High-performance Imaging Research Articles

Heterointerface Design of Perovskite Single Crystals for High-Performance X-Ray Imaging.

Metal halide perovskite single crystals (MHP-SCs) are known for their facile fabrication into large sizes using inexpensive solution methods. Owing to their combination of large mobility-lifetime products and strong X-ray absorption, they are considered promising materials for efficient X-ray detection. However, they suffer from large dark currents and severe ion migration, which limit their sensitivity and stability in critical X-ray detection applications. Herein, a heterointerface design is proposed to reduce both the dark current and ion migration by forming a heterojunction. In addition, the carrier transport performance is significantly improved using heterointerface engineering by designing a gradient band structure in the SCs. The SC heterojunction detectors exhibit a high sensitivity of 3.98 × 105 µC Gyair -1 cm-2 with a low detection limit of 12.2 nGyair s-1 and a high spatial resolution of 10.2 lp mm-1 during imaging. These values are among the highest reported for state-of-the-art MHP X-ray detectors. Moreover, the detectors show excellent stability under continuous X-ray irradiation and maintainclear X-ray imaging after 240 d. This study provides novel insights into the design and fabrication of X-ray detectors with high detection efficiency and stability, which are beneficial for developing inexpensive, high-resolution X-ray imaging equipment.

Research on a High-Performance Rock Image Classification Method

Efficient and convenient rock image classification methods are important for geological research. They help in identifying and categorizing rocks based on their physical and chemical properties, which can provide insights into their geological history, origin, and potential uses in various applications. The classification and identification of rocks often rely on experienced and knowledgeable professionals and are less efficient. Fine-grained rock image classification is a challenging task because of the inherent subtle differences between highly confusing categories, which require a large number of data samples and computational resources, resulting in low recognition accuracy, and are difficult to apply in mobile scenarios, requiring the design of a high-performance image processing classification architecture. In this paper we design a knowledge distillation and high-accuracy feature localization comparison network (FPCN)-based learning architecture for generating small high-performance rock image classification models. Specifically, for a pair of images, we interact with the feature vectors generated from the localized feature maps to capture common and unique features, let the network focus on more complementary information according to the different scales of the objects, and then the important features of the images learned in this way are made available for the micro-model to learn the critical information for discrimination via model distillation. The proposed method improves the accuracy of the micro-model by 3%.

Effect of Red Light on the Expression of the Phytochrome Gene Family and the Accumulation of Glycoside Alkaloids in Potatoes

Potatoes are the fourth major food crop in the world. Higher levels of glycoside alkaloids (GAs) lead to detrimental effects on the edibility and processing qualities. GAs are largely influenced by light; however, the mechanisms of this regulation by light are not well understood. By analyzing the bioinformatics of the phytochrome genes (PHYs) in potatoes, its expression level, the content of GAs and the correlation between them under different lights, this study aims to reveal the specific mechanism of light-regulated GAs accumulation and provide a theoretical basis for improved potato processing. Results based on high-performance liquid chromatography and imaging mass microscopy showed that red light induced a significant increase in α-chaconine and α-solanine accumulation compared to white light, but there was almost no accumulation in the dark within 12 days. Meanwhile, a bioinformatic analysis of PHY gene family members was performed, and the results showed that the five StPHYs were distributed on chromosomes 1, 2, 5, 7 and 10, with amino acid counts ranging from 704 to 1130. StPHYs genes have abundant light-responsive elements. Also, the expression patterns of StPHYs were dramatically induced by red light. Additionally, a correlation analysis showed that the GAs accumulation was significantly correlated with StPHYs expression. This research is useful for comprehending the metabolism of GAs regulated by light and monitoring food safety in potatoes.

Deep-learning based multi-scale computational ghost imaging for high-performance complex image recovery

In recent years, deep-learning technology has been used in computational ghost imaging (CGI) to significantly lower the sampling rate (SR) and increase imaging efficiency. The network will be able to extract fewer target features from the bucket detector (BD) values because of the decrease in information input at a very low SR, and it will be challenging for the network to image. For complicated targets, deep learning based computational ghost imaging (DL-CGI) will difficultly recovery high-quality targets. Our method uses multi-scale random patterns in the CGI to obtain the target's details and contour, information, and it feeds the network's richer target information with a small amount of data so that it can be better trained to recover complex targets with high quality at a lower SR. Additionally, a multi-input network is created, and the network is excellent for our sampling method. The results demonstrate that complex targets can be recovered with high quality at an extremely lower SR of just 2 %. Our method also exhibits good generalization, it can still obtain promising results for the targets with varying complexities, although they have not been trained before. The issue of challenging to recover complex targets at low SR is resolved by our system. It has great significance for the CGI's practical application.

