Image Details Research Articles

Biodegradable Citrate‐Based Polymers Enable 5D Monitoring of Implant Evolution

AbstractBiodegradable tissue engineering scaffolds have garnered increasing interest for their role in providing mechanical support, promoting tissue regeneration, and eliminating the need for removal. However, the in vivo degradation processes remain challenging to track. Here, a novel biodegradable polymer, N‐methyldiethanolamine (MDEA) and Gadolinium(III) diethylenetriamine pentaacetate (Gd‐DTPA) modified biodegradable photoluminescent polymers (BPLPMGd), which combines near‐infrared (NIR) fluorescence and magnetic resonance (MR) dual‐modality imaging are introduced to monitor scaffold degradation in vivo. The chemical structure of BPLPMGd is characterized and its dual‐imaging properties in vitro are evaluated. Subsequently, non‐invasive dual‐modality imaging to track the degradation of implanted BPLPMGd scaffolds is performed in a rat model, comparing these results with histological data. This approach reveals that BPLPMGd enables reliable non‐invasive tracking of the degradation, where NIR fluorescence imaging offers a qualitative and quantitative analysis of scaffold mass loss, total volume and solid content changes, while magnetic resonance imaging (MRI) details structural and morphological changes, allowing for 5D monitoring of implant degradation, including 3D structure, location, mass, volume, and geometry. The combination of these imaging modalities provides a comprehensive view of scaffold degradation, where the synergistic use of both yields results greater than either modality alone, offering unprecedented 5D information of implantable devices. This innovative approach has potential applications in regenerative engineering and beyond.

QualityNet: A multi-stream fusion framework with spatial and channel attention for blind image quality assessment

This study introduces a novel Blind Image Quality Assessment (BIQA) approach leveraging a multi-stream spatial and channel attention model. Our method addresses challenges posed by diverse image content and distortions by integrating feature maps from two distinct backbones. Through spatial and channel attention mechanisms, our algorithm prioritizes regions of interest, enhancing its ability to capture crucial image details. Extensive evaluations on four benchmark datasets demonstrate superior performance compared to existing methods, closely aligning with human perceptual assessment. Our approach exhibits exceptional generalization capabilities on both authentic and synthetic distortion databases. Moreover, it demonstrates a distinctive focus on perceptual foreground information, enhancing its practical applicability. Thorough quantitative analyses underscore the algorithm’s superior performance, establishing its dominance over existing methods.

WGAN-Based Realization Process of Gravel Soil for Hydraulic Property Simulation

Gravel soil faces significant engineering challenges such as leakage erosion and soil flow due to its complex composition and susceptibility to groundwater effects. This study integrates the entire machine learning process, including pre- and post-processing of images, WGAN implementation, and validation of hydraulic and morphological properties. Obtaining intact gravel soil samples is difficult and costly due to their erodible nature in the Li River, China. A μ-CT scanning series is employed to capture detailed images with three microstructural characteristics of gravel soil, forming the basis for training datasets using WGANs. This approach allows the generation of similar 3D realizations that replicate the microstructural characteristics and hydraulic behaviors of a prototype of gravel soils. Through computational fluid dynamics (CFD) simulations, the effectiveness of the realizations in hydraulic behavior within reconstructed porous structures is verified. This process indirectly validates the consistency between the realization′s microstructure and the prototype. This integrated methodology not only enhances understanding but also aids in the optimization of engineering designs and applications in geotechnical and materials science disciplines.

CT imaging analysis of the C7 pedicle and lateral mass in children aged 0–14 years

Background and objectiveThere is currently a lack of anatomical research on the C7 pedicle and lateral mass in children aged 0–14 years, and experience with pediatric lower cervical spine surgeries remains limited. This study aims to investigate the anatomical structure of the C7 pedicle and lateral mass in a large pediatric sample to understand their growth patterns, providing imaging and anatomical references for pediatric lower cervical spine surgeries and the design of pediatric C7 screws. MethodsWe measured 12 parameters, including the width, length, height, and angles of the C7 pedicle and lateral mass in children aged 0–14 years. The t-test was used to analyze bilateral and gender differences, and least squares regression was applied for curve fitting analysis. ResultsThe size parameters of the C7 pedicle and lateral mass generally increased with age, while angular parameters fluctuated within specific ranges. The average values for each parameter between ages 0–14 were as follows: D1: 2.78–5.28 mm, D2: 18.15–30.54 mm, D3: 2.44–6.01 mm, angle A: 38.05–44.48°; D4: 6.81–10.94 mm, D5: 5.37–11.23 mm, D6: 8.07–13.28 mm, D7: 1.94–4.88 mm, D8: 5.67–9.39 mm, angle C: 18.50–28.80°, angle D: 43.23–52.01°, angle E: 33.74–44.96°. Bilateral analysis showed no significant differences for most parameters across most age groups, with differences mainly observed in angle A, angle C, D6, and D8. Gender differences were primarily noted in length parameters across different age groups, especially in the 3–4, 6–7, 9–11, and 13–14 age groups. Regression analysis indicated that most parameters followed cubic function curves, while a few followed power or quadratic function curves. ConclusionThis study provides detailed CT imaging anatomy of the C7 pedicle and lateral mass in children aged 0–14 years. Preoperative thin-slice CT scans and careful measurements of key parameters are essential for pediatric lower cervical spine surgeries. The findings offer valuable imaging and anatomical references for pediatric posterior cervical fixation surgery and screw design.

Pictorial Review of Rare Pancreatic Tumors and Tumor-like Lesions: Radiologic–Pathologic Correlation

Rare pancreatic tumors and non-neoplastic tumor-like lesions present a diagnostic challenge due to their uncommon occurrence and overlapping imaging characteristics with more prevalent pancreatic neoplasms. Advances in imaging technologies and diagnostic criteria have contributed to increased detection of these rare entities in clinical practice. This pictorial review focuses on the radiologic–pathologic correlation of rare pancreatic tumors, including colloid carcinoma, acinar cell carcinoma, pancreatoblastoma, primary pancreatic lymphoma, and non-neoplastic tumor-like lesions such as hamartomas and inflammatory pseudotumors. Detailed imaging features, such as signal intensities on MRI and enhancement patterns on CT, are correlated with pathological findings to assist in the differential diagnosis. Familiarity with these characteristics is crucial for radiologists to ensure accurate diagnosis and guide appropriate treatment strategies, as management and prognosis significantly differ from common pancreatic neoplasms.

Epiretinal membrane: an overview and update.

Epiretinal membrane (ERM) is a frequently diagnosed macular disease associated with aging, characterized by a fibrous membrane forming on the internal limiting membrane (ILM) and leading to visual dysfunctions such as metamorphopsia. Various hypotheses regarding the pathology of metamorphopsia have been proposed; however, the complete pathophysiologic mechanism underlying ERM remains unclear. Optical coherence tomography (OCT) provides detailed images enabling precise diagnosis and characterization of ERM, with several recent studies using the latest OCT imaging techniques. Surgical removal of ERM is the only treatment option; however, criteria for surgical intervention are not established, complicating the decision-making processes. Furthermore, the debate on whether simultaneous peeling of the ILM during ERM surgery enhances outcomes or poses unnecessary risks is ongoing, with no definite conclusion having yet been reached. This review also focuses on epiretinal proliferation, which is different from ERM and is characteristic of lamellar macular hole (LMH). Recently, diagnostic criteria for LMH and related diseases were proposed. Reports on effective surgical procedures for LMH exist, although more research is needed to confirm the long-term outcomes. Thus, this review article aims to provide an overview and updated knowledge of ERM, LMH, and related diseases.

Integrated Radiology and Radiation Protection: The Physics of Optimizing Safety in the Era of 3D and 4D Imaging

In the era of ever-evolving medical imaging technology, integrated radiology plays a vital role in improving diagnosis and therapy. The application of 3D and 4D technology in radiology has provided significant advances in terms of diagnostic accuracy and therapy planning. With the ability to produce clearer and more detailed images, doctors can make better decisions in patient care. However, the increasing use of radiation in medical imaging also raises serious concerns about the safety of both patients and medical personnel. This study aims to explore the importance of physics in optimizing radiation protection, as well as the challenges faced in integrating advanced technologies. We use a Systematic Literature Review (SLR) approach to identify best practices and recommendations that can be implemented to improve safety in the use of radiation. Through an analysis of existing literature, this study aims to provide insights into effective strategies in protecting patients from radiation risks without compromising imaging quality. Thus, this study will not only contribute to academic knowledge but will also benefit future clinical practice, ensuring the safety of patients and medical personnel in the era of high technology.

A Practical Guide to Loco-Regional Nerve Blocks for Oromaxillofacial Surgery in Dogs and Cats.

This article provides an overview of the neuroanatomy of the head with a detailed explanation and visual images to enable accurate placement of loco-regional nerve blocks to achieve pre-emptive blockade of the nociceptive input which occurs when performing oral surgery. Variations in anatomy and between species are addressed to assist in accurate placement.

