High-contrast Imaging Research Articles

Development of Hydrophilic Oxide/Polymer Composite Membranes for Polymer Electrolyte Fuel Cells Operating at Wide Temperature Range

Toward the wide-spread use of heavy-duty vehicles (HDVs) operated by polymer electrolyte fuel cells (PEFCs) should approach the higher temperature around 120oC besides normal temperature (60-80oC). Commercial perfluorosulfonic acid-based electrolyte (PFSA, e.g. NafionⓇ) shows better proton conductivity with chemical stability in wide temperatures range, though decreases under low back pressure at 100~120 oC. In order to improve the proton conductivity at the higher temperature range, composite membrane of NafionⓇ with a hydrophilic filler of Ta doped TiO2 (Ta-TiO2) was synthesized. The Ta-TiO2 nanoparticles fillers with fused-aggregate network microstructure were synthesized by the flame method, and were crushed by wet mill(Star Burst, :Sugino Machine Limited CO.). The fillers were mixed with NafionⓇsolution (20 wt.%：D2020 Chemours Co.) by use of a rotary ultrasonic dispersing processor (500 rpm, 60 min. PR-1 :Thnky Co.), a planetary ball mill (500 rpm、30 min.：PULVERISETTE 6：FRITSCH Co.), and a defoaming processor (500 rpm、5 min.：HM-400W: Kyouritu Seiki). The dispersion solution was coated in hot air by use of a die coating system (die coater: Taku-Dai Mini-50: Daimon Co., Ltd.), and were hot-pressed (140 ℃ 3 min. TCMD-2.5:TOHO KOGYO co.) to form composite membranes (9 cm×9 cm ×25 µmt).The cross-sectional backscattered electron image BSE) of the composite membrane observed by scanning electron microscope (SEM, acceleration voltage 2 kV, SU9000: Hitachi High-Tech Co.) and transmission electron microscope (TEM, H-9500: Hitachi High-Tech Co.). The proton conductivity of the composite membrane was measured by the AC four-probe method. The current-voltage (IV) polarization curves of Single cells using the prepared composite membrane (membrane thickness: 25 µm) were measured at 80 ℃ 100% RH and 120 ℃ 20% RH, back pressure 50 kPaG.The obtained composite membranes were well flexibility with softness. The BSE images indicated that the high contrast images of Ta-TiO2 parties were uniformly dispersed in composite membrane. The higher magnified of the cross-sectional TEM images also showed that the hydrophilic surface of Ta-TiO2 adhered to the Nafion® without any air pores. The proton conductivity of the composite membrane at 80% RH increased with increasing Ta-TiO2 content and showed maximum value of 0.13 S cm-1at 3 wt%, and then decreased. The maximum proton conductivity was 1.3 times higher than that of Nafion®. The IV performance of the single cell using composite membrane kept same performance to that using Nafion® membrane at 80 oC 100% RH, and then improved at 120 oC 20% RH. The water content at 120 oC 20% RH kept higher value by the effect of hydrophilicity of the Ta-TiO2, which would be one of the reasons to enhance the cell performance at 120 oC 20% RH. Figure 1

Deep learning framework for automated goblet cell density analysis in in-vivo rabbit conjunctiva

Goblet cells (GCs) in the conjunctiva are specialized epithelial cells secreting mucins for the mucus layer of protective tear film and playing immune tolerance functions for ocular surface health. Because GC loss is observed in various ocular surface diseases, GC examination is important for precision diagnosis. Moxifloxacin-based fluorescence microscopy (MBFM) was recently developed for non-invasive high-contrast GC visualization. MBFM showed promise for GC examination by high-speed large-area imaging and a robust analysis method is needed to provide GC information. In this study, we developed a deep learning framework for GC image analysis, named dual-channel attention U-Net (DCAU-Net). Dual-channel convolution was used both to extract the overall image texture and to acquire the GC morphological characteristics. A global channel attention module was adopted by combining attention algorithms and channel-wise pooling. DCAU-Net showed 93.1% GC segmentation accuracy and 94.3% GC density estimation accuracy. Further application to both normal and ocular surface damage rabbit models revealed the spatial variations of both GC density and size in normal rabbits and the decreases of both GC density and size in damage rabbit models during recovery after acute damage. The GC analysis results were consistent with histology. Together with the non-invasive high-contrast imaging method, DCAU-Net would provide GC information for the diagnosis of ocular surface diseases.