Efficient image reconstruction for a small animal PET system with dual-layer-offset detector design.

A compact PET/SPECT/CT system Inliview-3000B has been developed to provide multi-modality information on small animals for biomedical research. Its PET subsystem employed a dual-layer-offset detector design for depth-of-interaction capability and higher detection efficiency, but the irregular design caused some difficulties in calculating the normalization factors and the sensitivity map. Besides, the relatively larger (2mm) crystal cross-section size also posed a challenge to high-resolution image reconstruction. We present an efficient image reconstruction method to achieve high imaging performance for the PET subsystem of Inliview-3000B. List mode reconstruction with efficient system modeling was used for the PET imaging. We adopt an on-the-fly multi-ray tracing method with random crystal sampling to model the solid angle, crystal penetration and object attenuation effect, and modify the system response model during each iteration to improve the reconstruction performance and computational efficiency. We estimate crystal efficiency with a novel iterative approach that combines measured cylinder phantom data with simulated line-of-response (LOR)-based factors for normalization correction before reconstruction. Since it is necessary to calculate normalization factors and the sensitivity map, we stack the two crystal layers together and extend the conventional data organization method here to index all useful LORs. Simulations and experiments were performed to demonstrate the feasibility and advantage of the proposed method. Simulation results showed that the iterative algorithm for crystal efficiency estimation could achieve good accuracy. NEMA image quality phantom studies have demonstrated the superiority of random sampling, which is able to achieve good imaging performance with much less computation than traditional uniform sampling. In the spatial resolution evaluation based on the mini-Derenzo phantom, 1.1mm hot rods could be identified with the proposed reconstruction method. Reconstruction of double mice and a rat showed good spatial resolution and a high signal-to-noise ratio, and organs with higher uptake could be recognized well. The results validated the superiority of introducing randomness into reconstruction, and demonstrated its reliability for high-performance imaging. The Inliview-3000B PET subsystem with the proposed image reconstruction can provide rich and detailed information on small animals for preclinical research.

N-type organic phototransistors based on PTCDA single crystals for broadband imaging

Organic single crystals (OSCs) herald new opportunities for the fabrication of advanced optoelectronic devices, especially for flexible and wearable devices, owing to their unique material properties including high mobility and intrinsic flexibility. Up to now, though the electrical characteristics have been a breakthrough in OSCs, the devices have not been fully optimized for inherent photoelectric properties, and their photoresponse characteristics are also scarcely satisfactory, especially for the n-type organic materials. In this work, we prepared a high-performance organic phototransistor employing the air-stable PTCDA single crystal, grown by a simple microspacing in-air sublimation technique. The responsivity and photosensitivity of the device can reach up to 180 A/W and 103 for 532 nm illumination, with also a good response speed (rise and decay times less than 1 and 2 ms). In addition, we demonstrated several high-resolution imaging in this organic transistor. Our results demonstrated that the high-quality n-type organic single crystal is also a promising candidate for future high-performance photodetector and imaging applications.

Development and performance evaluation of enhanced image dehazing method using deep learning networks

In this work, a deep learning-based high-performance image dehazing technique is proposed for image processing applications. The end-to-end network model is constructed and implemented using a dehazing network, discriminator network, and fine-tuning network models. These three methods are well-trained individually using appropriate datasets. The individual network models are integrated as an end-to-end network model to enhance the dehazing process. The applied input hazy image is processed by the dehazing network model using the estimation of transmission map and atmospheric light along with parallel convolution layers. A discriminated dehazing image was extracted from the discrimination network. Finally, fine-tuning is carried out based on the results of the discriminator network model. Various hazy images from different datasets are collected and applied to the proposed model, and performance metrics such as PSNR, SSIM, and MSE are evaluated. Qualitative, and quantitative comparison and analysis are carried out between the proposed learning-based image dehazing and existing dehazing methods. The average PSNR value of the proposed dehazing model is obtained by a maximum of 40.7 %, and a minimum of 1.34 %, when compared to the existing works. The average SSIM of the proposed work is increased by a maximum of 22.12 %, and a minimum of 3.38 % with respect to the existing works. The maximum average value of MSE for the proposed model is decreased by 72.6 % and the minimum decrease of MSE is 4.08 % when compared to the state-of-art-works.

