Dual Contrast Research Articles

CSSRS: Pore segmentation method of sandstone cast thin section images based on weak supervised learning

Semantic segmentation based on deep learning has been gradually applied to digital rocks, but the amount of training sets and the fineness of data labelling affect the accuracy of the final segmentation, as well as the pixel-level annotation would cost a lot of human effort and be accompanied by certain errors. To address the problem of high data labelling cost of sandstone cast thin section images and segmentation accuracy affected by manual bias, this paper proposes a new method combining Smoothed Score Class Activation Mapping (SSCAM) and Restricted Similarity Dual Contrast Learning (RSimDCL), and we named it CSSRS. Using an image-level annotated dataset, the study applied CSSRS to the images with pore segments (pore images) presented and those without (background images). CSSRS utilized SSCAM to obtain the general location of the pores from the cast thin section map, adopted RSimDCL to obtain the fine boundaries of the pore area, and combined these two methods to generate more accurate pore segmentation of sandstone cast thin section images. Finally, pore segmentation was performed on the same test dataset using CSSRS and RSimDCL, and the segmentation results were compared with various Class Activation Mapping (CAM) algorithms based on weakly supervised learning and various Generative Adversarial Networks (GAN) algorithms. Results showed that CSSRS demonstrated the highest Precision of 79.35% (18.65% higher than the second best method RSimDCL). In addition, the study applied Mean Intersection over Union (MIoU), Dice, Pixel Accuracy (PA), False Positive Rate (FPR) comparison metrics and found that CSSRS consistently demonstrated a better performance of 5%–6% improvement than the second best method in each metric. In conclusion, the CSSRS method based on weakly supervised learning can be applied to pore segmentation of sandstone cast thin section images, which significantly reduces the data annotation workload, avoids the manual bias, and also increases the segmentation effect to a greater extent.

Unpaired sonar image denoising with simultaneous contrastive learning

Implementing underwater sonar image denoising algorithms through unpaired clean and noisy images is feasible and meaningful, since compliant paired real underwater sonar data is nearly impractical. Nevertheless, acquiring high-performance denoising algorithm is challenging without pairs of data as learning objects. In addition, the wide domain gap between noisy and clean samples as well as the noise contained in underwater sonar images is significantly more complex than that involved in normal images. Therefore, directly utilizing current unpaired optimization algorithms (e.g. generative adversarial learning and circular consistency learning) in underwater sonar image denoising tasks is not sufficient to explore the essential connection from noisy inputs to clean outputs. In this paper, we propose a novel framework for unpaired sonar image denoising which probes the same features of unpaired samples through a dual contrast optimization approach in latent space, named as NCD-GAN. The designed algorithm is composed of two basic parts: Simultaneous Transition Structure (STS) and Separable Contrast Unit (SCU). Separately, STS adopts a cyclic structure of multi-domain transformation to generate valuable surface information, and mines potential feature distributions between different domains via the addition of bidirectional mappings. Meanwhile, SCU introduces additional constraints in layer domains to encourage closer background layers between adjacent domains, while increasing the distance between noise layers so as to better promote the removal of noise and assist in image restoration. Sufficient experiments confirm that our algorithm provides better performance than existing unpaired denoising algorithms on multiple publicly available sonar image datasets and exhibits excellent robustness in the processing of multiple noises. Compared to the latest denoising algorithms, there is at most a 26.43% improvement in the evaluation metrics. Furthermore, it gains a 22.75% mAP boost on the subsequent advanced visual tasks.

Concurrent Dual-Contrast Enhancement Using Fe3O4 Nanoparticles to Achieve a CEST Signal Controllability.

Traditional T2 magnetic resonance imaging (MRI) contrast agents have defects inherent to negative contrast agents, while chemical exchange saturation transfer (CEST) contrast agents can quantify substances at trace concentrations. After reaching a certain concentration, iron-based contrast agents can "shut down" CEST signals. The application range of T2 contrast agents can be widened through a combination of CEST and T2 contrast agents, which has promising application prospects. The purpose of this study is to develop a T2 MRI negative contrast agent with a controllable size and to explore the feasibility of dual contrast enhancement by combining T2 with CEST contrast agents. The study was carried out in vitro with HCT-116 human colon cancer cells. A GE SIGNA Pioneer 3.0 T medical MRI scanner was used to acquire CEST images with different saturation radio-frequency powers (1.25/2.5/3.75/5 μT) by 2D spin echo-echo planar imaging (SE-EPI). Magnetic resonance image compilation (MAGiC) was acquired by a multidynamic multiecho 2D fast spin-echo sequence. The feasibility of this dual-contrast enhancement method was assessed by scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, dynamic light scattering, ζ potential analysis, inductively coupled plasma, X-ray photoelectron spectroscopy, X-ray powder diffraction, vibrating-sample magnetometry, MRI, and a Cell Counting Kit-8 assay. The association between the transverse relaxation rate r2 and the pH of the iron-based contrast agents was analyzed by linear fitting, and the linear relationship between the CEST effect in different B1 fields and pH was analyzed by the ratio method. Fe3O4 nanoparticles (NPs) with a mean particle size of 82.6 ± 22.4 nm were prepared by a classical process, and their surface was successfully modified with -OH active functional groups. They exhibited self-aggregation in an acidic environment. The CEST effect was enhanced as the B1 field increased, and an in vitro pH map was successfully plotted using the ratio method. Fe3O4 NPs could stably serve as reference agents at different pH values. At a concentration of 30 μg/mL, Fe3O4 NPs "shut down" the CEST signals, but when the concentration of Fe3O4 NPs was less than 10 μg/mL, the two contrast agents coexisted. The prepared Fe3O4 NPs had almost no toxicity, and when their concentration rose to 200 μg/mL at pH 6.5 or 7.4, they did not reach the half-maximum inhibitory concentration (IC50). Fe3O4 magnetic NPs with a controllable size and no toxicity were successfully synthesized. By combining Fe3O4 NPs with a CEST contrast agent, the two contrast agents could be imaged simultaneously; at higher concentrations, the iron-based contrast agent "shut down" the CEST signal. An in vitro pH map was successfully plotted by the ratio method. CEST signal inhibition can be used to realize the pH mapping of solid tumors and the identification of tumor active components, thus providing a new imaging method for tumor efficacy evaluation.

Automated quantitative myocardial perfusion cardiac magnetic resonance with artificial intelligence-based arterial input function correction versus fractional flow reserve

