BACKGROUND: The quality of low-light endoscopic images involves applications in medical disciplines such as physiology and anatomy for the identification and judgement of tissue structures. Due to the use of point light sources and the constraints of narrow physiological structures, medical endoscopic images display uneven brightness, low contrast, and a lack of texture information, presenting diagnostic challenges for physicians.
METHODS: In this paper, a nonlinear brightness enhancement and denoising network based on Retinex theory is designed to improve the brightness and details of low-light endoscopic images. The nonlinear luminance enhancement module uses higher-order curvilinear functions to improve overall brightness; the dual-attention denoising module captures detailed features of anatomical structures; and the color loss function mitigates color distortion.
RESULTS: Experimental results on the Endo4IE dataset demonstrate that the proposed method outperforms existing state-of-the-art methods in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The PSNR is 27.2202, SSIM is 0.8342, and the LPIPS is 0.1492. It provides a method to enhance image quality in clinical diagnosis and treatment.
CONCLUSIONS: It offers an efficient method to enhance images captured by endoscopes and offers valuable insights into intricate human physiological structures, which can effectively assist clinical diagnosis and treatment.

From its inception as a two-dimensional snapshot of the beating heart, echocardiography has become an indelible part of cardiovascular diagnostics. The integration of ultrasound enhancing agents (UEAs) marks a pivotal transition, enhancing its diagnostic acumen beyond myocardial perfusion. These agents have refined echocardiography\'s capacity to visualize complex cardiac anatomy and pathology with unprecedented clarity, especially in non-coronary artery disease contexts. UEAs aid in detailed assessments of myocardial viability, endocardial border delineation in left ventricular opacification, and identification of intracardiac masses. Recent innovations in UEAs, accompanied by advancements in echocardiographic technology, offer clinicians a more nuanced view of cardiac function and blood flow dynamics. This review explores recent developments in these applications and future contemplated studies.

When applying deep learning and image processing techniques for bridge crack detection, the obtained images in real-world scenarios have severe image degradation problem. This study focuses on restoring low-illumination bridge crack images corrupted by noise to improve the accuracy of subsequent crack detection and semantic segmentation. The proposed algorithm consists of a deep CNN denoiser and a normalized flow-based brightness enhancement module. By taking the noise spectrum as an input, the deep CNN denoiser restores image at a broad range of noise levels. The normalized flow module, employs a conditional encoder and a reversible network to map the distribution of normally exposed images to a Gaussian distribution, effectively improving the image brightness. Extensive experiments have demonstrated the approach can usefully recover low-illumination images corrupted by noise compared to the state-of-the-art methods. Furthermore, the algorithm presented in this study can also be applied to other image quality restoration with high generalization and robust abilities. And the semantic segmentation accuracy of the restored image is significantly improved.

UNASSIGNED: This study aims to explore the value of real-time strain elastography (RTE) and contrast-enhanced ultrasonography (CEUS) in the diagnosis of breast BI-RADS 4 lesions. It collected 85 cases (totaling 85 lesions) diagnosed with breast BI-RADS 4 through routine ultrasound from October 2020 to December 2022 in Huangshan City People\'s Hospital. All lesions underwent RTE and CEUS examination before surgery, and the ImageJ software was used to measure the periphery of lesion images in the enhancement peak mode and grayscale mode to calculate the contrast-enhanced ultrasound area ratio. The diagnostic capabilities of single-modal and multimodal ultrasound examination for the malignancy of breast BI-RADS 4 lesions were compared using the receiver operating characteristic curve; the Spearman correlation analysis was adopted to evaluate the correlation between multimodal ultrasound and CEUS area ratio. As a result, among the 85 lesions, 51 were benign, and 34 were malignant. The areas under the curve (AUCs) of routine ultrasound (US), US + RTE, US + CEUS, and US + RTE + CEUS were 0.816, 0.928, 0.953, and 0.967, respectively, with the combined method showing a higher AUC than the single application. The AUC of the CEUS area ratio diagnosing breast lesions was 0.888. There was a strong positive correlation (r = 0.819, P < 0.001) between the diagnostic performance of US + RTE + CEUS and the CEUS area ratio. In conclusion, based on routine ultrasound, the combination of RTE and CEUS can further improve the differential diagnosis of benign and malignant lesions in breast BI-RADS 4.