WiTUnet: A U-shaped architecture integrating CNN and Transformer for improved feature alignment and local information fusion

Low-dose computed tomography (LDCT) has emerged as the preferred technology for diagnostic medical imaging due to the potential health risks associated with X-ray radiation and conventional computed tomography (CT) techniques. While LDCT utilizes a lower radiation dose compared to standard CT, it results in increased image noise, which can impair the accuracy of diagnoses. To mitigate this issue, advanced deep learning-based LDCT denoising algorithms have been developed. These primarily utilize Convolutional Neural Networks (CNNs) or Transformer Networks and often employ the Unet architecture, which enhances image detail by integrating feature maps from the encoder and decoder via skip connections. However, existing methods focus excessively on the optimization of the encoder and decoder structures while overlooking potential enhancements to the Unet architecture itself. This oversight can be problematic due to significant differences in feature map characteristics between the encoder and decoder, where simple fusion strategies may hinder effective image reconstruction. In this paper, we introduce WiTUnet, a novel LDCT image denoising method that utilizes nested, dense skip pathway in place of traditional skip connections to improve feature integration. Additionally, to address the high computational demands of conventional Transformers on large images, WiTUnet incorporates a windowed Transformer structure that processes images in smaller, non-overlapping segments, significantly reducing computational load. Moreover, our approach includes a Local Image Perception Enhancement (LiPe) module within both the encoder and decoder to replace the standard multi-layer perceptron (MLP) in Transformers, thereby improving the capture and representation of local image features. Through extensive experimental comparisons, WiTUnet has demonstrated superior performance over existing methods in critical metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Root Mean Square Error (RMSE), significantly enhancing noise removal and image quality. The code is available on github https://github.com/woldier/WiTUNet.

PAFPT: Progressive aggregator with feature prompted transformer for underwater image enhancement

The optical characteristics of underwater environments often result in distorted underwater images, which makes it difficult to meet the needs of complex underwater environment perception. The demand for obtaining high-quality underwater images presents challenges for existing underwater image enhancement methods. In this paper, a progressive aggregator with feature prompted transformer for underwater image enhancement is presented. First, a feature prompted transformer module consisting of a feature convolutional transposed attention and a prompted feedforward neural network is proposed. It integrates feature attention mechanisms and adjusts input features through prompt weights to better focus on global information while enhancing local details, enriching texture, removing blurring, and correcting color deviation. Concurrently, a progressive aggregator consisting of four stages of up-sampling aggregation blocks is constructed to transfer features effectively and facilitate the aggregation of features of different scales at different stages to realize comprehensive attention to image details and semantic information. Besides, a region reconstruction unit is designed, and a reconstruction attention mechanism is introduced to pay attention to the bottleneck localized region to suppress noise. The performance of our method is thoroughly evaluated on public underwater image datasets: EUVP, UIEB, RUIE and SQUID. The quantitative and qualitative results indicate that our method outperforms nine state-of-the-art methods in both subjective perception and evaluation metrics, demonstrating excellent learning and generalization abilities. Additionally, the excellent results reflect the significant performance gains it brings to downstream visual applications.

Colon polyp detection based on multi-scale and multi-level feature fusion and lightweight convolutional neural network

Early diagnosis and treatment of colorectal polyps are crucial for preventing colorectal cancer. This paper proposes a lightweight convolutional neural network for the automatic detection and auxiliary diagnosis of colorectal polyps. Initially, a 53-layer convolutional backbone network is used, incorporating a spatial pyramid pooling module to achieve feature extraction with different receptive field sizes. Subsequently, a feature pyramid network is employed to perform cross-scale fusion of feature maps from the backbone network. A spatial attention module is utilized to enhance the perception of polyp image boundaries and details. Further, a positional pattern attention module is used to automatically mine and integrate key features across different levels of feature maps, achieving rapid, efficient, and accurate automatic detection of colorectal polyps. The proposed model is evaluated on a clinical dataset, achieving an accuracy of 0.9982, recall of 0.9988, F1 score of 0.9984, and mean average precision (mAP) of 0.9953 at an intersection over union (IOU) threshold of 0.5, with a frame rate of 74 frames per second and a parameter count of 9.08 M. Compared to existing mainstream methods, the proposed method is lightweight, has low operating configuration requirements, high detection speed, and high accuracy, making it a feasible technical method and important tool for the early detection and diagnosis of colorectal cancer.

Improved Real-Time Detection Transformer-Based Rail Fastener Defect Detection Algorithm

To address the issues of the Real-Time DEtection TRansformer (RT-DETR) object detection model, including poor defect feature extraction in the task of rail fastener defect detection, inefficient use of computational resources, and suboptimal channel attention in the self-attention mechanism, the following improvements were made. Firstly, a Super-Resolution Convolutional Module (SRConv) was designed as a separate component and integrated into the Backbone network, which enhances the image details and clarity while preserving the original image structure and semantic content. This integration improves the model’s ability to extract defect features. Secondly, a channel attention mechanism was integrated into the self-attention module of RT-DETR to enhance the focus on feature map channels, addressing the problem of sparse attention maps caused by the lack of channel attention while saving computational resources. Finally, the experimental results show that compared to the original model, the improved RT-DETR-based rail fastener defect detection algorithm, with an additional 0.4 MB of parameters, achieved a higher accuracy, with a 2.8 percentage point increase in the Mean Average Precision (mAP) across IoU thresholds from 0.5 to 0.9 and a 1.7 percentage point increase in the Average Recall (AR) across the same thresholds.

Enhanced Wavelet-Based Medical Image Denoising with Bayesian-Optimized Bilateral Filtering.

Medical image denoising is essential for improving the clarity and accuracy of diagnostic images. In this paper, we present an enhanced wavelet-based method for medical image denoising, aiming to effectively remove noise while preserving critical image details. After applying wavelet denoising, a bilateral filter is utilized as a post-processing step to further enhance image quality by reducing noise while maintaining edge sharpness. The bilateral filter's effectiveness heavily depends on its parameters, which must be carefully optimized. To achieve this, we employ Bayesian optimization, a powerful technique that efficiently identifies the optimal filter parameters, ensuring the best balance between noise reduction and detail preservation. The experimental results demonstrate a significant improvement in image denoising performance, validating the effectiveness of our approach.

Super Resolved Single-Cell Spatial Metabolomics from Multimodal Mass Spectrometry Imaging guided by Imaging Mass Cytometry.

Mass spectrometry imaging (MSI) is a powerful technique for spatially resolved analysis of metabolites and other biomolecules within biological tissues. However, the inherent low spatial resolution of MSI often limits its ability to provide detailed cellular-level information. To address this limitation, we propose a guided super-resolution (GSR) approach that leverages high-resolution Imaging Mass Cytometry (IMC) images to enhance the spatial resolution of low-resolution MSI data. By using these detailed IMC images as guides, we improve the resolution of MSI images, creating high-resolution metabolite maps. This enhancement facilitates more precise analysis of cellular structures and tissue architectures, providing deeper insights into super-resolved spatial metabolomics at the single-cell level.

Combined evaluation of visual field and optical coherence tomography angiography data in multiple sclerosis patients

Aims: To evaluate Humphrey visual field analyzer (HVFA) data and optical coherence tomography angiography (OCT-A) parameters in multiple sclerosis (MS) patients with optic neuritis (ON) attacks. Methods: 34 eyes of 34 patients with MS diagnosis who had ON attacks were included in the study. After detailed ophthalmoscopic examination, OCT-A and HVFA images were taken. Spearman correlation analysis was used in statistical analysis. Results: The mean age of the participants was 31.52±10.09 years. In the correlation analysis, a moderate correlation was found between mean deviation (MD) and foveal deep blood flow (DF) (rho=0.52), and a weak correlation was found between MD and foveal superficial blood flow (SF) (rho=0.40) and optic disc head blood flow (ODF) (rho=0.38). A weak negative correlation was found between pattern standard deviation (PSD) and DF (rho=-0.37) and ODF (rho=-0.40). Conclusion: This study showed that OCTA data were affected in a manner consistent with HVFA data. More practical and quickly applicable OCTA scans may facilitate the estimation of visual field findings in MS patients.

PET image reconstruction using weighted nuclear norm maximization and deep learning prior

The ill-posed Positron emission tomography (PET) reconstruction problem usually results in limited resolution and significant noise. Recently, deep neural networks have been incorporated into PET iterative reconstruction framework to improve the image quality. In this paper, we propose a new neural network-based iterative reconstruction method by using weighted nuclear norm (WNN) maximization, which aims to recover the image details in the reconstruction process. The novelty of our method is the application of WNN maximization rather than WNN minimization in PET image reconstruction. Meanwhile, a neural network is used to control the noise originated from WNN maximization. Our method is evaluated on simulated and clinical datasets. The simulation results show that the proposed approach outperforms state-of-the-art neural network-based iterative methods by achieving the best contrast/noise tradeoff with a remarkable contrast improvement on the lesion contrast recovery. The study on clinical datasets also demonstrates that our method can recover lesions of different sizes while suppressing noise in various low-dose PET image reconstruction tasks. Our code is available athttps://github.com/Kuangxd/PETReconstruction.

