Contrast Reduction Research Articles

EDOT substitution site effect on fluorenone acceptor units: electrochemical polymerization and electrochromic properties of D-A systems

A series of donor-acceptor-donor type molecules (P(2,7-DEF) and P(3,6-DEF)) based on fluorenone acceptor units and 3,4-ethylenedioxythiophene (EDOT) donor units were successfully obtained by Stille coupling reaction in this work, and then generated their corresponding D-A polymers by electrodeposition method. As prepared P(2,7-DEF) and P(3,6-DEF) were characterized by CV, UV–vis spectroscopy, SEM, spectroelectrochemistry, and kinetic studies. Both of these two monomers revealed bright red/yellow light-emitting property in dilute solution. Since the π-conjugation length is longer, the oxidation potential of 2,7-DEF was reduced to 0.85 V, accompanied with red-shifted electronic spectra and emission spectra. The resulting P(2,7-DEF) demonstrated distinct electrochromic properties under various potentials with two different colors from dark-brown to blue-purple. Due to its more porous structure, P(2,7-DEF) also showed favorable optical contrast (26.3 %), very fast switching time (0.32 s), and decent coloration efficiency (221.25 cm2 C−1). However, P(3,6-DEF) displayed much worse electrochromic performances due to its bad electroactivity. P(2,7-DEF) also displayed better redox stability (remaining 79 % electroactivity after 1000 cycles), good optical stability (6 % reduction in optical contrast after 3200 s), and stable optical memory. From these results, P(2,7-DEF) can be further used as a promising near-infrared electrochromic materials.

Unsupervised MRI motion artifact disentanglement: introducing MAUDGAN

Objective. This study developed an unsupervised motion artifact reduction method for magnetic resonance imaging (MRI) images of patients with brain tumors. The proposed novel design uses multi-parametric multicenter contrast-enhanced T1W (ceT1W) and T2-FLAIR MRI images. Approach. The proposed framework included two generators, two discriminators, and two feature extractor networks. A 3-fold cross-validation was used to train and fine-tune the hyperparameters of the proposed model using 230 brain MRI images with tumors, which were then tested on 148 patients’ in-vivo datasets. An ablation was performed to evaluate the model’s compartments. Our model was compared with Pix2pix and CycleGAN. Six evaluation metrics were reported, including normalized mean squared error (NMSE), structural similarity index (SSIM), multi-scale-SSIM (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multi-scale gradient magnitude similarity deviation (MS-GMSD). Artifact reduction and consistency of tumor regions, image contrast, and sharpness were evaluated by three evaluators using Likert scales and compared with ANOVA and Tukey’s HSD tests. Main results. On average, our method outperforms comparative models to remove heavy motion artifacts with the lowest NMSE (18.34±5.07%) and MS-GMSD (0.07 ± 0.03) for heavy motion artifact level. Additionally, our method creates motion-free images with the highest SSIM (0.93 ± 0.04), PSNR (30.63 ± 4.96), and VIF (0.45 ± 0.05) values, along with comparable MS-SSIM (0.96 ± 0.31). Similarly, our method outperformed comparative models in removing in-vivo motion artifacts for different distortion levels except for MS- SSIM and VIF, which have comparable performance with CycleGAN. Moreover, our method had a consistent performance for different artifact levels. For the heavy level of motion artifacts, our method got the highest Likert scores of 2.82 ± 0.52, 1.88 ± 0.71, and 1.02 ± 0.14 (p-values ≪ 0.0001) for our method, CycleGAN, and Pix2pix respectively. Similar trends were also found for other motion artifact levels. Significance. Our proposed unsupervised method was demonstrated to reduce motion artifacts from the ceT1W brain images under a multi-parametric framework.

A new 'CFS tracking' paradigm reveals uniform suppression depth regardless of target complexity or salience.

