imaging and pathological indicators was established. This study included 1488 patients [median age, 52 years (IQR, 45-61 years); 1309 male]. Microvascular invasion (MVI) positive, and diameter >5cm tumors achieved a higher recurrence-free survival (RFS), and overall survival (OS) benefit, respectively, from PA-TACE than MVI negative, and diameter ≤5cm tumors. Patients with internal arteries (IA) positive benefited more than those with IA-negative in terms of RFS ( P =0.016) and OS ( P =0.018). PA-TACE achieved significant RFS and OS improvements in HIPC3 (IA present and diameter >5 cm, or two or three tumors) patients but not in HIPC1 (diameter ≤5cm, MVI negative) and HIPC2 (other single tumor) patients. Our scheme may decrease the number of patients receiving PA-TACE by ~36.5% compared to the previous suggestion. IA can provide more value for predicting the benefit of PA-TACE treatment. The proposed HIPC scheme can be used to stratify patients with and without survival benefits from PA-TACE.

Культурный код и его образный компонент (на примере образа природы в английских фильмах)

The study aims to solve the problem of the image cultural identification within the bounds of the English national cultural code. The object of the study is concentrated around linguistic metaphors representing images of fire and water as a part of the English nature-morphic code in films about catastrophes and survival. In order to solve the problem, the two natural image schemes, the pyromorphic image scheme FIRE > EMOTIONS and the hydromorphic scheme WATER > EMOTIONS, which are subordinate to the general hyperscheme NATURE > PSYCHE, are examined in detail. The study is novel in that it is the first to focus on the natural-elementary imagery as a source of stereotypes in the national cultural code. In order to do so, the article involves the modern cognitive-psychological image paradigm, according to which the activation of an image vividly demonstrates secondary stereotypical comprehension of the emotional world through the world of nature. The results achieved demonstrate that natural metaphors are basic, original and unavoidable in the world of psychology, while their use is quite a powerful source for naming unknown and problematic modern phenomena, such as emotions, feelings and experiences.

Extended Kalman filtering for enhancing a cyclic pattern-updating scheme of single-pixel dynamic fluorescence spatial frequency domain imaging: publisher's note.

This publisher's note contains a correction to Opt. Lett.48, 5443 (2023)10.1364/OL.495099.

A Contraband Detection Scheme in X-ray Security Images Based on Improved YOLOv8s Network Model.

X-ray inspections of contraband are widely used to maintain public transportation safety and protect life and property when people travel. To improve detection accuracy and reduce the probability of missed and false detection, a contraband detection algorithm YOLOv8s-DCN-EMA-IPIO* based on YOLOv8s is proposed. Firstly, the super-resolution reconstruction method based on the SRGAN network enhances the original data set, which is more conducive to model training. Secondly, DCNv2 (deformable convolution net v2) is introduced in the backbone network and merged with the C2f layer to improve the ability of the feature extraction and robustness of the model. Then, an EMA (efficient multi-scale attention) mechanism is proposed to suppress the interference of complex background noise and occlusion overlap in the detection process. Finally, the IPIO (improved pigeon-inspired optimization), which is based on the cross-mutation strategy, is employed to maximize the convolutional neural network's learning rate to derive the optimal group's weight information and ultimately improve the model's detection and recognition accuracy. The experimental results show that on the self-built data set, the mAP (mean average precision) of the improved model YOLOv8s-DCN-EMA-IPIO* is 73.43%, 3.98% higher than that of the original model YOLOv8s, and the FPS is 95, meeting the deployment requirements of both high precision and real-time.

64 picosecond time resolved time-correlated single photon counting imaging.