Neuroplasticity in honey bee brains: An enhanced micro-computed tomography protocol for precise mushroom body volume measurement

BackgroundIn insect brains, mushroom bodies are associated with memory and learning behavior. It has been demonstrated that the volume of the mushroom bodies in the brain of a worker honey bee changes during the adult stage. Changes in mushroom body volume imply high neuroplasticity in the brains and may be related to the age polyethism of honey bees. A suitable volume measurement method is needed to understand the correlation between behavioral changes and mushroom body volume changes in honey bees. New methodWe developed a new protocol for insect micro-computed tomography by modifying a previously reported method. Permount™ mounting medium was used as the embedding medium for micro-computed tomography scanning. ResultsThis protocol can generate images with high contrast inside the brain and reduce the marked shape changes during specimen processing. From the resulting high-contrast images, we used freeware to generate a three-dimensional model and calculate the volumes of the mushroom bodies in honey bees. The measured volumes of the mushroom bodies were larger than the values reported in most previous studies. There was no significant difference between the left and right mushroom body volumes, but the volumes of honey bee mushroom bodies significantly increased with age. Comparison with existing methodsPrevious protocols for micro-computed tomography using dried samples would cause brain shrinkage; protocols using ethanol-preserved or resin-embedded samples generated images with lower contrast. ConclusionsThe embedding protocol for micro-computed tomography is suitable for calculating volume of the mushroom bodies in honey bee brains.

Aptamer Conjugate-Based Ratiometric Fluorescent Probe for Precise Imaging of Therapy-Induced Cancer Senescence.

Therapy-induced cellular senescence has been increasingly recognized as a key mechanism to promote various aspects of carcinogenesis in a nonautonomous manner. Thus, real-time imaging monitoring of cellular senescence during cancer therapy is imperative not only to further elucidate its roles in cancer progression but also to provide guidance for medical management of cancer. However, it has long been a challenging task due to the lack of effective imaging molecule tools with high specificity and accuracy toward cancer senescence. Herein, we report the rational design, synthesis, and evaluation of an aptamer conjugate-based ratiometric fluorescent probe for precise imaging of therapy-induced cancer senescence. Unlike traditional senescence imaging systems, our probe targets two senescence-associated markers at both cellular and subcellular dimensions, namely, aptamer-mediated membrane marker recognition for active cell targeting and lysosomal marker-triggered ratiometric fluorescence changes of two cyanine dyes for site-specific, high-contrast imaging. Moreover, such a two-channel fluorescence response is activated after a one-step reaction and at the same location, avoiding the diffusion-caused signal decay previously encountered in dual-marker activated probes, contributing to spatiotemporally specific imaging of therapy-induced cancer senescence in living cells and three-dimensional multicellular tumor spheroids. This work may offer a valuable tool for a basic understanding of cellular senescence in cancer biology and interventions.

Optical vortex coronagraphy for high-contrast imaging of femtosecond laser filaments

A high-contrast imaging technique for phase objects based on the optical vortex coronagraph (OVC) is proposed. This method offers the unique advantage of background-free imaging due to the introduction of azimuthal phase in the Fourier plane. We employed the OVC method to detect femtosecond laser-induced air plasma and compared it with the classic diffractometry and fluorescent imaging methods. We achieved a phase sensitivity of ∼0.035 waves that surpassed the capabilities of the other two methods. The combination of this highly sensitive imaging technique with the pump–probe method holds promise for applications in ultrafast imaging of laser–material interactions.

Engineering of a Self-Regulatory Bidirectional DNA Assembly Circuit for Amplified MicroRNA Imaging.

The engineering of catalytic hybridization DNA circuits represents versatile ways to orchestrate a complex flux of molecular information at the nanoscale, with potential applications in DNA-encoded biosensing, drug discovery, and therapeutics. However, the diffusive escape of intermediates and unintentional binding interactions remain an unsolved challenge. Herein, we developed a compact, yet efficient, self-regulatory assembly circuit (SAC) for achieving robust microRNA (miRNA) imaging in live cells through DNA-templated guaranteed catalytic hybridization. By integrating the toehold strand with a preblocked palindromic fragment in the stem domain, the proposed miniature SAC system allows the reactant-to-template-controlled proximal hybridization, thus facilitating the bidirectional-sustained assembly and the localization-intensified signal amplification without undesired crosstalk. With condensed components and low reactant complexity, the SAC amplifier realized high-contrast intracellular miRNA imaging. We anticipate that this simple and template-controlled design can enrich the clinical diagnosis and prognosis toolbox.

CXCR4-directed PET/CT with [68 Ga]Ga-pentixafor in solid tumors—a comprehensive analysis of imaging findings and comparison with histopathology

BackgroundC-X-C motif chemokine receptor 4 (CXCR4) is overexpressed in various solid cancers and can be targeted by CXCR4-directed molecular imaging. We aimed to characterize the in-vivo CXCR4 expression in patients affected with solid tumors, along with a comparison to ex-vivo findings.MethodsA total 142 patients with 23 different histologically proven solid tumors were imaged with CXCR4-directed PET/CT using [68 Ga]Ga-pentixafor (total number of scans, 152). A semi-quantitative analysis of the CXCR4-positive tumor burden including maximum standardized uptake values (SUVmax) and target-to-background ratios (TBR) using blood pool was conducted. In addition, we performed histopathological staining to determine the immuno-reactive score (IRS) from patients’ tumor tissue and investigated possible correlations with SUVmax (by providing Spearman’s rho ρ). Based on imaging, we also assessed the eligibility for CXCR4-targeted radioligand therapy or non-radioactive CXCR4 inhibitory treatment (defined as more than five CXCR4-avid target lesions [TL] with SUVmax above 10).ResultsOne hundred three of 152 (67.8%) scans showed discernible uptake above blood pool (TBR > 1) in 462 lesions (52 primary tumors and 410 metastases). Median TBR was 4.4 (1.05–24.98), thereby indicating high image contrast. The highest SUVmax was observed in ovarian cancer, followed by small cell lung cancer, desmoplastic small round cell tumor, and adrenocortical carcinoma. When comparing radiotracer accumulation between primary tumors and metastases for the entire cohort, comparable SUVmax was recorded (P > 0.999), except for pulmonal findings (P = 0.013), indicative for uniform CXCR4 expression among TL. For higher IRS, a weak, but statistically significant correlation with increased SUVmax was observed (ρ = 0.328; P = 0.018). In 42/103 (40.8%) scans, more than five TL were recorded, with 12/42 (28.6%) exhibiting SUVmax above 10, suggesting eligibility for CXCR4-targeted treatment in this subcohort.ConclusionsIn a whole-body tumor read-out, a substantial portion of prevalent solid tumors demonstrated increased and uniform [68 Ga]Ga-pentixafor uptake, along with high image contrast. We also observed a respective link between in- and ex-vivo CXCR4 expression, suggesting high specificity of the PET agent. Last, a fraction of patients with [68 Ga]Ga-pentixafor-positive tumor burden were rendered potentially suitable for CXCR4-directed therapy.

The polarisation properties of the HD 18327 debris ring. Evidence for sub-micron particles from scattered light observations

Polarisation is a powerful remote-sensing tool to study the nature of particles scattering the starlight. It is widely used to characterise interplanetary dust particles in the Solar System and increasingly employed to investigate extrasolar dust in debris discs' systems. We aim to measure the scattering properties of the dust from the debris ring around at near-infrared wavelengths. We obtained high-contrast polarimetric images of in the H band with the SPHERE / IRDIS instrument on the Very Large Telescope (ESO). We complemented them with archival data from HST / NICMOS in the F110W filter reprocessed in the context of the Archival Legacy Investigations of Circumstellar Environments (ALICE) project. We developed a combined forward-modelling framework to simultaneously retrieve the scattering phase function in polarisation and intensity. We detected the debris disc around in polarised light and total intensity. We measured the scattering phase function and the degree of linear polarisation of the dust at 1.6 in the birth ring. The maximum polarisation is $23.6<!PCT!> 2.6<!PCT!>$ and occurs between a scattering angle of $70^ and $82^ We show that compact spherical particles made of a highly refractive and relatively absorbing material in a differential power-law size distribution of exponent $-3.5$ can simultaneously reproduce the polarimetric and total intensity scattering properties of the dust. This type of material cannot be obtained with a mixture of silicates, amorphous carbon, water ice, and porosity, and requires a more refracting component such as iron-bearing minerals. We reveal a striking analogy between the near-infrared polarisation of comets and that of The methodology developed here combining VLT/SPHERE and HST/NICMOS may be applicable in the future to combine the polarimetric capabilities of SPHERE with the sensitivity of JWST.

Multimodal photoacoustic microscopy, optical coherence tomography, and fluorescence imaging of USH2A knockout rabbits

Usher syndrome type 2A (USH2A) is a genetic disorder characterized by retinal degeneration and hearing loss. To better understand the pathogenesis and progression of this syndrome, animal models such as USH2A knockout (USH2AKO) rabbits have been developed. In this study, we employed multimodal imaging techniques, including photoacoustic microscopy (PAM), optical coherence tomography (OCT), fundus autofluorescence (FAF), fluorescein angiography (FA), and indocyanine green angiography (ICGA) imaging to evaluate the retinal changes in the USH2AKO rabbit model. Twelve New Zealand White rabbits including USH2AKO and wild type (WT) were used for the experiments. Multimodal imaging was implemented at different time points over a period of 12 months to visualize the progression of retinal changes in USH2AKO rabbits. The results demonstrate that ellipsoid zone (EZ) disruption and degeneration, key features of Usher syndrome, began at the age of 4 months old and persisted up to 12 months. The EZ degeneration areas were clearly observed on the FAF and OCT images. The FAF images revealed retinal pigment epithelium (RPE) degeneration, confirming the presence of the disease phenotype in the USH2AKO rabbits. In addition, PAM images provided high-resolution and high image contrast of the optic nerve and the retinal microvasculature, including retinal vessels, choroidal vessels, and capillaries in three-dimensions. The quantification of EZ fluorescent intensity using FAF and EZ thickness using OCT provided comprehensive quantitative data on the progression of degenerative changes over time. This multimodal imaging approach allowed for a comprehensive and non-invasive assessment of retinal structure, microvasculature, and degenerative changes in the USH2AKO rabbit model. The combination of PAM, OCT, and fluorescent imaging facilitated longitudinal monitoring of disease progression and provided valuable insights into the pathophysiology of USH2A syndrome. These findings contribute to the understanding of USH2A syndrome and may have implications for the development of diagnostic and therapeutic strategies for affected individuals. The multimodal imaging techniques employed in this study offer a promising platform for preclinical evaluation of potential treatments and may pave the way for future clinical applications in patients with Usher syndrome.

High-contrast tumor imaging via a de novo designed aminopeptidase N targeted fluorogenic probe

Cancer, a leading cause of death worldwide, still needs surgery as essential curation in over 80% population. However, complete resection of cancerous tissues remains a great challenge due to the difficulty in tumor margin identification. Fluorescent probe based imaged-guided surgery (IGS) for in-situ local disease focus imaging is thought to be an ideal method for intraoperation tumor identification due to its high spatiotemporal resolution and real-time manner. By targeting tumor microenvironments (TME) or tumor-associated biomolecules, fluorescent probes could “light-up” tumor tissues and navigate surgical operation. Among which, biosensors targeting aminopeptidase N (APN) have attracted intense attention due to its high relevance with progression of tumor. However, most of the probes adopting its natural substrate (L-Alaine) as the recognition unit may be vulnerable toward other widely distributed aminopeptidase such as leukotriene A4 hydrolase (LTA4H) and leucine aminopeptidase (LAP). In order to acquire high specific APN targeted probe for accurate detection of APN activity in biological samples and fulfill high contrast tumor imaging, herein a non-natural amino acid (P-Chloro-L-Phenylalanine, PCPA) based fluorescent probe was firstly reported for APN targeted bioimaging. The acquired named as TMN-PCPA showed high affinity (Km = 3.27 ± 0.23 μM) and dramatically improved selectivity toward APN than previously reported APN probe. TMN-PCPA was successfully applicated into discrimination tumor bio-samples from normal bio-samples in both cellular and ex vivo level with significantly increased tumor to normal (T/N) ratio, thus showed great potential in intraoperation application such imaged-guided surgery and providing structural basis to high specific APN targeted biosensor construction.