When the eyes view separate and incompatible images, the brain suppresses one image and promotes the other into visual awareness. Periods of interocular suppression can be prolonged during continuous flash suppression (CFS) - when one eye views a static 'target' while the other views a complex dynamic stimulus. Measuring the time needed for a suppressed image to break CFS (bCFS) has been widely used to investigate unconscious processing, and the results have generated controversy regarding the scope of visual processing without awareness. Here, we address this controversy with a new 'CFS tracking' paradigm (tCFS) in which the suppressed monocular target steadily increases in contrast until breaking into awareness (as in bCFS) after which it decreases until it again disappears (reCFS), with this cycle continuing for many reversals. Unlike bCFS, tCFS provides a measure of suppression depth by quantifying the difference between breakthrough and suppression thresholds. tCFS confirms that (i) breakthrough thresholds indeed differ across target types (e.g. faces vs gratings, as bCFS has shown) - but (ii) suppression depth does not vary across target types. Once the breakthrough contrast is reached for a given stimulus, all stimuli require a strikingly uniform reduction in contrast to reach the corresponding suppression threshold. This uniform suppression depth points to a single mechanism of CFS suppression, one that likely occurs early in visual processing because suppression depth was not modulated by target salience or complexity. More fundamentally, it shows that variations in bCFS thresholds alone are insufficient for inferring whether the barrier to achieving awareness exerted by interocular suppression is weaker for some categories of visual stimuli compared to others.

A new ‘CFS tracking’ paradigm reveals uniform suppression depth regardless of target complexity or salience

When the eyes view separate and incompatible images, the brain suppresses one image and promotes the other into visual awareness. Periods of interocular suppression can be prolonged during continuous flash suppression (CFS) – when one eye views a static ‘target’ while the other views a complex dynamic stimulus. Measuring the time needed for a suppressed image to break CFS (bCFS) has been widely used to investigate unconscious processing, and the results have generated controversy regarding the scope of visual processing without awareness. Here, we address this controversy with a new ‘CFS tracking’ paradigm (tCFS) in which the suppressed monocular target steadily increases in contrast until breaking into awareness (as in bCFS) after which it decreases until it again disappears (reCFS), with this cycle continuing for many reversals. Unlike bCFS, tCFS provides a measure of suppression depth by quantifying the difference between breakthrough and suppression thresholds. tCFS confirms that (i) breakthrough thresholds indeed differ across target types (e.g. faces vs gratings, as bCFS has shown) – but (ii) suppression depth does not vary across target types. Once the breakthrough contrast is reached for a given stimulus, all stimuli require a strikingly uniform reduction in contrast to reach the corresponding suppression threshold. This uniform suppression depth points to a single mechanism of CFS suppression, one that likely occurs early in visual processing because suppression depth was not modulated by target salience or complexity. More fundamentally, it shows that variations in bCFS thresholds alone are insufficient for inferring whether the barrier to achieving awareness exerted by interocular suppression is weaker for some categories of visual stimuli compared to others.

Tunable optoelectronic performances of novel 7H-benzo[c]fluoren-7-one acceptor unit-based conjugated D-A polymers

In this paper, two novel D-A-D type molecules (EBZFO1 and TBZFO2), which consisting of 7 H-benzo[c]fluoren-7-one as electron acceptor (A) unit, 3,4-ethylenedioxythiophene (EDOT) and thiophene (Th) as the terminal donor (D) units, were designed and synthesized. Afterwards, the corresponding D-A polymers of P(EBZFO1) and P(TBZFO2) were prepared by electrochemical deposition method. The electrochemical behavior of the monomers, electrochemical, and electrochromic properties of the D-A polymers were comparatively studied. Although both of the monomers have lower onset oxidation potentials (1.0 V/1.2 V) and good adherence on the electrode, the redox stability of their polymers was very different. P(EBZFO1) displayed much better redox stability (remaining 78.4 % electroactivity after 1000 cycles), good optical stability (2 % reduction in optical contrast after 1500 s), and stable optical memory, while P(TBZFO2) remained inferior redox stability with only 8.82 % of its original electroactivity was retained after 1000 cycles. As a result, higher optical band gap of P(TBZFO2) (1.65 eV) displayed much worse electrochromic performances with the normal optical contrast (11.4 %∼46 %) and relatively slow switching time (2.5–3.8 s). By contrast, P(EBZFO1) (band gap: 1.55 eV) showed favorable optical contrast (15.8 %) at 1100 nm with a very fast response time of 0.24 s, and decent coloration efficiency (228.8 cm2 C−1 at 433 nm). From these results, P(EBZFO1) can further render an appropriate electrochromic material for the smart windows or displays.