High-speed imaging of dynamic scenes is a challenging and important task in many applications. However, conventional imaging methods based on charge coupled devices or complementary metal oxide semiconductors have limitations in temporal resolution and photon sensitivity. To address this problem, we propose a novel high-speed imaging scheme that combines single-pixel imaging with single photon detection and time-correlated single photon counting. Our scheme can achieve high-speed imaging with 64ps resolution by repeating the motion scenes and using binary outputs from single photon detectors. We demonstrate our scheme by reconstructing the switching process of a digital micro-mirror device and a liquid crystal spatial light modulator. Our scheme can be further improved to 1ps resolution by using a more accurate time-correlated single photon counting system. Moreover, our scheme can adapt to different speed scenes by adjusting the temporal resolution and reducing the sampling time. Our high temporal resolution imaging scheme further expands the application areas of single-pixel imaging and provides solutions for scenes requiring single photon detection and higher temporal resolution, such as reproducible chemical reaction processes imaging, cellular or sub-cellular bio imaging, single-molecule imaging of rotary motors, high-speed equipment inspection, and other periodic high-speed scenes imaging.

Projection–Subtraction X-ray Imaging Scheme for Studying Fast Fluid-Dynamics Processes in Porous Media

Projection–Subtraction X-ray Imaging Scheme for Studying Fast Fluid-Dynamics Processes in Porous Media

Analysis of multispectral polarization imaging image information based on micro-polarizer array.

As a new detection technology, polarization imaging is of great significance in the field of target detection. At present, polarization imaging technology usually adopts visible light polarization imaging. The technique is difficult to image the target in complex background due to its narrow working spectrum and short detection distance. Therefore, based on the principle of full Stokes micro-polarizer array, this paper proposes a multi-spectral polarization imaging scheme and designs a multi-spectral polarization imaging detection system penetrating haze. Conducting indoor and outdoor polarized imaging experiments. Finally, image quality was assessed using metrics such as information entropy (EN), average gradient (AG), and standard deviation (STD). The results show that compared with traditional strength detection, the imaging system has significantly improved detection distance and imaging quality in smoky environments. The imaging system can effectively enhance the contours and details of the target object and improve detection and recognition capabilities.

Improved beamforming schemes for estimation of multimode surface wave dispersion curves from seismic noise with reducing effect of the irregular array geometry and/or anisotropic source distribution

SUMMARY Dense array observation and seismic interferometry have revolutionized the imaging schemes of the earth structure. It is becoming possible to directly obtain the lateral variation of the earth's structure by applying array-based methods such as the cross-correlation beamforming (CBF) of the ambient noise to the subsets of the dense array, without tomography. CBF has been proven to extract the azimuth-averaged multimode surface wave dispersion curves. However, the resolution of the dispersion image generated by conventional CBF is low at high frequencies in the frequency–velocity (f-v) domain. Moreover, the irregular array geometry and uneven source distribution would bias the result of CBF, especially for the estimation of azimuth-dependent velocity. In this paper, two beamforming (BF) es are suggested to improve the resolution of multimode dispersion images in the f-v domain. First, the geometrical spreading of the wavefield is corrected to enhance the amplitude at high frequency (or large distance) and thereby improve the resolution of the dispersion image at high frequency. We call this scheme weighted correlation beamforming (WCBF). The azimuth-averaged velocity can be estimated with sufficient resolution using WCBF by stacking the BF output at each azimuth. We show that WCBF is the 2-D Fourier transform of the spatial wavefield from the viewpoint of the wavefield transform. Secondly, a modified beamforming scheme (MCBF) is suggested to reduce the effect of uneven source and/or irregular array geometry. The delay and summation in MCBF are performed only for plane waves incident from the stationary phase region. The azimuth-dependent velocity can therefore be estimated by MCBF with less dependence on the array geometry, as well as on the uneven source distribution. In terms of the estimation of azimuth-averaged phase velocity, we show the F-J method, another array-based method for extracting multimode surface waves from ambient noise using the Fourier–Bessel transform, is the azimuth-averaged version of WCBF. The reliability of WCBF and MCBF is verified based on the synthetic and field data using the array with different geometry. The dispersion image of multimode Rayleigh wave phase velocity at local and regional scales can be generated by WCBF or MCBF with high resolution. In particular, multimode dispersion curves at the local scale can be measured by MCBF with sufficient accuracy using quite short recordings from hours to days. This offers the possibility of a rapid assessment of the media properties.