Lattice-Gradient Perovskite KTaO3 Films for an Ultrastable and Low-Dose X-Ray Detector.

Conventional indirect X-ray detectors employ scintillating phosphors to convert X-ray photons into photodiode-detectable visible photons, leading to low conversion efficiencies, low spatial resolutions, and optical crosstalk. Consequently, X-ray detectors that directly convert photons into electric signals have long been desired for high-performance medical imaging and industrial inspection. Although emerging hybrid inorganic-organic halide perovskites, such as CH3 NH3 PbI3 and CH3 NH3 PbBr3 , exhibit high sensitivity, they have salient drawbacks including structural instability, ion motion, and the use of toxic Pb. Here, this work reports an ultrastable, low-dose X-ray detector comprising KTaO3 perovskite films epitaxially grown on a Nb-doped strontium titanate substrate using a low-cost solution method. The detector exhibits a stable photocurrent under high-dose irradiation, high-temperature (200 °C), and aqueous conditions. Moreover, the prototype KTaO3 -film-based detector exhibits a 150-fold higher sensitivity (3150 µC Gyair -1 cm-2 ) and 150-fold lower detection limit (<40 nGyair s-1 ) than those of commercial α-Se-based direct detectors. Systematic investigations reveal that the high stability of the detector originates from the strong covalent bonds within the KTaO3 film, whereas the low detection limit is due to a lattice-gradient-driven built-in electric field and the high insulating property of KTaO3 film. This study unveils a new path toward the fabrication of green, stable, and low-dose X-ray detectors using oxide perovskite films, which have significant application potential in medical imaging and security operations.

Polarization differential interference contrast microscopy with physics-inspired plug-and-play denoiser for single-shot high-performance quantitative phase imaging.

We present single-shot high-performance quantitative phase imaging with a physics-inspired plug-and-play denoiser for polarization differential interference contrast (PDIC) microscopy. The quantitative phase is recovered by the alternating direction method of multipliers (ADMM), balancing total variance regularization and a pre-trained dense residual U-net (DRUNet) denoiser. The custom DRUNet uses the Tanh activation function to guarantee the symmetry requirement for phase retrieval. In addition, we introduce an adaptive strategy accelerating convergence and explicitly incorporating measurement noise. After validating this deep denoiser-enhanced PDIC microscopy on simulated data and phantom experiments, we demonstrated high-performance phase imaging of histological tissue sections. The phase retrieval by the denoiser-enhanced PDIC microscopy achieves significantly higher quality and accuracy than the solution based on Fourier transforms or the iterative solution with total variance regularization alone.

Study of Proton Radiation Damage Effects in Si microstrip Detector for New and Fast Tracking System of pCT

A new and fast radiation hard p-Si micro strip detector requires for the high proton imaging performance using the Proton Computed Tomography (pCT) system. Silicon trackers of the pCT have been measured the actual proton dose delivered to the cancer patient during the medical treatment. In this paper, several proton cancer doses up to 9.62x 1014 neq,/cm2 are used for the SRH modelling of the full depletion voltage and leakage current at 293 K of the Si micro-strip detector using microscopic radiation damage model. Radiation hard thin (150μm) p-type MCz Si micro strip detector design is proposed for the pCT that can work up to 800V for very high-irradiated proton cancer doses.

A Snapshot Imaging System for the Measurement of Solar-Induced Chlorophyll Fluorescence— Addressing the Challenges of High-Performance Spectral Imaging

A Snapshot Imaging System for the Measurement of Solar-Induced Chlorophyll Fluorescence— Addressing the Challenges of High-Performance Spectral Imaging

High performance image steganography integrating IWT and Hamming code within secret sharing

AbstractSteganography and secret sharing are schemes to increase the security of private information against attackers. Steganography emphasizes secrecy while secret sharing distributes the secret key in shares that are conditionally classified to reconstruct the original secret. This paper introduces a counting‐based secret sharing scheme that aims to reduce the computational complexity for longer keys, thereby providing a practical steganographic approach for efficient sharing. The scheme integrates counting‐based secret sharing with integer wavelet transform (IWT) and steganography. The subscriptions created using the Hamming code are embedded in the cover image. Using this method, IWT significantly reduces the occurrence of common rounding errors. As a result, secret key extraction becomes very accurate and eliminates the need to access the original images. This design, using secret sharing based on counting, not only has simplicity and efficiency, but also features such as flexibility, scalability, and lack of central authority. In addition, high‐quality steganography was obtained for keys with lengths of 64, 256, 512, 1024, and 3072, showing an average PSNR=80.11 in different color images. This outstanding performance makes it a highly efficient alternative to previous designs, representing a groundbreaking contribution with significant public interest in the field.