Restoration of digital visual media acquired from repositories of historical photographic and cinematographic material is of key importance for the preservation, study and transmission of the legacy of past cultures to the coming generations. In this paper, a fully automatic approach to the digital restoration of historical stereo photographs is proposed, referred to as Stacked Median Restoration plus (SMR+). The approach exploits the content redundancy in stereo pairs for detecting and fixing scratches, dust, dirt spots and many other defects in the original images, as well as improving contrast and illumination. This is done by estimating the optical flow between the images, and using it to register one view onto the other both geometrically and photometrically. Restoration is then accomplished in three steps: (1) image fusion according to the stacked median operator, (2) low-resolution detail enhancement by guided supersampling, and (3) iterative visual consistency checking and refinement. Each step implements an original algorithm specifically designed for this work. The restored image is fully consistent with the original content, thus improving over the methods based on image hallucination. Comparative results on three different datasets of historical stereograms show the effectiveness of the proposed approach, and its superiority over single-image denoising and super-resolution methods. Results also show that the performance of the state-of-the-art single-image deep restoration network Bringing Old Photo Back to Life (BOPBtL) can be strongly improved when the input image is pre-processed by SMR+.

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UNASSIGNED: Traditional diffuse optical tomography (DOT) reconstructions are hampered by image artifacts arising from factors such as DOT sources being closer to shallow lesions, poor optode-tissue coupling, tissue heterogeneity, and large high-contrast lesions lacking information in deeper regions (known as shadowing effect). Addressing these challenges is crucial for improving the quality of DOT images and obtaining robust lesion diagnosis.
UNASSIGNED: We address the limitations of current DOT imaging reconstruction by introducing an attention-based U-Net (APU-Net) model to enhance the image quality of DOT reconstruction, ultimately improving lesion diagnostic accuracy.
UNASSIGNED: We designed an APU-Net model incorporating a contextual transformer attention module to enhance DOT reconstruction. The model was trained on simulation and phantom data, focusing on challenges such as artifact-induced distortions and lesion-shadowing effects. The model was then evaluated by the clinical data.
UNASSIGNED: Transitioning from simulation and phantom data to clinical patients\' data, our APU-Net model effectively reduced artifacts with an average artifact contrast decrease of 26.83% and improved image quality. In addition, statistical analyses revealed significant contrast improvements in depth profile with an average contrast increase of 20.28% and 45.31% for the second and third target layers, respectively. These results highlighted the efficacy of our approach in breast cancer diagnosis.
UNASSIGNED: The APU-Net model improves the image quality of DOT reconstruction by reducing DOT image artifacts and improving the target depth profile.

In the field of dentistry, the presence of dental calculus is a commonly encountered issue. If not addressed promptly, it has the potential to lead to gum inflammation and eventual tooth loss. Bitewing (BW) images play a crucial role by providing a comprehensive visual representation of the tooth structure, allowing dentists to examine hard-to-reach areas with precision during clinical assessments. This visual aid significantly aids in the early detection of calculus, facilitating timely interventions and improving overall outcomes for patients. This study introduces a system designed for the detection of dental calculus in BW images, leveraging the power of YOLOv8 to identify individual teeth accurately. This system boasts an impressive precision rate of 97.48%, a recall (sensitivity) of 96.81%, and a specificity rate of 98.25%. Furthermore, this study introduces a novel approach to enhancing interdental edges through an advanced image-enhancement algorithm. This algorithm combines the use of a median filter and bilateral filter to refine the accuracy of convolutional neural networks in classifying dental calculus. Before image enhancement, the accuracy achieved using GoogLeNet stands at 75.00%, which significantly improves to 96.11% post-enhancement. These results hold the potential for streamlining dental consultations, enhancing the overall efficiency of dental services.

In this paper, we present a cascaded deep convolution neural network (CNN) for assessing enlarged perivascular space (ePVS) within the basal ganglia region using T2-weighted MRI. Enlarged perivascular spaces (ePVSs) are potential biomarkers for various neurodegenerative disorders, including dementia and Parkinson\'s disease. Accurate assessment of ePVS is crucial for early diagnosis and monitoring disease progression. Our approach first utilizes an ePVS enhancement CNN to improve ePVS visibility and then employs a quantification CNN to predict the number of ePVSs. The ePVS enhancement CNN selectively enhances the ePVS areas without the need for additional heuristic parameters, achieving a higher contrast-to-noise ratio (CNR) of 113.77 compared to Tophat, Clahe, and Laplacian-based enhancement algorithms. The subsequent ePVS quantification CNN was trained and validated using fourfold cross-validation on a dataset of 76 participants. The quantification CNN attained 88% accuracy at the image level and 94% accuracy at the subject level. These results demonstrate significant improvements over traditional algorithm-based methods, highlighting the robustness and reliability of our deep learning approach. The proposed cascaded deep CNN model not only enhances the visibility of ePVS but also provides accurate quantification, making it a promising tool for evaluating neurodegenerative disorders. This method offers a novel and significant advancement in the non-invasive assessment of ePVS, potentially aiding in early diagnosis and targeted treatment strategies.

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