Correlation of microstructure and hardness distribution of high-speed train wheels under original and service statuses

High-speed train wheels have complex service environments during wheel–rail contact, and a comprehensive understanding of the microstructural inhomogeneity at the rim is critical for service safety assessment. The local quenching process in the vicinity of the rim will cause a difference in microstructure, directly affecting the service performance of the material. In this study, high-throughput characterization method based on material genome engineering was used, the methods for characterising and identifying the microstructure of wheel steel were developed using high-throughput scanning electron microscopy, and the accuracy and reproducibility of the methods were evaluated. A point-to-point research scheme for microstructure image and hardness data was designed based on the concept of statistical mapping characterization. The area fraction of the proeutectoid ferrite and micro-Vickers hardness distribution in a large size range of the key area of the rim and their internal correlation was obtained, these helped avoid statistical and representativeness limitations of traditional local selection analysis of microstructure and hardness. The results indicates that the area fraction of proeutectoid ferrite and micro-Vickers hardness showed a clear gradient distribution, and opposite distribution trend, with a high negative correlation. In addition, by observing the correlation between microstructure and hardness, and studying the evolution of microstructure, it was found that the microstructure and hardness of the rim near the tread demonstrated a significant synergistic evolution law under the influence of service, this was observed both statistically and microscopically. These findings enhanced the application prospects of the microstructure-performance statistical mapping method, and were beneficial in efficiently evaluating the service life and safety of key components.

Run length encoding based reversible data hiding scheme in encrypted images

Run length encoding based reversible data hiding scheme in encrypted images

Adaptive Basis Function Method for the Detection of an Undersurface Magnetic Anomaly Target

The orthogonal basis functions (OBFs) method is a prevailing choice for the detection of undersurface magnetic anomaly targets. However, it requires the detecting platform or target to move uniformly along a straight path. To circumvent the restrictions, a new adaptive basis functions (ABFs) approach is proposed in this article. It permits the detection platform to search for a possible target at different speeds along any course. The ABFs are constructed using the real-time data of the onboard triaxial fluxgate, GPS module, and attitude gyro. Based on the pseudo-energy of an apparent target signal, the constant false alarm rate (CFAR) method is employed to judge whether a target is present. Moreover, by defining the pixel as a relative possibility for a target at a geographic location, a magnetic anomaly target imaging scheme is introduced by displaying the pixels onto the searching area. On-site experimental data are utilized to demonstrate the proposed approach. Compared with the traditional OBFs method, the present ABFs approach can substantially improve the detection possibility and reduce false alarms.

Retrospectively gated ultrashort-echo-time MRI T1 mapping reveals compromised pulmonary microvascular NO-dependent function in a murine model of acute lung injury.

This study sought to develop noninvasive, in vivo imaging schemes that allow for quantitative assessment of pulmonary microvascular functional status based on the combination of pulmonary T1 mapping and dynamic contrast-enhanced (DynCE) imaging. Ultrashort-echo-time (UTE) imaging at 9.4 T of lung parenchyma was performed. Retrospective gating was based on modulation of the first point in each recorded spoke. T1 maps were obtained using a series of five consecutive images with varying RF angles and analyzed with the variable flip angle approach. The obtained mean T1 lung value of 1078 ± 38 ms correlated well with previous reports. Improved intersession variability was observed, as evident from a decreased standard deviation of motion-resolved T1 mapping (F-test = 0.051). Animals received lipopolysaccharide (LPS) and were imaged at t= 2, 6, and 12 h after administration. The nitric oxide (NO)-dependent function was assessed according to changes in lung T1 after L-NAME injection, while microvascular perfusion and oxidant stress were assessed with contrast-enhanced imaging after injection of gadolinium or 3-carbamoyl-proxyl nitroxide radical, respectively. Retrospectivel gated UTE allowed robust, motion-compensated imaging that could be used for T1 mapping of lung parenchyma. Changes in lung T1 after L-NAME injection indicated that LPS induced overproduction of NO at t= 2 and 6 h after LPS, but NO-dependent microvascular function was impaired at t= 12 h after LPS. DynCE imaging at t= 6 h after LPS injection revealed decreased microvascular perfusion, with increased vascular permeability and oxidant stress. MRI allows to visualize and quantify lung microvascular NO-dependent function and its concomitant impairment during acute respiratory distress syndrome development with high sensitivity. UTE T1 mapping appears to be sensitive and useful in probing pulmonary microvascular functional status.