A Flexible Bivalent Approach to Comprehensively Improve the Performances of Stilbazolium Dyes as Amyloid-β Fluorescent Probes.

Exploring new ways to reconstruct the structure and function of inappropriate organic fluorophores for improving amyloid-β (Aβ) fluorescent imaging performance is desired for precise detection and early diagnosis of Alzheimer's disease (AD). With stilbazolium dyes as examples, here, we present a multipronged approach to comprehensively improved the Aβ fluorescent imaging performance through a flexible bivalent method, where a flexible carbon chain was introduced to link two monomers to form a homodimer. Our results reveal a mechanism wherein the flexible linker creates a well-defined probe with specific orientations and distinct photophysical properties. Applying this approach in combination with theoretical simulation, the homodimers exhibited a comprehensive improvement of the Aβ fluorescent imaging performance of the dye monomers, including better photostability and higher signal-to-noise (S/N) ratio, higher "off-on" near-infrared fluorescence (NIRF) response sensitivity, higher specificity and affinity to Aβ deposits, and more reasonable lipophilicity for blood-brain barrier (BBB) penetrability. The results demonstrate that flexible homodimers offer a multipronged approach to obtaining high-performance NIRF imaging reagents for the detection of Aβ deposits both in vitro and in vivo.

Zero-dimensional hybrid zinc halide scintillators with efficient blue light emission for X-ray imaging

Scintillators, an important component of optoelectronic materials, have been used extensively for medical imaging and security detection. Currently, most X-ray imaging scintillators rely on inorganic scintillators that use rare earths. Scintillator materials with mild growth conditions, high light yield, environmental friendliness, and low cost are still worth exploring. Here, we prepared novel zero-dimensional (0D) organic-inorganic hybrid zinc halides [C9H14N]2ZnBr4 (C9H14N+ = N-butylpyridinium bromide cation) based on pyridine quaternary ammonium salt C9H14NBr and ZnBr2. Under UV excitation, the [C9H14N]2ZnBr4 single crystal shows efficient blue light emission with the center at 426 nm, a large Stokes shift of 129 nm, and a high quantum yield of 49.95 %. In addition, the highly efficient blue emission of [C9H14N]2ZnBr4 crystals was also able to be excited by X-rays. The [C9H14N]2ZnBr4 crystal exhibited a relatively low detection limit of 4.995 μGyair s-1. X-ray imaging results based on [C9H14N]2ZnBr4 scintillation screens demonstrate high-performance X-ray imaging capabilities. According to these results, [C9H14N]2ZnBr4 crystals are promising X-ray scintillators due to their non-toxicity, excellent luminescence, and good detection performance.

Deep Learning within a DICOM WSI Viewer for Histopathology

Microscopy scanners and artificial intelligence (AI) techniques have facilitated remarkable advancements in biomedicine. Incorporating these advancements into clinical practice is, however, hampered by the variety of digital file formats used, which poses a significant challenge for data processing. Open-source and commercial software solutions have attempted to address proprietary formats, but they fall short of providing comprehensive access to vital clinical information beyond image pixel data. The proliferation of competing proprietary formats makes the lack of interoperability even worse. DICOM stands out as a standard that transcends internal image formats via metadata-driven image exchange in this context. DICOM defines imaging workflow information objects for images, patients’ studies, reports, etc. DICOM promises standards-based pathology imaging, but its clinical use is limited. No FDA-approved digital pathology system natively generates DICOM, and only one high-performance whole slide images (WSI) device has been approved for diagnostic use in Asia and Europe. In a recent series of Digital Pathology Connectathons, the interoperability of our solution was demonstrated by integrating DICOM digital pathology imaging, i.e., WSI, into PACs and enabling their visualisation. However, no system that incorporates state-of-the-art AI methods and directly applies them to DICOM images has been presented. In this paper, we present the first web viewer system that employs WSI DICOM images and AI models. This approach aims to bridge the gap by integrating AI methods with DICOM images in a seamless manner, marking a significant step towards more effective CAD WSI processing tasks. Within this innovative framework, convolutional neural networks, including well-known architectures such as AlexNet and VGG, have been successfully integrated and evaluated.

