Image Contrast Research Articles

Multimodal optoacoustic imaging: methods and contrast materials.

Optoacoustic (OA) imaging offers powerful capabilities for interrogating biological tissues with rich optical absorption contrast while maintaining high spatial resolution for deep tissue observations. The spectrally distinct absorption of visible and near-infrared photons by endogenous tissue chromophores facilitates extraction of diverse anatomic, functional, molecular, and metabolic information from living tissues across various scales, from organelles and cells to whole organs and organisms. The primarily blood-related contrast and limited penetration depth of OA imaging have fostered the development of multimodal approaches to fully exploit the unique advantages and complementarity of the method. We review the recent hybridization efforts, including multimodal combinations of OA with ultrasound, fluorescence, optical coherence tomography, Raman scattering microscopy and magnetic resonance imaging as well as ionizing methods, such as X-ray computed tomography, single-photon-emission computed tomography and positron emission tomography. Considering that most molecules absorb light across a broad range of the electromagnetic spectrum, the OA interrogations can be extended to a large number of exogenously administered small molecules, particulate agents, and genetically encoded labels. This unique property further makes contrast moieties used in other imaging modalities amenable for OA sensing.

A Novel Lightweight Model for Underwater Image Enhancement

Underwater images suffer from low contrast and color distortion. In order to improve the quality of underwater images and reduce storage and computational resources, this paper proposes a lightweight model Rep-UWnet to enhance underwater images. The model consists of a fully connected convolutional network and three densely connected RepConv blocks in series, with the input images connected to the output of each block with a Skip connection. First, the original underwater image is subjected to feature extraction by the SimSPPF module and is processed through feature summation with the original one to be produced as the input image. Then, the first convolutional layer with a kernel size of 3 × 3, generates 64 feature maps, and the multi-scale hybrid convolutional attention module enhances the useful features by reweighting the features of different channels. Second, three RepConv blocks are connected to reduce the number of parameters in extracting features and increase the test speed. Finally, a convolutional layer with 3 kernels generates enhanced underwater images. Our method reduces the number of parameters from 2.7 M to 0.45 M (around 83% reduction) but outperforms state-of-the-art algorithms by extensive experiments. Furthermore, we demonstrate our Rep-UWnet effectively improves high-level vision tasks like edge detection and single image depth estimation. This method not only surpasses the contrast method in objective quality, but also significantly improves the contrast, colorimetry, and clarity of underwater images in subjective quality.

Rational Design and Comparison of Novel 99mTc-Labeled FAPI Dimers for Visualization of Multiple Tumor Types.

Recognizing the significance of SPECT in nuclear medicine and the pivotal role of fibroblast activation protein (FAP) in cancer diagnosis and therapy, this study focuses on the development of 99mTc-labeled dimeric HF2 with high tumor uptake and image contrast. The dimeric HF2 was synthesized and radiolabeled with 99mTc in one pot using various coligands (tricine, TPPTS, EDDA, and TPPMS) to yield [99mTc]Tc-TPPTS-HF2, [99mTc]Tc-EDDA-HF2, and [99mTc]Tc-TPPMS-HF2 dimers. SPECT imaging results indicated that [99mTc]Tc-TPPTS-HF2 exhibited higher tumor uptake and tumor-to-normal tissue (T/NT) ratio than [99mTc]Tc-EDDA-HF2 and [99mTc]Tc-TPPMS-HF2. Notably, [99mTc]Tc-TPPTS-HF2 exhibited remarkable tumor accumulation and retention in HT-1080-FAP and U87-MG tumor-bearing mice, thereby surpassing the monomeric [99mTc]Tc-TPPTS-HF. Moreover, [99mTc]Tc-TPPTS-HF2 achieved acceptable T/NT ratios in the hepatocellular carcinoma patient-derived xenograft (HCC-PDX) model, which provided identifiable contrast and imaging quality. In conclusion, this study presents proof-of-concept research on 99mTc-labeled FAP inhibitor dimers for the visualization of multiple tumor types. Among these candidate compounds, [99mTc]Tc-TPPTS-HF2 showed excellent clinical potential, thereby enriching the SPECT tracer toolbox.