Optimal algorithm for color medical encryption and compression images based on DNA coding and a hyperchaotic system in the moments

Currently, visual data security plays a significant role in various fields, especially in medical imaging. Addressing the challenges associated with limited key space and vulnerability to different types of attacks within current encryption schemes, this work proposes an optimal compression-encryption scheme for large medical images that incorporates elements of Archimedes' optimization algorithm, discrete orthogonal Hahn moments, chaotic systems, and DNA coding. The primary aim of this study is to develop an optimal and exceptionally resilient compression-encryption scheme capable of countering various attack types effectively. This approach is structured into three principal phases: a compression phase harnessing the efficiencies of Hahn's discrete orthogonal moments (HMs) in signal and image representation, coupled with the Archimedes optimization algorithm (AO) to ensure optimal tuning of polynomial parameters (a, b) for superior image reconstruction quality. The encryption phase is performed on the compressed image, using hyperchaotic memristive 4-D (HCM-4D), adapted logistics map (ALM) and DNA coding. Initially, the adapted logistics map is responsible for generating random sequences linked to the compressed image. Subsequently, chaotic sequences originating from the hyperchaotic 4-D memristive system govern both random sequences and DNA processes. The optimization phase, facilitated by the AO algorithm, focuses on minimizing the value of the objective function (correlation) on the compressed and encrypted images. Ultimately, the image with the lowest correlation value is designated as the optimal compressed-encrypted representation.The simulation results clearly illustrate the resilience of the AO algorithm when juxtaposed with other optimization algorithms, especially with respect to convergence speed and computational efficiency. On the other hand, the proposed compression approach demonstrated exceptional efficiency in compressing medical images, offering us the possibility to achieve impressive compression ratio (CR) as well as exceptional quality in decompressed images, evident thanks to high PSNR values. In addition, security analyze demonstrate that the proposed compression-encryption benefits from a larger key space and superior resistance against different types of attacks. Furthermore, our approach was subjected to a comparative analysis alongside various encryption method. These comparisons demonstrate that our encryption algorithm surpasses others in terms of both security and effectiveness.

Single-pixel imaging based on metasurface fuzzycoding.

Single-pixel imaging, renowned for its high sensitivity, robustness against interference, and superior resolution, has become increasingly prominent in the field of optical research. Over recent years, a diverse array of light modulation devices and methodologies has been devised to accomplish megahertz modulations rates. This work presents a single-pixel imaging scheme based on the fuzzy coding of metasurfaces. This unique encoding technique manipulates the quality of the mask pattern by adjusting the pixel count within the metasurface units. Notably, we expand the metasurface units to effectively mitigate the position sensitivity during movement or rotations, thus easing the challenge for the detector in collecting the correct light intensity during sub-mask transitions. A detailed analysis is drawn of the reconstruction quality of fuzzy masks. Simultaneously, we provide simulations of single-pixel imaging under the condition where the fuzzy-coded metasurface is moving. This work provides a new, to the best of our knowledge, mask generation mode for high-speed spatial light modulation.