Speckle Noise Reduction via Linewidth Broadening for Planetary Laser Reflectance Spectrometers

The low obliquity of the Moon leads to challenging solar illumination conditions at the poles, especially for passive reflectance measurements aimed at determining the presence and extent of surface volatiles. A nascent alternate method is to use active laser illumination sources in either a multispectral or hyperspectral design. With a laser spectral source, however, the achievable reflectance precision may be limited by speckle noise resulting from the interference effects of a coherent beam interacting with a rough surface. Here, we have experimentally tested the use of laser linewidth broadening to reduce speckle noise and, thus, increase reflectance precision. We performed a series of speckle imaging tests with near-infrared laser sources of varying coherence, compared them to both theory and speckle pattern simulations, and measured the reflectance precision using calibrated targets. By increasing the laser linewidth, we observed a reduction in speckle contrast and the corresponding increase in reflectance precision, which was 80% of the theoretical improvement. Finally, we discuss methods of laser linewidth broadening and spectral resolution requirements for planetary laser reflectance spectrometers.

Feasibility of Weight-based Tube Voltage and Iodine Delivery Rate for Coronary Artery CT Angiography.

The objective of this study was to evaluate the feasibility of weight-based tube voltage and iodine delivery rate (IDR) for coronary artery CT angiography (CCTA). A total of 193 patients (mean age: 58 ± 12 years) with suspected coronary heart disease indicated for CCTA between May and October 2022 were prospectively enrolled. The subjects were divided into five groups according to body weight: < 60 kg, 60 - 69 kg, 70 - 79 kg, 80 - 89 kg, and ≥ 90 kg. The tube voltage and IDR settings of each group were as follows: 70 kVp/0.8 gI/s, 80 kVp/1.0 gI/s, 80 kVp/1.1 gI/s, 100 kVp/1.5 gI/s, and 100 kVp/1.5 gI/s, respectively. Objective image quality data included the CT value and standard deviation (noise) of the aortic root (AR), the proximal left anterior descending branch (LAD), and the distal right coronary artery (RCA), as well as the signal-to-noise ratio and contrast-to-noise ratio of the LAD and RCA. Subjective image quality assessment was performed based on the 18-segment model. Contrast and radiation doses, as well as effective dose (ED), were recorded. All continuous variables were compared using either the one-way ANOVA or the Kruskal-Wallis rank sum test. No significant differences were observed in all objective and subjective parameters of image quality between the groups (P > 0.05). However, significant differences in contrast and radiation doses were observed (P < 0.05). The contrast doses across the weight groups were 27 mL, 35 mL, 38 mL, 53 mL, and 53 mL, respectively, while the ED were 1.567 (1.30, 2.197) mSv, 1.53 (1.373, 1.78) mSv, 2.113 (1.963, 2.256) mSv, 4.22 (3.771, 4.483) mSv, and 4.786 (4.339, 5.536) mSv, respectively. Weight-based tube voltage and IDR yielded consistently high image quality, and allowed for further reduction in contrast and radiation exposure during CCTA for coronary artery diseases.

Medical Image Fusion Based on Local Saliency Energy and Multi-scale Fractal Dimension.

At present, there are some problems in multimodal medical image fusion, such as texture detail loss, leading to edge contour blurring and image energy loss, leading to contrast reduction. To solve these problems and obtain higher-quality fusion images, this study proposes an image fusion method based on local saliency energy and multi-scale fractal dimension. First, by using a non-subsampled contourlet transform, the medical image was divided into 4 layers of high-pass subbands and 1 layer of low-pass subband. Second, in order to fuse the high-pass subbands of layers 2 to 4, the fusion rules based on a multi-scale morphological gradient and an activity measure were used as external stimuli in pulse coupled neural network. Third, a fusion rule based on the improved multi-scale fractal dimension and new local saliency energy was proposed, respectively, for the low-pass subband and the 1st closest to the low-pass subband. Layerhigh pass sub-bands were fused. Lastly, the fused image was created by performing the inverse non-subsampled contourlet transform on the fused sub-bands. On three multimodal medical image datasets, the proposed method was compared with 7 other fusion methods using 5 common objective evaluation metrics. Experiments showed that this method can protect the contrast and edge of fusion image well and has strong competitiveness in both subjective and objective evaluation.