Synchrotron radiation x-ray topography applied to nitride semiconductor crystals

Gallium nitride (GaN) and aluminum nitride (AlN), as examples of third-generation semiconductors, have attracted significant interest due to their remarkable physical attributes, including a wide bandgap, high breakdown voltage, exceptional chemical stability, and high thermal conductivity. These characteristics render GaN and AlN highly promising for use in power and (opto)-electronic devices. Consequently, there is a growing demand for high-quality GaN and AlN crystals on the centimeter scale. As the dislocation density in these materials decreases, the need for a reliable method of dislocation characterization becomes more pressing. Synchrotron radiation x-ray topography (SR-XRT) has emerged as a superior, nondestructive technique for the precise characterization of crystal defects. This review briefly introduced the principle of XRT, and its application in the analysis of dislocations in GaN and AlN crystals is summarized. By examining the relationship between the SR-XRT image contrast and the Burgers vectors of dislocations, it is possible to categorize wafer dislocations and determine the magnitude and direction of Burgers vectors. Additionally, SR-XRT facilitates the analysis of interactions between dislocations in GaN and AlN crystals. These analyses are instrumental in advancing the development of superior crystals. This review concludes with a discussion of the current challenges faced by SR-XRT and a projection of its future applications in characterizing third-generation semiconductor crystal materials. This review offers significant guidance for the characterization of nitride crystal defects using SR-XRT.

Unsupervised MRI motion artifact disentanglement: introducing MAUDGAN.

This study developed an unsupervised motion artifact reduction method for MRI images of patients with brain tumors. The proposed novel design uses multi-parametric multicenter contrast-enhanced T1W (ceT1W) and T2-FLAIR MRI images.&#xD;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. &#xD;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.&#xD;Significance: Our proposed unsupervised method was demonstrated to reduce motion artifacts from the ceT1W brain images under a multi-parametric framework.

Low illumination fog noise image denoising method based on ACE-GPM.

The Perona-Malik (P-M) model exhibits deficiencies such as noise amplification, new noise introduction, and significant gradient effects when processing noisy images. To address these issues, this paper proposes an image-denoising algorithm, ACE-GPM, which integrates an Automatic Color Equalization (ACE) algorithm with a gradient-adjusted P-M model. Initially, the ACE algorithm is employed to enhance the contrast of low-light images obscured by fog and noise. Subsequently, the Otsu method, a technique to find the optimal threshold based on between-class variance, is applied for precise segmentation, enabling more accurate identification of different regions within the image. After that, distinct gradients enhance the image's foreground and background via an enhancement function that accentuates edge and detailed information. The denoising process is finalized by applying the gradient P-M model, employing a gradient descent approach to further emphasize image edges and details. Experimental evidence indicates that the proposed ACE-GPM algorithm not only elevates image contrast and eliminates noise more effectively than other denoising methods but also preserves image details and texture information, evidenced by an average increase of 0.42 in the information entropy value. Moreover, the proposed solution achieves these outcomes with reduced computational resource expenditures while maintaining high image quality.

Foveated pancake lens design for improved optical performance and eye rotation support.

We propose a novel, to our knowledge, approach to address the limitations of traditional pancake lenses for virtual reality headsets, such as low image contrast and poor performance when eyes rotate. The design leverages the foveated nature of human vision, achieving a superior modulation transfer function in the foveal area to enhance optical performance significantly. Furthermore, the pancake lens design is presented that considers the rotation of the user's pupil position, maintaining optimal image quality even when the user's eye rotates. The proposed method presents the parameters and optimization of a novel pancake lens that utilizes the characteristics of the human visual system and accounts for the rotation of the pupil position of the user, leading to improvements in image quality and user experience. The lens design and image simulation results are presented to demonstrate the effectiveness of the approach.

Contrast ratio enhancement method of a pancake virtual reality head‐mounted display

AbstractFolded optical path designs have been widely applied in Virtual Reality (Pancake VR) displays in recent years. Compared with the traditional straight‐through and Fresnel optical path design, the Pancake design has obvious advantages in smaller size and clearer virtual image. However, the Pancake optical path uses polarization devices to fold the optical path and, light undergoes multiple refractions and reflections between lens groups, thus inevitably introducing stray light. This stray light greatly reduces the contrast ratio even if taking high‐contrasting Mini‐LED backlight LCD as panel, and seriously affects the user experience. In this paper, we first analyze the causes of stray light in the Pancake design and then propose an effective optical design method. In addition, an effective process scheme for improving image contrast is proposed by analyzing the impact of lens and tube reflectivity. Further, a prototype of the Pancake VR optical module is successfully implemented with low stray light and high contrast ratio.