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Wellcome Trust. Background Quantitative myocardial perfusion cardiac magnetic resonance (QP-CMR) is gaining importance in clinical practice due to the increasing availability of automated processing software. However, a significant obstacle in the widespread deployment of QP-CMR is the accurate estimation of the arterial input function (AIF), as this is precluded by the non-linear relationship between observed MR signal and the concentration of contrast agent. Modern solutions favour a dual-saturation acquisition sequence, acquiring specific low-resolution images with a short saturation time to minimise the signal saturation of the AIF. However, this approach is limited by the availability of the pulse sequence. Recently, the AI-AIF has been proposed (1), which uses an artificial intelligence (AI)-based correction of the AIF, without the need for a dual-sequence or dual-bolus acquisition. Purpose In this work, we compare QP-CMR values, quantified with the AI-AIF versus the invasive reference standard: fractional flow reserve (FFR), to identify diagnostically significant coronary artery disease (CAD), as defined by FFR < 0.80. Methods This study enrolled 18 patients (mean age 65 (± 11), 8 female) who underwent stress perfusion CMR (Philips Achieva 3T) and invasive FFR assessment of at least one coronary artery. Perfusion was quantified in a fully-automated manner with motion compensation (2) and AI-based segmentation and processing (3), using a Fermi function-constrained deconvolution. Fully automated pixelwise perfusion maps were generated, and further subdivided into 16 myocardial segments. Segments were assigned to their respective perfusion territory (4) and the QP value for a territory is given as the mean of its two lowest segments. Receiver operating characteristic (ROC) analysis was performed to identify cut-off thresholds and report the diagnostic accuracy of the proposed approach. Results The overall mean QP-CMR value was 2.29 ml/min/g (± 0.32). Figure 1 shows QP maps and FFR values for 3 example patients. The mean QP-CMR value was 2.09 ml/min/g (± 0.36) in patients with significant CAD and 2.95 ml/min/g (± 0.48) in patients without significant CAD. In total, 28 vessels underwent FFR assessment with 12 vessels in 10 patients being positive for significant CAD (FFR < 0.80). The mean QP-CMR value in FFR-positive coronary territories was 1.76 ml/min/g (± 0.37). The ROC curves are shown in Figure 2 on a per-vessel (left) and per-patient (right) level. The optimal QP threshold on a per-vessel level is 2.11 ml/min/g and on a per-patient level is 1.96 ml/min/g. Conclusion QP-CMR is feasible with the AI-AIF and does not require dual-sequence acquisition or a dual contrast injection. The QP values derived after the AI-based correction of the AIF show good agreement with FFR assessment of epicardial coronary disease. However, due to the limited sample size, further work is required to confirm these findings.

Dual Labeling of Primary Cells with Fluorescent Gadolinium Oxide Nanoparticles

The interest in mesenchymal stromal cells as a therapy option is increasing rapidly. To improve their implementation, location, and distribution, the properties of these must be investigated. Therefore, cells can be labeled with nanoparticles as a dual contrast agent for fluorescence and magnetic resonance imaging (MRI). In this study, a more efficient protocol for an easy synthesis of rose bengal–dextran-coated gadolinium oxide (Gd2O3-dex-RB) nanoparticles within only 4 h was established. Nanoparticles were characterized by zeta potential measurements, photometric measurements, fluorescence and transmission electron microscopy, and MRI. In vitro cell experiments with SK-MEL-28 and primary adipose-derived mesenchymal stromal cells (ASC), nanoparticle internalization, fluorescence and MRI properties, and cell proliferation were performed. The synthesis of Gd2O3-dex-RB nanoparticles was successful, and they were proven to show adequate signaling in fluorescence microscopy and MRI. Nanoparticles were internalized into SK-MEL-28 and ASC via endocytosis. Labeled cells showed sufficient fluorescence and MRI signal. Labeling concentrations of up to 4 mM and 8 mM for ASC and SK-MEL-28, respectively, did not interfere with cell viability and proliferation. Gd2O3-dex-RB nanoparticles are a feasible contrast agent to track cells via fluorescence microscopy and MRI. Fluorescence microscopy is a suitable method to track cells in in vitro experiments with smaller samples.

Ex situ and in situ functionalized Yb/Fe nanoparticles obtained by scanning pulsed laser ablation in liquids: A route to obtain biofunctionalized multiplatform contrast agents for MRI and CT imaging

Two distinct strategies were used to improve the colloidal properties of hybrid Yb/Fe oxide NPs previously prepared by the pulsed liquid laser ablation process for use as a contrast agent in medical imaging.First, an exhaustive optimization process of the laser ablation synthesis parameters was carried out to reduce the hydrodynamic diameters of the Yb/Fe NPs. The hydrodynamic size was successfully reduced to <200 nm, thereby decreasing the polydispertivity index.Second, ex situ and in situ functionalization processes using glutathione, cysteamine, or polyethylenimine as capping agents have been developed to increase their colloidal stability at physiological pH values. Transmission electron microscopy, dynamic light scattering, Z-potential measurements, and Fourier Transform Infrared spectroscopy were used to examine the structure, morphology, colloidal and surface properties of Yb/Fe NPs.Colloidal stability of the Yb/Fe NPs as well as the linkage mechanism of functionalization have been studied extensively. This last parameter provides a critical information for subsequently bioconjugations in biomedical applications. Additionally, the biocompatibility of the Yb/Fe NPs was evaluated by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) experiments. These results indicate more appropriate colloidal characteristics and higher biocompatibility for ex situ-functionalized Yb/Fe NPs, especially when the capping agent is glutathione. Additionally, these Yb/Fe NPs show good magnetic resonance imaging and X-ray computerized tomography imaging abilities, thereby indicating promising potential as dual contrast agents.

An attentive-based generative model for medical image synthesis

Magnetic resonance (MR) and computer tomography (CT) imaging are valuable tools for diagnosing diseases and planning treatment. However, limitations such as radiation exposure and cost can restrict access to certain imaging modalities. To address this issue, medical image synthesis can generate one modality from another, but many existing models struggle with high-quality image synthesis when multiple slices are present in the dataset. This study proposes an attention-based dual contrast generative model, called ADC-cycleGAN, which can synthesize medical images from unpaired data with multiple slices. The model integrates a dual contrast loss term with the CycleGAN loss to ensure that the synthesized images are distinguishable from the source domain. Additionally, an attention mechanism is incorporated into the generators to extract informative features from both channel and spatial domains. To improve performance when dealing with multiple slices, the $K$-means algorithm is used to cluster the dataset into $K$ groups, and each group is used to train a separate ADC-cycleGAN. Experimental results demonstrate that the proposed ADC-cycleGAN model produces comparable samples to other state-of-the-art generative models, achieving the highest PSNR and SSIM values of 19.04385 and 0.68551, respectively. We publish the code at https://github.com/JiayuanWang-JW/ADC-cycleGAN.

Incorporation of Tb and Gd improves the diagnostic functionality of magnetotactic bacteria.

Magnetotactic bacteria are envisaged as potential theranostic agents. Their internal magnetic compass, chemical environment specificity and natural motility enable these microorganisms to behave as nanorobots, as they can be tracked and guided towards specific regions in the body and activated to generate a therapeutic response. Here we provide additional diagnostic functionalities to magnetotactic bacteria Magnetospirillum gryphiswaldense MSR-1 while retaining their intrinsic capabilities. These additional functionalities are achieved by incorporating Tb or Gd in the bacteria by culturing them in Tb/Gd supplemented media. The incorporation of Tb provides luminescence properties, enabling potential applications of bacteria as biomarkers. The incorporation of Gd turns bacteria into dual contrast agents for magnetic resonance imaging, since Gd adds T1 contrast to the existing T2 contrast of unmodified bacteria. Given their potential clinical applications, the diagnostic ability of the modified MSR-1 has been successfully tested in vitro in two cell models, confirming their suitability as fluorescent markers (Tb-MSR-1) and dual contrast agents for MRI (Gd-MSR-1).

Biotin-Conjugated Upconversion KMnF3/Yb/Er Nanoparticles for Metabolic Magnetic Resonance Imaging of the Invasive Margin of Glioblastoma

The incomplete detection of glioblastoma or the remnants of surgery constitute the primary reasons underlying glioblastoma recurrence. However, the current commercial MR Gd-based contrast agents (GBCAs) are not specialized, thus posing a potentially high risk of missed diagnosis or inaccurate boundary identification. MR contrast agents have been widely explored in conjugation with tumor-specific markers to preoperatively diagnose and identify the intraoperative positioning of glioblastoma. Herein, we developed a type of upconversion nanoparticle (UCNP), KMnF3:18%Yb, 2%Er, via the typical hydrothermal method, which possesses unique MR upconversion luminescence bimodal imaging. Furthermore, UCNPs were transformed to the aqueous phase by replacing oleic acid with amine–polyethylene glycol (PEG)–thiol (NH2–PEG–SH), further enabling the covalent conjugation of the glioblastoma metabolic ligand biotin. The as-prepared multifunctional biotin/PEG–UCNPs were characterized using various techniques. Moreover, their biocompatibility, especially with the brain, was systematically assessed via histological and hematological examinations. The results indicated negligible toxicity in vivo. As a proof of concept, biotin/PEG–UCNPs exhibited considerable potential for accurate definition of glioma infiltration boundaries for surgery as metabolic dual imaging contrast agents.

Underwater image enhancement combining dual color space and contrast learning

With the continuous increase of human society’s demand for marine resources, underwater vision plays an increasingly important role in marine information acquisition. However, due to the absorption and scattering of light, underwater images have characteristics such as blurring, low contrast, and color distortion, which are not conducive to the acquisition of underwater image information. The rapid development of deep learning and powerful feature learning capabilities have been widely used in underwater image enhancement tasks, but current research still has problems such as loss of local details and oversaturation. Aiming at the above problems, this paper proposes an underwater image enhancement method combining dual color space and contrast learning, including RGB color space and Lab color space. The RGB color space is used for color correction and artifact removal, and the Lab color space is used to enhance saturation and detail texture. The contrastive learning of positive and negative samples makes the generated image closer to the clear underwater image and deviates from the real underwater image. Experimental results on real underwater datasets show that the proposed method achieves the best results on PSNR, SSIM and UIQM compared to other underwater image enhancement methods.

Synthesis and characterization of Gd3+-loaded hyaluronic acid-polydopamine nanoparticles as a dual contrast agent for CT and MRI scans

Magnetic resonance imaging and computed tomography (CT) suffer from low contrast sensitivity and potential toxicity of contrast agents. To overcome these limitations, we developed and tested a new class of dual contrast agents based on polydopamine nanoparticles (PDA-NPs) that are functionalized and targeted with hyaluronic acid (HA). These nanoparticles (NPs) are chelated with Gd3+ to provide suitable contrast. The targeted NPs were characterized through ultraviolet–visible spectroscopy (UV–vis), scanning electron microscopy (SEM), infrared Fourier transform (FTIR), and dynamic light scattering (DLS). The cytotoxicity was investigated on HEK293 cells using an MTT assay. The contrast property of synthesized Gd3+/PDA/HA was compared with Barium sulfate and Dotarem, as commercial contrast agents (CAs) for CT and MRI, respectively. The results illustrated that synthesized PDA-NPs have a spherical morphology and an average diameter of 72 nm. A distinct absorption peak around 280 nm in the UV–vis spectrum reported the self-polymerization of PDA-NPs. The HA coating on PDA-NPs was revealed through a shift in the FTIR peak of C=O from 1618 cm−1 to 1635 cm−1. The Gd3+ adsorption on PDA/HA-NPs was confirmed using an adsorption isotherm assay. The developed CA showed low in vitro toxicity (up to 158.98 µM), and created a similar contrast in MRI and CT when compared to the commercial agents. The r1 value for PDA/HA/Gd3+ (6.5 (mg/ml)−1 s−1) was more than Dotarem (5.6 (mg/ml)−1 s−1) and the results of the hemolysis test showed that at concentrations of 2, 4, 6, and 10 mg/ml, the hemolysis rate of red blood cells is very low. Additionally, the results demonstrated that PDA/HA/Gd3+ could better target the CD44+-expressing cancer cells than PDA/Gd3+. Thus, it can be concluded that lower doses of developed CA are needed to achieve similar contrast of Dotarem, and the developed CA has no safety concerns in terms of hemolysis. The stability of PDA/HA/Gd3+ has also been evaluated by ICP-OES, zeta potential, and DLS during 3 days, and the results suggested that Gd-HA NPs were stable.

Radiolabeled Iron Oxide Nanoparticles as Dual Modality Contrast Agents in SPECT/MRI and PET/MRI.

During the last decades, the utilization of imaging modalities such as single photon emission computed tomography (SPECT), positron emission tomography (PET), and magnetic resonance imaging (MRI) in every day clinical practice has enabled clinicians to diagnose diseases accurately at early stages. Radiolabeled iron oxide nanoparticles (RIONs) combine their intrinsic magnetic behavior with the extrinsic character of the radionuclide additive, so that they constitute a platform of multifaceted physical properties. Thus, at a practical level, RIONs serve as the physical parent of the so-called dual-modality contrast agents (DMCAs) utilized in SPECT/MRI and PET/MRI applications due to their ability to combine, at real time, the high sensitivity of SPECT or PET together with the high spatial resolution of MRI. This review focuses on the synthesis and in vivo investigation of both biodistribution and imaging efficacy of RIONs as potential SPECT/MRI or PET/MRI DMCAs.

Nanodiamonds Doped with Manganese for Applications in Magnetic Resonance Imaging.

Nanodiamonds (NDs) are emerging with great potential in biomedical applications like biomarking through fluorescence and magnetic resonance imaging (MRI), targeted drug delivery, and cancer therapy. The magnetic and optical properties of NDs could be tuned by selective doping. Therefore, we report multifunctional manganese-incorporated NDs (Mn-NDs) fabricated by Mn ion implantation. The fluorescent properties of Mn-NDs were tuned by inducing the defects by ion implantation and enhancing the residual nitrogen vacancy density achieved by a two-step annealing process. The cytotoxicity of Mn-NDs was investigated using NCTC clone 929 cells, and the results revealed no cytotoxicity effect. Mn-NDs have demonstrated dual mode contrast enhancement for both T 1- and T 2-weighted in vitro MR imaging. Furthermore, Mn-NDs have illustrated a significant increase in longitudinal relaxivity (fivefold) and transversal relaxivity (17-fold) compared to the as-received NDs. Mn-NDs are employed to investigate their ability for in vivo MR imaging by intraperitoneal (ip) injection of Mn-NDs into mice with liver tumors. After 2.5 h of ip injection, the enhancement of contrast in T 1- and T 2-weighted images has been observed via the accumulation of Mn-NDs in liver tumors of mice. Therefore, Mn-NDs have great potential for in vivo imaging by MR imaging in cancer therapy.

Multi-behavioral recommendation model based on dual neural networks and contrast learning.

In order to capture the complex dependencies between users and items in a recommender system and to alleviate the smoothing problem caused by the aggregation of multi-layer neighborhood information, a multi-behavior recommendation model (DNCLR) based on dual neural networks and contrast learning is proposed. In this paper, the complex dependencies between behaviors are divided into feature correlation and temporal correlation. First, we set up a personalized behavior vector for users and use a graph-convolution network to learn the features of users and items under different behaviors, and we then combine the features of self-attention mechanism to learn the correlation between behaviors. The multi-behavior interaction sequence of the user is input into the recurrent neural network, and the temporal correlation between the behaviors is captured by combining the attention mechanism. The contrast learning is introduced based on the double neural network. In the graph convolution network layer, the distances between users and similar users and between users and their preference items are shortened, and the distance between users and their short-term preference is shortened in the circular neural network layer. Finally, the personalized behavior vector is integrated into the prediction layer to obtain more accurate user, behavior and item characteristics. Compared with the sub-optimal model, the HR@10 on Yelp, ML20M and Tmall real datasets are improved by 2.5%, 0.3% and 4%, respectively. The experimental results show that the proposed model can effectively improve the recommendation accuracy compared with the existing methods.

Dual visual impairment and contrast sensitivity among community-dwelling older adults: a cross-sectional study from the PrevQuedas Brazil trial

Objective: To investigate the importance of improving visual assessment for community-dwelling older adult fallers. Methods: This cross-sectional study included eligible community-dwelling older adults (&gt; 60 years of age) from the PrevQuedas Brazil clinical trial who had suffered at least 1 fall in the last 12 months (n = 703). We evaluated visual acuity and contrast sensitivity using the Snellen E chart and low contrast visual acuity tests; those with both impairment types were considered to have dual visual impairment. Results: Visual acuity, low contrast sensitivity, and dual visual impairment were associated with recurrent falls (OR 1.85); visual impairment was more prevalent among the oldest old. Approximately 90.00% of the participants reported using glasses, and 63.80% used multifocal lenses. Dual impairment was identified in 143 (20.30%) participants. In multivariate logistic regression, the predictive variables for recurrent falls were low contrast sensitivity (95%CI 1.15–2.47), dual visual impairment (95%CI 1.16–2.83), and self-perceived fall risk (95%CI 1.16–2.46) which was measured using the Falls Efficacy Scale-International. Conclusion: Older adults with dual visual impairment are more likely to suffer recurrent falls. Low contrast sensitivity is crucial for fall risk assessment. Trial registration: ClinicalTrials.gov (NCT01698580).

Infrared Small Target Detection Based on Local Contrast-Weighted Multidirectional Derivative

Realizing robust infrared small target detection in complex backgrounds is of great essence for infrared search and tracking (IRST) applications. However, the high-intensity structures in background regions, such as the sharp edges, make it a challenging task, especially when the target is with low signal-to-clutter ratio (SCR). To address this issue, we propose an infrared small target detection method using local contrast-weighted multidirectional derivative (LCWMD). It is a robust detector that comprehensively considers the target property, background information, and the relation between them. First, we take into account the approximate isotropy of the infrared small target and present a new multidirectional derivative with penalty factors based on the Facet model to develop the target salience in the local region. Second, a dual local contrast fusion model with the tri-layer design is introduced to amplify the difference between the target and the background, so as to further suppress the high-intensity structural clutters. Finally, the LCWMD map is obtained by weighting the above two filtered maps, after which an adaptive segmentation operation is applied to accomplish the target detection. The results of comparative experiments implemented on real infrared images demonstrate that our method outperforms other state-of-the-art detectors by several times in terms of signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF).

PDGF-B conjugating mesoporous IO/GdO nanocomposites for accurate diagnosis of orthotopic prostatic cancer through T1-T2 dual-modal MRI contrast enhancement

The development of highly efficient magnetic resonance imaging (MRI) contrast agent contributes to the early and accurate diagnosis of cancer. Herein, we developed a novel iron oxide and gadolinium oxide (IO/GdO) nanocomposite as T1-T2 dual modal MRI contrast agent through hydrothermal synthesis method. Subsequently, tumor-targeting ligand (PDGFB-PEG) was further conjugated to the surface of IO/GdO nanocomposite (PDGFB-pIO/GdO) to fabricate tumor-targeting MRI probe for accurate diagnosis of orthotopic prostatic cancer. IO/GdO nanocomposite exhibited obvious mesoporous structure, which would promote the contact between water molecules and Gd or Fe centers and enhance the T1 relaxation rate (r1) and T2 relaxation rate (r2) of IO/GdO composite. In addition, the r1 and r2 values of IO/GdO composite were up to be 11.6 mM−1s−1 and 102.7 mM−1s−1, respectively, and the r2/r1 ratio of IO/GdO nanocomposite was calculated to be 8.85, demonstrating a great potential as dual-mode MRI contrast agent. Further study indicated that PDGFB-pIO/GdO exhibited an excellent biocompatibility. Besides, PDGFB-pIO/GdO presented a higher accumulation in tumor than untargeted pIO/GdO, confirming its excellent in vivo tumor-targeting ability. Lastly, in orthotopic prostatic cancer model, the systemic delivery of PDGFB-pIO/GdO significantly enhanced T1- and T2-weighted signal of orthotopic prostatic cancer, achieving high quality of MR images and realizing accurate diagnosis of orthotopic prostatic cancer. Collectively, we proposed a rational designed nanoplatform, PDGF-B conjugating mesoporous IO/GdO nanocomposites, suggesting potential for diagnostic identification of prostate cancer via T1-T2 dual-modal MRI contrast enhancement.

Capillary pressure mediated long-term dynamics of thin soft films

HypothesisThe conventional solid–solid contact is well studied in the literature. However, a number of practical applications, such as adhesive patches and biomimetic surfaces, require a much deeper understanding of soft contact where there is a distinct time-dependent adhesion behavior due to the dual-phase structure (solids and liquids). To understand this, currently existing solid–solid contact behavior is extrapolated to soft contact, wherein the size-effect of the gel film and the preload are typically neglected. When introducing the finite-size effect and preload, gels could experience distinctive long-term contact dynamics in contact with another material. ExperimentsWe reconstruct the evolving surface profile of the gel films intercalated between a glass sphere and glass slide using dual wavelength-reflection interference contrast microscopy. The macro-sized glass sphere compresses the gel. The indentation depth is comparable to the gel film thickness, wherein the conventional contact theories are inapplicable. FindingsThe gel surface experiences two deformation stages. The natural preload and elastic force develop the contact area in the early state. In the later state, the viscous free molecules of the gel develop the ridge. We discover that the residual surface stress relaxes over 85 hr. Our findings on the long-term gel deformation provide a new perspective on soft adhesion, from developing soft adhesives to understanding biological tissues.

The application of magnetic resonance ultrasmall magnetic Fe3O4 nano bimodal contrast agent in rabbit orthotopic liver tumors

AbstractTo investigate the diagnostic value of ultra‐small superparamagnetic Fe3O4 nanoparticles as dual modal contrast agent in rabbit orthotopic liver tumor. Twenty‐four New Zealand rabbits with orthotopic liver tumor were randomly divided into experimental group and control group. The experimental group contained 12 rabbits which intravenously injected with Fe3O4 nanoparticles and the control group contained 12 rabbits which intravenously injected with Gd‐DTPA. After injection, the cross‐sectional T1 and T2‐weighted scan images of experimental group and cross‐sectional T1‐weighted scan images of control group were obtained at the different timepoints (including 0, 5, 15, 20, 30, 45, 60, 75, 90, 120, 180, and 240 min) with a clinical 3.0 T MRI scanner. The SNR value of the tumor site and the normal liver site at different time points were calculated, respectively, and then the signal change value (CNR) of the tumor relative to the normal liver tissue was obtained. The r1 value of Fe3O4 nanoparticles was significantly higher than Gd‐DTPA (6.97 mM−1 s−1 for Fe3O4 nanoparticles and 4.75 mM−1 s−1 for Gd‐DTPA and only Fe3O4 nanoparticles exhibited the r2 relaxation rate which was 22.53 mM−1 s−1). In addition, the r2/r1 ratio of Fe3O4 was 3.23 which also demonstrated the strong MR relaxation characteristics of Fe3O4 nanoparticles. Compare with clinical used MRI contrast agent (Gd‐DTPA), the ultra‐small magnetic Fe3O4 nanoparticles not only showed the better T1 contrast enhancement but also exhibited the T1/T2 dual‐modal enhanced contrast ability, which could improve the accuracy and sensitivity of MRI diagnosis and realize the early diagnosis of diseases in prospect.

Finger-Vein Image Dual Contrast Adjustment and Recognition Using 2D-CNN

The suggested process enhances the low contrast of the finger-vein image using dual contrast adaptive histogram equalization (DCLAHE) for visual attributes. The finger-vein histogram intensity is split out all over the image when dual CLAHE is used. For preprocessing, the finger-vein image dataset is obtained from the SDUMLA-HMT finger-vein database. Following the deployment of DCLAHE, the updated dataset is used to recognize objects using an improved 2D-CNN model. The 2D CNN model learns features by optimizing values of a preprocessed dataset. The accuracy of this model stands at 91.114%.