Sensing and Imaging Agents for Cyclooxygenase Enzyme.

In this concept, we present a comprehensive study on the development and application of COX-2-specific fluorescent probes for cancer imaging and diagnosis. Targetting cancer cells and measuring cancer-related activities in specific organelles quickly and accurately are crucial factors for early diagnosis and research on cancer pathology and treatment. This concept explores a variety of probes which include BDIMC1-2, TPI-IMC, YZP1, IQ-1, ANQ-IMC-6, Niblue-C6-IMC, AZB-IMC2, CMP, CF3-Fluorocoxib, IBPC1, CoxFluor, etc. and each one demonstrates unique mechanisms and high selectivity towards COX-2 enzymes. These probes were designed to enhance fluorescence upon binding to COX-2 which enable precise visualization of tumor and inflamed tissues. The research emphasizes the importance of COX-2 as a biomarker in cancer diagnostics, particularly in identifying cancer stem cells and inflamed tissues. This concept highlights the potentiality of these probes in non-invasive imaging techniques which offering significant advancements in cancer diagnosis and monitoring. The in vivo and in vitro experiments, including applications in mouse models and human tissue samples, confirm the efficacy of these probes in providing detailed imaging for clinical and research applications.

Neural Radiance Field Image Refinement through End‐to‐End Sampling Point Optimization

Neural radiance field (NeRF), capable of synthesizing high‐quality novel viewpoint images, suffers from issues like artifact occurrence due to its fixed sampling points during rendering. This study proposes a method that optimizes sampling points to reduce artifacts and produce more detailed images. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Novel Bioreactor Design for Non-invasive Longitudinal Monitoring of Tissue-Engineered Heart Valves in 7T MRI and Ultrasound.

The development of cardiovascular implants is abundant, yet their clinical adoption remains a significant challenge in the treatment of valvular diseases. Tissue-engineered heart valves (TEHV) have emerged as a promising solution due to their remodeling capabilities, which have been extensively studied in recent years. However, ensuring reproducible production and clinical translation of TEHV requires robust longitudinal monitoring methods.Cardiovascular magnetic resonance imaging (MRI) is a non-invasive, radiation-free technique providing detailed valvular imaging and functional assessment. To facilitate this, we designed a state-of-the-art metal-free bioreactor enabling dynamic MRI and ultrasound imaging. Our compact bioreactor, tailored to fit a 72mm bore 7T MRI coil, features an integrated backflow design ensuring MRI compatibility. A pneumatic drive system operates the bioreactor, minimizing potential MRI interference. The bioreactor was digitally designed and constructed using polymethyl methacrylate, utilizing only polyether ether ketone screws for secure fastening. Our biohybrid TEHV incorporates a non-degradable polyethylene terephthalate textile scaffold with fibrin matrix hydrogel and human arterial smooth muscle cells.As a result, the bioreactor was successfully proven to be MRI compatible, with no blooming artifacts detected. The dynamic movement of the TEHVs was observed using gated MRI motion artifact compensation and ultrasound imaging techniques. In addition, the conditioning of TEHVs in the bioreactor enhanced ECM production. Immunohistology demonstrated abundant collagen, α-smooth muscle actin, and a monolayer of endothelial cells throughout the valve cusp. Our innovative methodology provides a physiologically relevant environment for TEHV conditioning and development, enabling accurate monitoring and assessment of functionality, thus accelerating clinical acceptance.

Spatial Resolution Enhancement Framework Using Convolutional Attention-Based Token Mixer

Spatial resolution enhancement in remote sensing data aims to augment the level of detail and accuracy in images captured by satellite sensors. We proposed a novel spatial resolution enhancement framework using the convolutional attention-based token mixer method. This approach leveraged spatial context and semantic information to improve the spatial resolution of images. This method used the multi-head convolutional attention block and sub-pixel convolution to extract spatial and spectral information and fused them using the same technique. The multi-head convolutional attention block can effectively utilize the local information of spatial and spectral dimensions. The method was tested on two kinds of data types, which were the visual-thermal dataset and the visual-hyperspectral dataset. Our method was also compared with the state-of-the-art methods, including traditional methods and deep learning methods. The experiment results showed that the method was effective and outperformed state-of-the-art methods in overall, spatial, and spectral accuracies.