Empirical contrast model for high-contrast imaging A VLT/SPHERE case study

Context. The ability to accurately predict the contrast achieved with high-contrast imagers is important for efficient scheduling and quality control measures in modern observatories. Aims. We aim to consistently predict and measure the raw contrast achieved by SPHERE/IRDIS on a frame-by-frame basis in order to improve the efficiency of SPHERE at the Very Large Telescope (VLT) and maximise scientific yield. Methods. Contrast curves were calculated for over 5 yr of archival data obtained using the most common SPHERE/IRDIS corona-graphic mode in the H2/H3 dual-band filter. These data consist of approximately 80 000 individual frames, which were merged and interpolated with atmospheric data to create a large database of contrast curves with associated features. An empirical power-law model for contrast – motivated by physical considerations – was then trained and finally tested on an out-of-sample test dataset. Results. At an angular separation of 300 mas, the contrast model achieved a mean (out-of-sample) test error of 0.13 magnitude with the 5th and 95th percentiles of the residuals equal to −0.23 and 0.64 magnitude respectively. The models test-set root mean square error (RMSE) between 250 and 600 mas was between 0.31 and 0.40 magnitude, which is equivalent to that of other state-of-the-art contrast models presented in the literature. In general, the model performed best for targets of between 5 and 9 G-band magnitude, with degraded performance for targets outside this range. This model is currently being incorporated into the Paranal SCUBA software for first-level quality control and real-time scheduling support.

NA-SODINN: A deep learning algorithm for exoplanet image detection based on residual noise regimes

Context. Supervised deep learning was recently introduced in high-contrast imaging (HCI) through the SODINN algorithm, a con-volutional neural network designed for exoplanet detection in angular differential imaging (ADI) datasets. The benchmarking of HCI algorithms within the Exoplanet Imaging Data Challenge (EIDC) showed that (i) SODINN can produce a high number of false positives in the final detection maps, and (ii) algorithms processing images in a more local manner perform better. Aims. This work aims to improve the SODINN detection performance by introducing new local processing approaches and adapting its learning process accordingly. Methods. We propose NA-SODINN, a new deep learning binary classifier based on a convolutional neural network (CNN) that better captures image noise correlations in ADI-processed frames by identifying noise regimes. The identification of these noise regimes is based on a novel technique, named PCA-pmaps, which allowed us to estimate the distance from the star in the image from which background noise started to dominate over residual speckle noise. NA-SODINN was also fed with local discriminators, such as signal-to-noise ratio (S/N) curves, which complement spatio-temporal feature maps during the model’s training. Results. Our new approach was tested against its predecessor, as well as two SODINN-based hybrid models and a more standard annular-PCA approach, through local receiving operating characteristics (ROC) analysis of ADI sequences from the VLT/SPHERE and Keck/NIRC-2 instruments. Results show that NA-SODINN enhances SODINN in both sensitivity and specificity, especially in the speckle-dominated noise regime. NA-SODINN is also benchmarked against the complete set of submitted detection algorithms in EIDC, in which we show that its final detection score matches or outperforms the most powerful detection algorithms. Conclusions. Throughout the supervised machine learning case, this study illustrates and reinforces the importance of adapting the task of detection to the local content of processed images.

Hippocampal subfield volume in relation to cerebrospinal fluid Amyloidβ1‐42 in early Alzheimer’s disease: A 7T MRI study

AbstractBackground7T MRI can provide high contrast and spatial resolution images for studying the volume of hippocampal subfields which can be used in conjunction with Amyloidβ1‐42 and tau measures in the cerebrospinal fluid (CSF) to provide a non‐invasive alternative marker for Alzheimer’s disease (AD).Method41 participants (20 AD and 21 healthy age‐ and gender‐matched controls) were recruited [aged 40 ‐ 76 years; 64% female). The protocol for the analysis of the CSF for the Amyloidβ1‐42 (reference range: 627‐1322 pg/ml), total tau (reference range: 146‐595 pg/ml), Thr181‐phosphorylated tau (reference range <68 pg/ml) have been previously published (Hosseini et al, 2022) and the measures were used for the ATN classification of Alzheimer’s disease. Imaging was carried out on a Philips Achieva 7T scanner using a Nova Medical (Wilmington MA, USA) single‐channel transmit, 32‐channel receive (1Tx32Rx) head coil. Images acquired are PSIR (TE/TR = 3.1/6.9ms; FA = 6o, isotropic 0.55 mm resolution) and T2‐weighted FSE (TE/TR = 119/59001ms, FA = 90o, 0.38×0.39×1.50 mm3 resolution).ResultFig 1 show the T2‐weighted image and the segmentation of the hippocampus obtained using ASHS (Yushkevich et al., 2015b)There was a significant decrease in the volume of each hippocampal subfield (Fig2) in AD patients compared to controls. The change in volume was not significant for the whole brain showing that the hippocampal volume loss was not simply part of general brain atrophy.There was an approximately linear relationship between the CSF Amyloidβ1‐42 and volume of the hippocampus for the AD participants (Fig 3). A positive linear relationship was observed in all the subfields of the hippocampus but a statistically significant relation was only achieved in the Entorhinal Cortex (ERC) [p<0.022, r = 0.508].The ERC is known to represent perception of time, memory and navigation (Tsao et al., 2018)(Du et al., 2001), and AD pathology may start from the ERC before migrating along the hippocampus (Braak & Braak, 1997). Further, the ERC volume loss on 1.5 T MRI has been reported greater in AD as compared with Mild Cognitive Impairment(Du et al., 2001).ConclusionThe association of the volume of ERC and CSF Amyloidβ1‐42 suggests the potential for using 7T MRI as a biomarker for an early identification of AD.

Simulation of High-contrast Polarimetric Observations of Debris Disks with the Roman Coronagraph Instrument

The Nancy Grace Roman Space Telescope Coronagraph Instrument will enable the polarimetric imaging of debris disks and inner dust belts in the optical and near-infrared wavelengths, in addition to the high-contrast polarimetric imaging and spectroscopy of exoplanets. The Coronagraph uses two Wollaston prisms to produce four orthogonally polarized images and is expected to measure the polarization fraction with measurement errors <3% per spatial resolution element. To simulate the polarization observations through the Hybrid Lyot Coronagraph (HLC) and Shaped Pupil Coronagraph (SPC), we model disk scattering, the coronagraphic point-response function, detector noise, speckles, jitter, and instrumental polarization and calculate the Stokes parameters. To illustrate the potential for discovery and a better understanding of known systems with both the HLC and SPC modes, we model the debris disks around Epsilon Eridani and HR 4796A, respectively. For Epsilon Eridani, using astrosilicates with 0.37 ± 0.01 as the peak input polarization fraction in one resolution element, we recover the peak disk polarization fraction of 0.33 ± 0.01. Similarly, for HR 4796A, for a peak input polarization fraction of 0.92 ± 0.01, we obtain the peak output polarization fraction as 0.80 ± 0.03. The Coronagraph design meets the required precision, and forward modeling is needed to accurately estimate the polarization fraction.

An algorithm of unordered wavefront propagation in terahertz phase retrieval with dense multiplane data acquisition

Iterative phase retrieval algorithms from multiple diffraction patterns in the terahertz (THz) frequency range are a promising tool of computational imaging capable of providing high spatial resolution of reconstructed phase images. One of the commonly used algorithms is SBMIR, which employs multiple intensity distributions of the diffraction object wavefield as input data. Compared with single-frame methods, the multi-plane approach allows for a faster convergence, but requires time-consuming data acquisition from a receiver positioned at a variety of distances from the object. Previously, we proposed a method for THz data acquisition in a single scan mode, which allows one to quickly obtain an exhaustive set of diffraction distributions. In this paper we evaluate an up-to-date phase retrieval algorithm based on the SBMIR/R-SBMIR method (which utilizes stochastic wavefront propagation) on the experimental data captured by a single-scan technique. Unlike a number of conventional phase retrieval algorithms, which may require a series of numerical experiments for determining optimal intensity distributions from a large dataset, disordered propagation of the estimation wavefront guarantees the high-contrast and high-resolution image reconstruction without pre-setting the parameters. It is shown that the package use of the single-scan technique with the subsequent data processing using the R-SBMIR algorithm has application potential for automation of the multi-plane phase retrieval in the THz range.