High Physiological 18F-FDG Uptake in Normal Pituitary Gland on Digital PET Scanner

High Physiological 18F-FDG Uptake in Normal Pituitary Gland on Digital PET Scanner

Integrated drug delivery system loading ROS-generating antitumor drugs for signal-amplified photoacoustic imaging and multimodal tumor therapy

The usefulness of antitumor drugs that produce reactive oxygen species (ROS) in accurate tumor diagnosis and treatment has been underestimated. Here, β-Lapachone (Lapa, antitumor drugs), 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS, precursor photoacoustic agents) and 2, 2′-azobis[2-(2-imidazolin-2-yl) propane] dihydrochloride (AIPH, free radical initiators) were successfully loaded into zeolitic imidazolate framework (ZIF) in situ by a one-pot method and named Lapa/ABTS/AIPH@ZIF-67 nanoparticles (LBIZ NPs). After entering the tumor cell, the ZIF structure of LBIZ NPs is disrupted in the acidic tumor microenvironment, which results in the release of Co2+ and drugs. In one hand, Lapa and Co2+ together increased hydroxyl radicals (•OH) levels to achieve Enhanced-Chemodynamic therapy (ECDT). As the amount of •OH went up, the amount of ABTS•+, the oxidation product of ABTS, would also increase, thus increasing the sensitivity and contrast of photoacoustic imaging. In the other hand, the photothermal effect produced by ABTS•+ induces AIPH to crack and release alkyl radicals (•R) to achieve thermodynamic therapy. The LBIZ NPs achieved more effective tumor treatments by fully utilizing light and heat energy. Hence, this integrated drug delivery system is able to achieve signal-amplified photoacoustic imaging and Enhanced-Chemodynamic/Photothermal/Thermodynamic trimodal combination therapy. This work provides a new insight into the application of ROS-generating antitumor drugs to precise imaging of tumors.

Barely visible impact damage detection in CFRP composite using vibrothermography with narrowband sweep excitation

The extensive use of carbon fiber-reinforced plastic (CFRP) composite materials in various industries has posed new challenges for conducting effective structural health monitoring and nondestructive evaluation techniques. Based on local defect resonance (LDR), the vibrothermography with narrowband sweep excitation is proposed to detect BVIDs in the CFRP composite. The response spectrum of displacement at the impact crater was collected by a laser Doppler vibrometer, which was used to determine the frequency range of narrowband sweep excitation. The frequency range was further validated by the analysis of local defect resonance for thermal responses, and the sweep duration was also optimized. Subsequently, the proposed vibrothermography was employed to detect BVIDs with varying severity. The impact damage could be efficiently activated and a significant temperature rise was generated in the defective areas, which enhances the temperature contrast of the thermal images and facilitates the quantification of defects.

Improving millimeter-wave imaging quality using the vortex phase method

This paper investigates a new vortex wave imaging approach to improve the imaging quality of small metal targets of size less than 1.5 mm. Antennas with different spiral phase plates are designed to efficiently transmit vortex beams with orbital angular momentums (OAMs). By analyzing the OAM spectrum of the target, it was discovered that the predominant reflection contains a particular OAM mode that carries abundant azimuthal information. This can be explained by the OAM selectivity of the target and the guidance of the vortex transmitting beam. A simple reflection vortex imaging system was designed to capture the phase information. Measurement results show that the high image contrast reaches 14.9%, which is twice as high as that of the imaging without OAM. Both of simulations and experiments demonstrate that the vortex phase imaging approach proposed in this paper can effectively improve the imaging quality at 80 GHz. This approach is suitable for other millimeter wave imaging systems and is helpful to improve the resolution in anti-terrorism security checks.