Lightweight underwater image adaptive enhancement based on zero-reference parameter estimation network

Underwater images suffer from severe color attenuation and contrast reduction due to the poor and complex lighting conditions in the water. Most mainstream methods employing deep learning typically require extensive underwater paired training data, resulting in complex network structures, long training time, and high computational cost. To address this issue, a novel ZeroReference Parameter Estimation Network (Zero-UAE) model is proposed in this paper for the adaptive enhancement of underwater images. Based on the principle of light attenuation curves, an underwater adaptive curve model is designed to eliminate uneven underwater illumination and color bias. A lightweight parameter estimation network is designed to estimate dynamic parameters of underwater adaptive curve models. A tailored set of non-reference loss functions are developed for underwater scenarios to fine-tune underwater images, enhancing the network’s generalization capabilities. These functions implicitly control the learning preferences of the network and effectively solve the problems of color bias and uneven illumination in underwater images without additional datasets. The proposed method examined on three widely used real-world underwater image enhancement datasets. Experimental results demonstrate that our method performs adaptive enhancement on underwater images. Meanwhile, the proposed method yields competitive performance compared with state-of-the-art other methods. Moreover, the Zero-UAE model requires only 17K parameters, minimizing the hardware requirements for underwater detection tasks. What’more, the adaptive enhancement capability of the Zero-UAE model offers a new solution for processing images under extreme underwater conditions, thus contributing to the advancement of underwater autonomous monitoring and ocean exploration technologies.

A Fuzzy Logic Control-Based Approach for Real-Time Energy Management of the Fuel Cell Electrical Bus Considering the Durability of the Fuel Cell System

The present study proposes a fuzzy logical control-based real-time energy management strategy (EMS) for a fuel cell electrical bus (FCEB), taking into account the durability of the fuel cell system (FCS), in order to enhance both the vehicle’s economic performance and the FCS’s service life. At first, the model of the FCEB is established whilst the power-following strategy is also formulated as a benchmark for the evaluation of the proposed strategy. Subsequently, a fuzzy logical controller is designed to improve the work efficiency of the FCS, in which the battery state-of-charge (SOC) and the vehicle’s desired power are considered the inputs, whilst the power of the FCS is the output. Then, a limitation method is integrated into the fuzzy logical controller to restrict the change rate of the FCS’s power to strengthen the FCS’s service life. At last, the evaluation is accessed based on the China city bus driving cycle (CCBC). The results indicate that the proposed fuzzy logical strategy can satisfy the dynamic performance of the FCEB well. Importantly, it also has a remarkable effectiveness in terms of promoting the FCEB’s economy. Despite a slight reduction in contrast to the fuzzy logical control, the improvements of the strategy in which the FCS’s durability is considered are still acceptable. The change rate of the FCS’s power can be confined to ±10 kW. Meanwhile, the promotion of economic performance can reach up to 8.43%, 7.69%, and 6.53% in the proposed durability consideration strategy in contrast to the power-following strategy under different battery SOCs. This will significantly benefit both the energy saving and the FCS’s durability.

Scorpionfish adjust skin pattern contrast on different backgrounds.

The two scorpionfish species Scorpaena maderensis and S. porcus are well camouflaged ambush predators that rapidly change body colouration to adjust to background colour in less than 1 min. We tested whether individuals of both species also adjust body pattern to that of the background. We placed fish on backgrounds of different pattern granularity and quantified the change in fish body pattern over 1 min. We used calibrated image analysis to analyse the patterns from the visual perspective of a prey fish species using a granularity (pattern energy) analysis and an image clustering approach. In our experiment, fish did not change their most contrasting pattern components as defined by the dominant marking size, but changed their average marking size. Moreover, fish responded with a change in pattern in contrast to the different experimental backgrounds, especially when compared to the acclimation phase. These results indicate that scorpionfish have one main pattern that can be adjusted by modulating its internal contrast. A reduction in pattern contrast could thereby improve background matching, while an increase could promote camouflage via disruptive colouration.

MTD-YOLOv5: Enhancing marine target detection with multi-scale feature fusion in YOLOv5 model

Underwater light attenuation leads to decreased image contrast. This reduction in contrast subsequently decreases target visibility. Additionally, marine target detection is challenging due to multi-scale problems from varying target-to-device distances, complex target clustering, and noise from waterborne particulates.To address these issues, we propose MTD-YOLOv5.Initially, we enhance image contrast with grayscale equalization and mitigate color shift issues through color space transformation.We then introduce a novel feature extraction module, PCBR, combining max pooling and convolution layers for more effective target feature extraction from the background.Furthermore, we present the Multi-Scale Perceptual Hybrid Pooling (MHP) module.This module integrates horizontal and vertical receptive fields to establish long-range dependencies, thereby capturing hidden target information in deep network feature maps. In the Labeled Fishes in the Wild test datasets, MTD-YOLOv5 achieves a precision of 88.1% and a mean Average Precision (mAP[0.5:.95]) of 49.6%.These results represent improvements of 2.6% in precision and 0.4% in mAP over the original YOLOv5.

Towards Removal of Shadows Caused due to Object Interferences in Smartphone Captured Document Images using Multiple Mask Generation Technique

Shadows cause a reduction of text legibility and contrast of the overall document, leading to poor recognition rates by Optical Character Recognition (OCR). Shadows were cast during photo capturing because of object interferences due to projection angle from illumination sources and are challenging to handle by OCR. This work proposes an automated technique to detect the shadows caused by object interferences and eliminate them from document images. The proposed algorithm generates two reference masks of shadow and non-shadow regions using a global thresholding method to detect and remove shadow. Initially, the document image is pre-processed, followed by mask generation for shadow and non-shadow regions, and finally, a global thresholding strategy that removes the shadow using an image mapping technique. To evaluate the effectiveness of the proposed approach, a dataset of real-world smartphone-captured document images of about 300 is considered. The datasets encompass Mobile Dataset 3 and Mobile Dataset 4 from DIBCO datasets. Experiments are conducted to quantify the efficiency of the proposed approach using performance measures such as recognition rate by OCR, presentation score, legibility score, skew, and overall document quality score. From the results, the proposed approach collectively produces an average recognition rate of 87.15% and 81.50% towards word-level recognition of Mobile Datasets 3 and 4. The text quality of the document image post shadow removal procedure achieves a higher performance score with an average presentation score of 0.35, legibility score of 0.78, skew as 73.45 degrees from the horizontal axis, and overall document quality score above six towards datasets considered. Based on qualitative analysis, comparative analysis proves that the proposed is simple yet more proficient than existing methods.

Underwater image denoising based on curved wave filtering and two-dimensional variational mode decomposition

Underwater image denoising technology is of great significance in underwater operation. Underwater operations (such as offshore oil drilling, undersea tunnels, pipeline construction, underwater archaeology, biological research, and lifesaving) require stable and clear underwater images to aid analysis. Due to the scattering and absorption of light by water bodies, obtaining high-quality underwater images is a challenging task. Underwater images are prone to low contrast, low resolution and edge distortion. Therefore, it is difficult to accurately separate the effective signal when removing the underwater image noise, which leads to the image contrast reduction. Also the edge contour is not clear, and the detail loss is serious. Therefore, we propose a novel underwater image denoising method based on curved wave filter and two-dimensional variational mode decomposition. Firstly, the noisy image is decomposed by two-dimensional variational mode decomposition, and a series of modal components with different center frequencies are obtained. The effective modal components are selected by correlation coefficient and structural similarity. And the effective modal components are processed by the curve-wave filter. Finally, the filtered modal components are reconstructed to remove the noise in the image. The experimental results show that, compared with other state-of-the-art methods, the proposed method has clearer denoising results, less mean square error, and better denoising effect.

Prevention of Contrast-induced Nephropathy in Patients Undergoing Percutaneous Coronary Intervention.

Contrast-induced nephropathy (CIN) or contrast-induced acute kidney injury has varying definitions, but in general, increased serum creatinine level by ≥ 0.3 mg/dL (26.5 µmol/L) or 1.5x of baseline value or urine output <0.5 mL/kg/h within 1-7 days after contrast media (CM) administration can be considered as CIN. CIN is one of the most common complications and is associated with increased mortality in patients undergoing percutaneous coronary intervention (PCI). Thus, risk stratification for CIN should be made and preventive strategies should be employed in which the intensity of the approach must be tailored to patient's risk profile. In all patients, adequate hydration is required, nephrotoxic medications should be discontinued, and pre-procedural high-intensity statin is recommended. In patients with an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2, IV hydration should be started 12 hours pre-procedure up until 12-24 hours after the procedure. Remote ischemic preconditioning may be performed pre-procedurally. Radial first approach for vascular access is recommended. During the procedure, low or iso-osmolar CM should be used and its volume should be limited to eGFR x 3.7. In patients at high risk for CIN, additional contrast-sparing strategies may be applied, such as using a contrast reduction system, 5 Fr catheter with no sideholes, CM dilution, limiting test injection, confirming placement using guidewire, use of stent enhancing imaging technology, using metallic/software roadmap to guide PCI, use of IVUS or dextran-based OCT, and coronary aspiration. A more advanced hydration technique based on central venous pressure, left ventricular end-diastolic pressure, or using furosemide-matched hydration, might be considered.

Push–Pull With Device Sampling

We consider decentralized optimization problems in which a number of agents collaborate to minimize the average of their local functions by exchanging over an underlying communication graph. Specifically, we place ourselves in an asynchronous model where only a random portion of nodes perform computation at each iteration, while the information exchange can be conducted between all the nodes and in an asymmetric fashion. For this setting, we propose an algorithm that combines gradient tracking with a network-level variance reduction (in contrast to variance reduction within each node). This enables the nodes to track the average of the gradients of the objective functions. Our theoretical analysis shows that the algorithm converges linearly, when the local objective functions are strongly convex, under mild connectivity conditions on the expected mixing matrices. In particular, our result does not require the mixing matrices to be doubly stochastic. In the experiments, we investigate a broadcast mechanism that transmits information from computing nodes to their neighbors, and confirm the linear convergence of our method on both synthetic and real-world datasets.

Speckle contrast reduction through the use of a modally-specific photonic lantern for optical coherence tomography.

A few-mode optical coherence tomography (FM-OCT) system was developed around a 2 × 1 modally-specific photonic lantern (MSPL) centered at 1310 nm. The MSPL allowed FM-OCT to acquire two coregistered images with uncorrelated speckle patterns generated by their specific coherent spread function. Here, we showed that averaging such images in vitro and in vivo reduced the speckle contrast by up to 28% and increased signal-to-noise ratio (SNR) by up to 48% with negligible impact on image spatial resolution. This method is compatible with other speckle reduction techniques to further improve OCT image quality.

Abstract 15109: Percutaneous Coronary Intervention During the Global Shortage of Iodinated Contrast

Background: A recent coronavirus-related factory shutdown led to a global shortage of iodinated contrast media. Its impact on percutaneous coronary interventions (PCI), which require contrast, has not been investigated. Research Question: The purpose of the study was twofold: (1) to evaluate the impact of the contrast shortage on PCI procedure volumes, patient and procedure characteristics and (2) to evaluate whether those changes impacted patient outcomes, especially acute kidney injury (AKI). Methods: Using CathPCI registry data from 19 Viriginia hospitals, we evaluated two time periods: pre-shortage (May 2021 - April 2022) and during the contrast shortage (May 2022 - October 2022). We compared procedure volumes, patient and procedure characteristics, and short-term outcomes. Of primary interest was whether a reduction in contrast would reduce the incidence of AKI. Results: There were 8,980 patients in the pre-shortage period and 4,046 during the shortage. Procedure volumes per hospital and patient characteristics remained similar. Multivessel procedures declined during the shortage (45.3% vs. 42.8%, p=0.007). There was a sharp decline in contrast per procedure during the shortage (149.9 ± 68.1 mL per case to 137.5 ± 62.4 mL (p&lt;0.0001) that began at the start of the shortage and then continued throughout. There were no differences in patient outcomes, including AKI (7.9% versus 7.4%, p=0.40), between study periods. When limited to patients at increased risk of AKI, there remained no difference in AKI between the study periods, despite a similar decrease in contrast volume in that cohort. Multivariable analysis showed strong correlation between baseline risk of AKI and subsequent AKI (p&lt;0.0001), but no impact of procedure characteristics or the timing of the PCI. Conclusions: The global shortage of iodinated contrast led to a significant decline in contrast use during PCI, with no impact on patient outcomes.

Electrochemical sensor based on onion-like carbon (OLC) materials modified electrode for sensitive determination of 4-nitrophenol

Due to its advantageous features, the electrochemical technique has garnered considerable attention in detecting 4-nitrophenol (4-NP). And the modified materials of such working electrode significantly influence the sensitivity of the electrochemical sensor. In this investigation, a sensitive and selective electrochemical sensor for detecting 4-NP in real water samples was developed by using a spherical onion-like carbon (OLC) as a new material for electrode modification. By comparing with generally used carbon nanotube (CNT), OLC exhibits higher electroactive surface area and conductibility. The electroactive surface area of OLC modified glassy carbon electrode (OLC/GCE) and CNT modified GCE (CNT/GCE) was determined to be 0.1508 cm2 and 0.1231 cm2 respectively. Notably, the OLC/GCE (Ipc:−139 μA) exhibited a significantly higher reduction peak current for 4-NP reduction in contrast to CNT/GCE (Ipc:−94 μA) determined by the cyclic voltammetry test. Remarkably, under ideal conditions, the OLC/GCE displayed a linear detection range for 4-NP spanning from 0.05 to 120 μM, with an exceptionally low identifying limit of 3.74 nM. In addition, OLC/GCE exhibits excellent selectivity, reproducibility, and repeatability for determining 4-NP. And the current response remains about 92% of its primal value 15 days later, demonstrating an outstanding stability of OLC/GCE. Furthermore, OLC/GCE was successfully employed in determining 4-NP in authentic water samples, yielding satisfactory recovery rates ranging from 96.14% to 102.1%. This investigation exemplifies the outstanding sensing capabilities and promising potential of OLC in the field of electrochemically detecting 4-NP in environmental settings, which provides a new method for the simple, sensitive and quantitative detection of 4-NP.

Renal protection CT protocol using low-dose and low-concentration iodine contrast medium in at-risk patients of HCC and with chronic kidney disease: a randomized controlled non-inferiority trial

BackgroundAlthough efforts have been made to reduce the dose of Contrast Medium (CM) to improve patient safety, there are ongoing concerns regarding its potential effects on image quality and diagnostic performance. Moreover, research is lacking to establish a lower limit for safe and effective CM dose reduction. To determine whether the image quality of contrast-enhanced liver computed tomography (CT) using a reduced amount of iodinated CM was similar to that of standard liver CT.MethodsWe enrolled participants at risk for hepatocellular carcinoma with decreased estimated glomerular filtration rates (< 60 mL/min/1.73m2). Participants were randomly assigned to the standard group or the renal protection protocol (RPP) group. In the standard group, images were reconstructed using hybrid iterative reconstruction (iDose), while in the RPP group, low monoenergetic (50-keV) images and deep learning (DL)-based iodine-boosting reconstruction were used. Four radiologists independently assessed image quality and lesion conspicuity.ResultsFifty-two participants were assigned to the standard (n = 25) or RPP (n = 27) groups. The iodine load was significantly lower in the RPP group than in the standard group (301.5 ± 1.71 vs. 524 ± 7.37 mgI/kg, P < 0.001). The 50-keV and DL-based iodine-boosting images from the RPP group exhibited higher image contrast than those from the standard group during arterial (3.60 ± 0.65, 3.75 ± 0.60, and 3.09 ± 0.43, respectively) and portal venous phases (4.01 ± 0.49, 3.86 ± 0.42, and 3.21 ± 0.31, respectively) (P < 0.05 for all). Overall image quality was superior in the RPP group (P < 0.05 for all). No significant difference in lesion conspicuity was observed (P > 0.017).ConclusionsThe reduction in image contrast and overall image quality caused by decreased CM can be restored using either low monoenergetic imaging or DL-based iodine-boosting reconstruction.Trial registrationclinicaltrials.gov, NCT04024514, Registered July 18, 2019, prospectively registered, https://classic.clinicaltrials.gov/ct2/show/NCT04024514.