The brighter-fatter effect in the JWST MIRI Si:As IBC detectors

Context. The Mid-Infrared Instrument (MIRI) on board the James Webb Space Telescope (JWST) uses three Si:As impurity band conduction (IBC) detector arrays. The output voltage level of each MIRI detector pixel is digitally recorded by sampling up the ramp. For uniform or low-contrast illumination, the pixel ramps become nonlinear in a predictable way, but in areas of high contrast, the nonlinearity curve becomes much more complex. The origin of the effect is poorly understood and currently not calibrated out of the data. Aims. We provide observational evidence of the brighter-fatter effect (BFE) in MIRI conventional and high-contrast coronagraphic imaging, low-resolution spectroscopy, and medium-resolution spectroscopy data, and we investigate the physical mechanism that gives rise to the effect on the MIRI detector pixel raw voltage integration ramps. Methods. We used public data from the JWST/MIRI commissioning and Cycle 1 phase. We also developed a numerical electrostatic model of the MIRI detectors using a modified version of the public Poisson_CCD code. Results. We find that the physical mechanism behind the BFE manifesting in MIRI data is fundamentally different to that of charge-coupled devices and photodiode arrays such as the Hawaii-XRG near-infrared detectors used by the NIRISS, NIRCam, and NIRSpec instruments on board JWST. Observationally, the BFE makes the JWST MIRI data yield 10–25% larger point sources and spectral line profiles as a function of the relative level of de-biasing of neighboring detector pixels. This broadening impacts the MIRI absolute flux calibration, time-series observations of faint companions, and point spread function modeling and subtraction. We also find that the intra-pixel 2D profile of the shrinking Si:As IBC detector depletion region directly impacts the accuracy of the pixel ramp nonlinearity calibration model.

Adaptive high-gray image enhancement algorithm based on logarithmic mapping and simulated exposure

In the fields of industry, security, military and other areas, high gray-level low contrast images have been widely used. However, due to the large dynamic range of gray level, high noise, blurry detail information and low contrast, it brings difficulties to subsequent image enhancement, target detection and other processes. Ordinary image enhancement algorithms are only suitable for conventional 8-bit images, which are not effective to deal with high-gray and low-contrast images. To address these issues, this paper proposed an adaptive high-gray image enhancement algorithm based on logarithmic mapping and simulated exposure, which was used for processing X-ray film images, infrared images, SAR images and other high gray-level low contrast images. Firstly, a detailed algorithm model construction method was provided for high gray-level low contrast images. Secondly, in order to verify the universality of the algorithm proposed in this paper, qualitative and quantitative experimental analyses were carried out using X-ray film images, infrared images and SAR images respectively. Finally, taking the most complex infrared image in the application environment as an example, the images, enhanced by the algorithm proposed in this paper, were sent to the YOLOv8 neural network for target detection, which improved the detection accuracy. The experiment shows that the algorithm, proposed in this paper, can significantly improve the image quality of high gray-level low contrast images, the detail information is obvious and the contrast is significantly improved. Various indicators are far higher than those of traditional algorithms. At the same time, this algorithm can provide reliable technical support for applications, such as target detection and target tracking in this field.

Feasibility of delta radiomics–based pCR prediction for rectal cancer patients treated with magnetic resonance–guided adaptive radiotherapy

Magnetic resonance–guided adaptive radiotherapy (MRgART) represents the latest frontier in precision radiotherapy. It is distinguished from other modalities by the possibility of acquiring high-contrast soft tissue images, combined with the ability to recalculate and re-optimize the plan on the daily anatomy. The extensive database of available images offers ample scope for using disciplines such as radiomics to try to correlate features and outcomes. This study aimed to correlate the change of radiomics feature along the treatment to pathological complete response (pCR) for locally advanced rectal cancer (LARC) patients. Twenty-eight LARC patients undergoing neoadjuvant chemoradiotherapy (nCRT) with a short course (25 Gy, 5 Gy × 5f) MRgART at 1.5 Tesla MR-Linac were enrolled. The T2-weighted images acquired at each fraction, corresponding target delineation, pCR result of the surgical specimen, and clinical variables were collected. Seven families of features [First Order, Shape, Gray-level Co-occurrence Matrix (GLCM), Gray-level Dependence Matrix (GLDM), Gray-level Run Length Matrix (GLRLM), Gray-level Size Zone Matrix (GLSZM), and Neighborhood Gray Tone Difference Matrix (NGTDM)] were extracted, and delta features were calculated from the ratio of features of each successive fraction to those of the first fraction. Mann-Whitney U test and LASSO were utilized to reduce the dimension of features and select those features that are most significant to pCR. At last, the radiomics signatures were established by linear regression with the final set of features and their coefficients. A total of 581 radiomics features were extracted, and 2,324 delta features were calculated for each patient. Nineteen features and delta features, and one clinical variable (cN) were significant (p&amp;lt; 0.05) to pCR; seven predictive features were further selected and included in the linear regression to construct the radiomics signature significantly discriminating pCR and non-pCR groups (p&amp;lt; 0.05). Delta features based on MRI images acquired during a short course MRgART could potentially be used to predict treatment response in LARC patients undergoing nCRT.

A Wolf 359 in Sheep's Clothing: Hunting for Substellar Companions in the Fifth-closest System Using Combined High-contrast Imaging and Radial Velocity Analysis

Wolf 359 (CN Leo, GJ 406, Gaia DR3 3864972938605115520) is a low-mass star in the fifth-closest neighboring system (2.41 pc). Because of its relative youth and proximity, Wolf 359 offers a unique opportunity to study substellar companions around M stars using infrared high-contrast imaging and radial velocity monitoring. We present the results of Ms-band (4.67 μm) vector vortex coronagraphic imaging using Keck-NIRC2 and add 12 Keck-HIRES and 68 MAROON-X velocities to the radial velocity baseline. Our analysis incorporates these data alongside literature radial velocities from CARMENES, the High Accuracy Radial velocity Planet Searcher, and Keck-HIRES to rule out the existence of a close (a < 10 au) stellar or brown dwarf companion and the majority of large gas giant companions. Our survey does not refute or confirm the long-period radial velocity candidate, Wolf 359 b (P ∼ 2900 days), but rules out the candidate's existence as a large gas giant (>4 M Jup) assuming an age of younger than 1 Gyr. We discuss the performance of our high-contrast imaging survey to aid future observers using Keck-NIRC2 in conjunction with the vortex coronagraph in the Ms band and conclude by exploring the direct imaging capabilities with JWST to observe Jupiter- and Neptune-mass planets around Wolf 359.

Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving

Auto-driving detection usually acquires low-light infrared images, which pose a great challenge to the autopilot function at night due to their low contrast and unclear texture details. As a precursor algorithm in the field of automatic driving, the infrared image contrast enhancement method is of great significance in accelerating the operation speed of automatic driving target recognition algorithms and improving the accuracy of object localization. In this study, a convolutional neural network model including feature extraction and image enhancement modules is proposed to enhance infrared images. Specifically, the feature extraction module consists of three branches, a concatenation layer, and a fusion layer that connect in parallel to extract the feature images. The image enhancement module contains eight convolutional layers, one connectivity layer, and one difference layer for enhancing contrast in infrared images. In order to overcome the problem of the lack of a large amount of training data and to improve the accuracy of the model, the brightness and sharpness of the infrared images are randomly transformed to expand the number of pictures in the training set and form more sample pairs. Unlike traditional enhancement methods, the proposed model directly learns the end-to-end mapping between low- and high-contrast images. Extensive experiments from qualitative and quantitative perspectives demonstrate that our method can achieve better clarity in a shorter time.