Sub-GHz optical resolution mid-infrared hyperspectral imaging with dual-comb

High-performance hyperspectral imaging is becoming one of the most sought-after tools in the world today by providing a wide range of capabilities and applications. However, traditional systems have significant performance limitations, mainly related to the ability to resolve narrow spectral features and to detection sensitivity. With the aim of addressing these weaknesses, we present here the first hyperspectral dual-comb imaging system capable of providing sub-GHz (<0.033 cm−1) optical resolutions and fast acquisition rates in the mid-infrared region. The system has been experimentally demonstrated by analyzing methane at 2968 cm−1 with optical resolutions of down to 300 MHz (0.01 cm−1) and time resolutions well into the sub-second range.

Imaging through scattering media under strong ambient light interference via the lock-in process.

Scattered light imaging techniques leveraging memory effects have been extensively investigated, yet most approaches are limited to operating in predominantly dark environments. The introduction of additional optical noise disrupts the fine structure of the original speckle pattern, undermining spatial correlation and resulting in imaging failure. In this study, we present a high-performance imaging method that integrates a lock-in process to overcome this limitation. Our experimental results demonstrate that the proposed technique enables successful imaging of targets in low signal-to-background ratio (SBR) environments, even at SBR levels as low as -28.0 dB. Furthermore, the method allows for the directional separation of targets with distinct modulation frequencies. This innovative approach has the potential to significantly expand the applicability of scattering imaging techniques by eliminating the constraints of dark field environments, thereby enhancing the convenience of in vivo microscopy and daytime astronomical observations.

Efficient production of ligand-free microscintillators at gram-scale for high-resolution X-ray luminescence imaging

The efficient production of high-quality scintillators with long radioluminescence afterglow is crucial for high-performance X-ray luminescence extension imaging. However, scaling-up the synthesis of ligand-free scintillators to fabricate large-area X-ray imaging screens for industrial applications remains a challenge. In this study, we report an efficient method to synthesize ligand-free, lanthanide-doped microscintillators by a one-pot reaction via the concentrated hydrothermal method. The as-synthesized microscintillators exhibit prolonged persistent radioluminescence for up to 30 days after X-ray exposure and remain high stability in air or water for more than 18 months without deterioration. Monte Carlo simulations indicate that the size effect is responsible for the excellent afterglow performance of the microscintillators. We employ these high-quality lanthanide-doped microscintillators to fabricate a large-area X-ray imaging detector using a blade-coating method, a spatial resolution of 24.9 lp/mm for X-ray imaging. Our study offers a solution for scaling-up the synthesis of low-cost microscintillators for practical applications.

Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human.

The development of high-performance imaging processing algorithms is a core area of photoacoustic tomography. While various deep learning based image processing techniques have been developed in the area, their applications in 3D imaging are still limited due to challenges in computational cost and memory allocation. To address those limitations, this work implements a 3D fully-dense (3DFD) U-net to linear array based photoacoustic tomography and utilizes volumetric simulation and mixed precision training to increase efficiency and training size. Through numerical simulation, phantom imaging, and in vivo experiments, this work demonstrates that the trained network restores the true object size, reduces the noise level and artifacts, improves the contrast at deep regions, and reveals vessels subject to limited view distortion. With these enhancements, 3DFD U-net successfully produces clear 3D vascular images of the palm, arms, breasts, and feet of human subjects. These enhanced vascular images offer improved capabilities for biometric identification, foot ulcer evaluation, and breast cancer imaging. These results indicate that the new algorithm will have a significant impact on preclinical and clinical photoacoustic tomography.

Approaching pixel-level readout of SNSPD array by inductor-shaping pulse

Although many multiplexed arrays of a superconducting nanowire single-photon detector (SNSPD) have been reported, it is still a major challenge to develop pixel-level readout arrays with high efficiency, parallel detection, and fast processing for real-time imaging. Here, we report a SNSPD array with inductor-shaping pulses for approaching the pixel-level readout. Optimized inductors are introduced to shape the output pulses of each pixel, and the response pulses of all pixels are synthesized in a series-connected structure. Then, the on/off states of all pixels can be encoded to the widths, amplitudes, and areas of the output pulses by the single-channel readout. This proposal is verified by a 4-pixel SNSPD array and a 16-pixel SNSPD array. It shows that the array not only inherits the features of the single-pixel SNSPD, such as photosensitive area, filling factor, quantum efficiency, and dark count rate, but also implements parallel operation of all pixels, which is always confused in traditional multiplexed SNSPD arrays. At the same time, the single-channel readout simplifies the system, and the serial digital signal converted from the shaped pulse enabled an easy and fast readout process, paving the way for high performance and real-time imaging.