Characterizing damage evolution in fiber reinforced composites using in-situ X-ray computed tomography, deep machine learning and digital volume correlation (DVC)

In-situ X-ray computed tomography (CT) techniques enable to examine microstructural evolution of composite materials continuously and nondestructively under loading for better understanding the initiation and propagation of microscopic damages. This study explores the tensile damage behavior of notched woven carbon/epoxy composites under successive loading process. Since the image contrast of different composite material phases is normally low and conventional image segmentation techniques are hard to identify microstructures and defects in different directions distinctively, a U-Net image segmentation model based on deep machine learning algorithms is employed to characterize microstructures and material damages precisely and consistently. Digital volume correlation (DVC) approach is proposed here to measure the three-dimensional (3D) deformation fields of carbon/epoxy composites at different loading steps, and the DVC results are used to predict location of damage initiation and propagation. It is found that the damage modes of notched carbon/epoxy composites mainly include fiber breakage, longitudinal cracks in warp fiber tows, and transverse cracks in weft fiber tows. The individual fiber breakage occurs in warp fiber tows at the intermediate loading level, and longitudinal and transverse cracks appear in further loading levels. Longitudinal cracks and fiber breakage events take place in a region tangent to the notch tip and exhibit a strong interaction. The development of longitudinal cracks promotes the initiation and propagation of transverse cracks. In addition, the strain concentration regions in 3D deformation fields match well with the microscopic cracks segmented by the U-Net deep learning model. This study demonstrated that the integration of X-ray computed tomography, digital volume correlation and image semantic segmentation enables to identify damage initiation, evolution, extent and mechanism.

Reversible data hiding with automatic contrast enhancement for color images

Automatic contrast enhancement (ACE) is a technique that can automatically enhance the image contrast. Reversible data hiding (RDH) with ACE (ACERDH) can achieve ACE while hiding data. However, some methods with good performance for color images suffer from insufficient enhancement. Therefore, an ACERDH method based on the R, G, B, and V channels enhancement is proposed. First, histogram shifting with contrast control is proposed to enhance the R, G, and B channels. It can prevent contrast degradation and histogram shifting from stopping prematurely. Then, the V channel is enhanced. Since some RDH methods with non-ACE that can well enhance the V channel have a low automation level, histogram shifting with brightness control that can realize ACE very well is proposed. It can effectively avoid over-enhancement by controlling the brightness. Experimental results verify that the proposed method improves the image quality and embedding capability better than some state-of-the-art methods.

Bioactivity of radiopaque 45S5 bioactive glass with progressive additions of Bi2O3: A dissolution study under static conditions

The integration of bioactive glasses (BGs) into hard tissues in in-vivo models can be evaluated through X-ray radiographs. The image contrast plays an important role and should be enhanced to improve accurate minimal invasive diagnostics of the performance of BGs containing implants after surgery. The present work shows the effect of the progressive addition of Bi2O3 (up to 12.2 wt%) on the structure, thermal properties, radiopacity, and bioactivity of 45S5-like BG obtained by quenching of the melt prepared from oxide precursors. The BG chemical composition, phase composition, structure, chemical state, thermal properties, and radiopacity were respectively investigated by X-ray fluorescence (XRF), X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), differential scanning calorimetry (DSC), and radiopacity analysis according to the ISO13116:2014 norm. The bioactivity of the glasses was monitored by immersing them in a simulated body fluid (SBF) under static conditions. The apatite formation ability and ion concentration in SBF after 3, 7, 14, and 21 days were investigated by XRD, scanning electron microscopy (SEM), inductively coupled plasma optical emission spectroscopy (ICP-OES), and ICP mass spectrometry (ICP-MS). All samples were found to be X-ray amorphous. Simultaneous formation of [BiO3] and [BiO6] units is noted in the BG. The glass radiopacity increased up to 1.8 and 4.2 times compared to the reference glasses without Bi2O3. Bi2O3 additions in the glass slow down the formation of apatite-like structures as a result of the network forming role of [BiO3] and [BiO6] units in the BG. Nevertheless, the BGs with the 12.2 wt % Bi2O3 still shows the ability to form apatite deposits after 7 days of immersion in simulated body fluid (SBF).

Adaptive histogram equalization with visual perception consistency

Histogram equalization (HE) is a well-established method for image contrast enhancement due to its simplicity and effectiveness. However, it suffers from three main shortcomings, i.e., over-enhancement, under-enhancement and mean shift. To address these issues, this paper proposes a systematic scheme, that is, adaptive histogram equalization with visual perception consistency (AHEVPC). Firstly, a novel histogram correction model is designed to get the optimal controlling parameters, which specifically address the aforementioned issues of HE. Besides, considering the subjective perception of the initial output of the model, two strategies are proposed to make the enhanced image more natural and comfortable. Finally, histogram equalization is applied to the modified histogram. Extensive experimental results demonstrate that the proposed scheme is reasonable and effective, and outperforms several state-of-the-art methods in terms of subjective and objective metrics.

Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time.

Deep-learning-based reconstruction (DLR) improves the quality of magnetic resonance (MR) images which allows faster acquisitions. The aim of this study was to compare the image quality of standard and accelerated T2 weighted turbo-spin-echo (TSE) images of the prostate reconstructed with and without DLR and to find associations between perceived image quality and calculated image characteristics. In a cohort of 47 prospectively enrolled consecutive patients referred for bi-parametric prostate magnetic resonance imaging (MRI), two T2-TSE acquisitions in the transverse plane were acquired on a 3T scanner-a standard T2-TSE sequence and a short sequence accelerated by a factor of two using compressed sensing (CS). The images were reconstructed with and without DLR in super-resolution mode. The image quality was rated in six domains. Signal-to-noise ratio (SNR), and image sharpness were measured. The mean acquisition time was 281±23 s for the standard and 140±12 s for the short acquisition (P<0.0001). DLR images had higher sharpness compared to non-DLR (P<0.001). Short and short-DLR had lower SNR than the standard and standard-DLR (P<0.001). The perceived image quality of short-DLR was rated better in all categories compared to the standard sequence (P<0.001 to P=0.004). All domains of subjective evaluation were correlated with measured image sharpness (P<0.001). T2-TSE acquisition of the prostate accelerated using CS combined with DLR reconstruction provides images with increased sharpness that have a superior quality as perceived by human readers compared to standard T2-TSE. The perceived image quality is correlated with measured image contrast.

Low-dose versus standard-dose normal temporal bone CT in children: a comparison study

ObjectiveTo compare the image quality of normal anatomical structures and radiation dose on low-dose (LDCT) and standard-dose (SDCT) temporal bone CT in children.MethodsThe study included 45 LDCT (80 kV and 130 mAs) and 45 SDCT (120 kV and 170 mAs) scans in children, 1–15 years of age. LDCT and SDCT scans were analyzed on H60s and H70h reconstruction kernels, respectively. Two readers assessed the image quality for 25 anatomical structures, using a 5-point scale. A score of 3 and above was considered “sufficient” and 2 and below was considered “insufficient” image quality. Image noise, contrast, age and size-specific effective doses were calculated.ResultsDespite an increase in image noise on LDCT, image quality remained sufficient for most structures owing to increased image contrast. The median effective dose on LDCT, calculated with age-specific conversion factor, decreased by 72.9% and that calculated with size-specific conversion factor decreased by 81.8% compared to the dose on SDCT.ConclusionLDCT provides comparable image quality for evaluation of temporal bone with significant reduction in radiation dose in children.

Research on Laser Dual-Mode Fusion Detection Method of Ship Wake Bubbles

Addressing the issues of weak echo signals and strong background interference in the laser detection of ships’ wakes, an analysis of the laser backscatter detection characteristics of ships’ wakes has been conducted. Based on the Monte Carlo method, a simulation model for the dual-mode fusion detection of ship wake bubbles using laser technology was constructed under different target characteristics. A dual-mode fusion detection system for ships’ wakes was designed, and an indoor experimental platform for the dual-mode fusion detection of ship wake bubbles using laser technology was established. To address problems such as a wide range of echo signal intensity changes, severe signal fluctuations, low resolution, poor image contrast, and blurred target edge information, an algorithm based on multi-timescale hierarchical fusion signal processing and temporal difference accumulation image processing was proposed. Verification experiments for ship wake detection were conducted, which revealed that the dual-mode fusion detection method for ship wake bubbles using laser technology can effectively enhance the detection signal-to-background ratio and counter the maneuvering evasion of underwater weapons by ships. It achieved high sensitivity, large dynamic range, high resolution, and a wide field of view detection and real-time signal processing of ship wake bubble targets of different magnitudes against a strong reverberation background. The effectiveness of the dual-mode fusion detection mode was validated, providing theoretical support for the overall system design and parameter settings.

Feature shared multi-decoder network using complementary learning for Photon counting CT ring artifact suppression.

Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT. To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images. Specifically, we employ a feature-sharing encoder to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details. We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods. In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts.