Imaging Technology Research Articles

Breast cancer lesion segmentation based on co-learning feature fusion and Transformer

The PET/CT imaging technology combining positron emission tomography (PET) and computed tomography (CT) is the most advanced imaging examination method currently, and is mainly used for tumor screening, differential diagnosis of benign and malignant tumors, staging and grading. This paper proposes a method for breast cancer lesion segmentation based on PET/CT bimodal images, and designs a dual-path U-Net framework, which mainly includes three modules: encoder module, feature fusion module and decoder module. Among them, the encoder module uses traditional convolution for feature extraction of single mode image; The feature fusion module adopts collaborative learning feature fusion technology and uses Transformer to extract the global features of the fusion image; The decoder module mainly uses multi-layer perceptron to achieve lesion segmentation. This experiment uses actual clinical PET/CT data to evaluate the effectiveness of the algorithm. The experimental results show that the accuracy, recall and accuracy of breast cancer lesion segmentation are 95.67%, 97.58% and 96.16%, respectively, which are better than the baseline algorithm. Therefore, it proves the rationality of the single and bimodal feature extraction method combining convolution and Transformer in the experimental design of this article, and provides reference for feature extraction methods for tasks such as multimodal medical image segmentation or classification.

Revolutionizing the Pancreatic Tumor Diagnosis: Emerging Trends in Imaging Technologies: A Systematic Review

Background and Objectives: The pancreas, ensconced within the abdominal cavity, requires a plethora of sophisticated imaging modalities for its comprehensive evaluation, with ultrasonography serving as a primary investigative technique. A myriad of pancreatic pathologies, encompassing pancreatic neoplasia and a spectrum of inflammatory diseases, are detectable through these imaging strategies. Nevertheless, the intricate anatomical confluence and the pancreas’s deep-seated topography render the visualization and accurate diagnosis of its pathologies a formidable endeavor. The objective of our paper is to review the best diagnostic imagistic tools for the pancreas. Materials and Methods: we have gathered several articles using Prisma guidelines to determine the best imagistic methods. The imperative of pancreatic scanning transcends its diagnostic utility, proving to be a pivotal element in a multitude of clinical specialties, notably surgical oncology. Within this domain, multidetector computed tomography (MDCT) of the pancreas holds the distinction of being the paramount imaging modality, endorsed for its unrivaled capacity to delineate the staging and progression of pancreatic carcinoma. In synergy with MDCT, there has been a notable advent of avant-garde imaging techniques in recent years. These advanced methodologies, including ultrasonography, endoscopic ultrasonography, contrast-enhanced ultrasonography, and magnetic resonance imaging (MRI) conjoined with magnetic resonance cholangiopancreatography (MRCP), have broadened the horizon of tumor characterization, offering unparalleled depth and precision in oncological assessment. Other emerging diagnostic techniques, such as elastography, also hold a lot of potential and promise for the future of pancreatic imaging. Fine needle aspiration (FNA) is a quick, minimally invasive procedure to evaluate lumps using a thin needle to extract tissue for analysis. It is less invasive than surgical biopsies and usually performed as an outpatient with quick recovery. Its accuracy depends on sample quality, and the risks include minimal bleeding or discomfort. Results, guiding further treatment, are typically available within a week. Elastography is a non-invasive medical imaging technique that maps the elastic properties and stiffness of soft tissue. This method, often used in conjunction with ultrasound or MRI, helps differentiate between hard and soft areas in tissue, providing valuable diagnostic information. It is particularly useful for assessing liver fibrosis, thyroid nodules, breast lumps, and musculoskeletal conditions. The technique is painless and involves applying gentle pressure to the area being examined. The resulting images show tissue stiffness, indicating potential abnormalities. Elastography is advantageous for its ability to detect diseases in early stages and monitor treatment effectiveness. The procedure is quick, safe, and requires no special preparation, with results typically available immediately. Results: The assembled and gathered data shows the efficacy of various techniques in discerning the nature and extent of neoplastic lesions within the pancreas. Conclusions: The most common imaging modalities currently used in diagnosing pancreatic neoplasms are multidetector computed tomography (MDCT), endoscopic ultrasound (EUS), and magnetic resonance imaging (MRI), alongside new technologies, such as elastography.

Effects of mini-basketball training program on social communication impairments and regional homogeneity of brain functions in preschool children with autism spectrum disorder

BackgroundSocial communication impairments (SCI) is a core symptom of autism spectrum disorder (ASD) and is marked by challenges in social interaction. Although physical exercise has been shown to improve SCI, this finding has not been supported by comprehensive scientific evidence. Existing research has established a strong link between the SCI in children with ASD and abnormalities in regional homogeneity (ReHo). Therefore, investigating the effects of physical exercise on SCI and Reho in patients with ASD may help to elucidate the neurological mechanisms involved.MethodsThe present study included 30 preschool children diagnosed with ASD, with 15 participants in each group (experimental and control). The experimental group underwent a 12-week mini-basketball training program (MBTP) based on routine behavioral rehabilitation, while the control group only received routine behavioral rehabilitation. The Social Responsiveness Scale-Second Edition (SRS-2) was employed to assess SCI in both groups. Resting-state functional magnetic resonance imaging technology was used to evaluate ReHo in both groups.ResultsAfter 12-week of MBTP, significant group × time interactions were observed between the experimental and control groups in total SRS-2 scores (F = 14.514, p < 0.001, ηp2 = 0.341), as well as in the domains of social cognition (F = 15.620, p < 0.001, ηp2 = 0.358), social communication (F = 12.460, p < 0.01, ηp2 = 0.308), and autistic mannerisms (F = 9.970, p < 0.01, ηp2 = 0.263). No statistical difference was found in the scores for the social awareness subscale and social motivation subscale in the group × time interaction (all p > 0.05). The experimental group exhibited increased ReHo in the right Cerebellum_Crus1 and right parahippocampal gyrus, coupled with decreased ReHo in the left middle frontal gyrus (orbital part), left superior frontal gyrus (dorsolateral), left postcentral gyrus, and right superior parietal gyrus. Furthermore, a decrease in ReHo in the left postcentral gyrus positively correlated with changes in social communication scores in SCI behaviors (p < 0.05).ConclusionsOur study underscores the effectiveness of a 12-week MBTP in ameliorating SCI and abnormalities in ReHo among preschool children with ASD.Trial registrationThe trial is retrospectively registered on the Chinese Clinical Trial Registry (ChiCTR1900024973; August 5, 2019).

Adaptive Resource Scheduling Algorithm for Multi-Target ISAR Imaging in Radar Systems

Inverse synthetic-aperture radar (ISAR) can achieve precise imaging of targets, which enables precise perception of battlefield information, and it has become one of the most important tasks for radar systems. In multi-target scenarios, a resource scheduling method is required to improve the sensing ability and the overall efficiency of a radar system due to the limited resources. Considering the motion state of the target will change as the observation distance increases and image defocusing can occur due to the prolonged coherence accumulation time and significant changes in the target’s motion state, the optimal observation period should be an important consideration factor in the resource scheduling method to further improve the imaging efficiency of radar system, which has not yet been involved in existing research. In this paper, we first derive the expressions of the target’s effective rotation angle and the equivalent rotation angular velocity and then define the target’s optimal observation period. Then, for multi-target imaging scenarios, we allocate pulse resources within a given time period based on sparse-aperture ISAR imaging technology. An adaptive radar resource scheduling algorithm for multi-target ISAR imaging is proposed, which prioritizes allocating resources based on the optimal observation periods for the targets. In the algorithm, a radar resource scheduling model for multi-target ISAR imaging is established, and a feedback-based closed-loop search optimization method is proposed to solve the model. Finally, the best scheduling strategy can be obtained, which includes imaging task duration and the pulse allocation sequence for each target. Simulation results validate the effectiveness of the algorithm.

Spectral imaging based on custom-made multi-strip filter arrays

Spectral imaging technology based on on-chip spectroscopy can find applications in areas including aerospace, industrial and consumer electronics, and so on. Since each application normally requires a different set and number of spectral bands, the development of customized spectroscopy solutions with more compact size and lower cost becomes quite important. In this paper, we demonstrate a compact, highly customizable imaging spectrometer scheme based on custom-made multi-strip filter arrays, which maintains an average high transmission of ∼85%, narrow bandwidth of ∼30nm, and high optical density of ∼OD2 in the blocking regions across the visible to near-infrared waveband. Spectral imaging experiments are conducted, and the accurate reconstruction of sparse spectral image data is demonstrated as well to prove the validity of the proposed scheme. As a result, the work reported in this paper allows researchers to develop customized spectral imaging equipment in a relatively easy way and also has a great potential to be engineered further for scalable production with a quite low cost.

Reflective optical imaging for scattering medium using chaotic laser.

Optical imaging is a non-invasive imaging technology that utilizes near-infrared light, allows for the image reconstruction of optical properties like diffuse and absorption coefficients within the tissue. A recent trend is to use signal processing techniques or new light sources and expanding its application. We aim to develop the reflective optical imaging using the chaotic correlation technology with chaotic laser and optimize the quality and spatial resolution of reflective optical imaging. Scattering medium was measured using reflective configuration in different inhomogeneous regions to evaluate the performance of the imaging system. The accuracy of the recovered optical properties was investigated. The reconstruction errors of absorption coefficients and geometric centers were analyzed, and the feature metrics of the reconstructed images were evaluated. We showed how chaotic correlation technology can be utilized for information extraction and image reconstruction. This means that a higher signal-to-noise ratio and image reconstruction of inhomogeneous phantoms under different scenarios successfully were achieved. This work highlights that the peak values of correlation of chaotic exhibit smaller reconstruction error and better reconstruction performance in optical imaging compared with reflective optical imaging with the continuous wave laser.

Dispersion-engineered metasurfaces for high-sensitivity color image sensors

AbstractIncreasing the sensitivity of image sensors is a major challenge for current imaging technology. Researchers are tackling it because highly sensitive sensors enable objects to be recognized even in dark environments, which is critical for today’s smartphones, wearable devices, and automobiles. Unfortunately, conventional image-sensor architectures use light-absorptive color filters on every pixel, which fundamentally limits the detected light power per pixel. Recent advances in optical metasurfaces have led to the creation of pixelated light-transmissive color splitters with the potential to enhance sensor sensitivity. These metasurfaces can be used instead of color filters to distinguish primary colors, and unlike color filters, they can direct almost all of the incident light to the photodetectors, thereby maximizing the detectable light power. This review focuses on such metasurface-based color splitters enabling high-sensitivity color-image sensors. Their underlying principles are introduced with a focus on dispersion engineering. Then, their capabilities as optical elements are assessed on the basis of our recent findings. Finally, it is discussed how they can be used to create high-sensitivity color-image sensors.

Characterization of rutting damage based on two-dimensional image analysis of changes in mesoscopic aggregate properties of asphalt mixtures

Accurately predicting the rutting development law of asphalt pavement is of great significance for preventing and controlling pavement diseases. The aggregate skeleton of the asphalt mixture is the main load-bearing part and plays an important role in its anti-rutting performance. However, quantitative and mesoscopic evaluation indicators and standards for aggregate skeletons have not been established. This study aims to reveal the relationship between the mesostructured characteristics of asphalt mixtures and rutting damage. This paper uses two-dimensional image technology to analyze the dynamic change process of internal skeleton information of two graded asphalt mixtures under cyclic wheel load. According to the fractal theory, the fractal dimension and multifractal spectrum of the full-depth aggregate were calculated. The results indicate that the linear regression model between the mesoscopic skeleton information of asphalt mixture and the average rutting depth during the rutting test can be used to quantify the rutting performance. There is an obvious correlation between the fractal dimension of the section aggregate and the loading times, and there is a certain linear relationship between the multifractal spectrum indexes and the loading times. It indicates that the fractal theory index of aggregate can reflect the change of the macro performance of loading times.

Potential Benefits of Photon-Counting CT in Dental Imaging: A Narrative Review.

Background/Objectives: Advancements in oral imaging technology are continually shaping the landscape of dental diagnosis and treatment planning. Among these, photon-counting computed tomography (PCCT), introduced in 2021, has emerged as a promising, high-quality oral technology. Dental imaging typically requires a resolution beyond the standard CT systems achievable with the specialized cone-beam CT. PCCT can offer up to 100 µm resolution, improve soft-tissue contrast, and provide faster scanning times, which are crucial for detailed dental diagnosis and treatment planning. Using semiconductor detectors, PCCT produces sharper images and can potentially reduce the number of scans required, thereby decreasing patient radiation exposure. This review aimed to explore the potential benefits of PCCT in dental imaging. Methods: This review analyzed the literature on PCCT in dental imaging from January 2010 to February 2024, sourced from PubMed, Scopus, and Web of Science databases, focusing on high-resolution, patient safety, and diagnostic efficiency in dental structure assessment. We included English-language articles, case studies, letters, observational studies, and randomized controlled trials while excluding duplicates and studies unrelated to PCCT's application in dental imaging. Results: Studies have highlighted the superiority of PCCT in reducing artifacts, which are often problematic, compared to conventional CBCT and traditional CT scans, due to metallic dental implants, particularly when used with virtual monoenergetic imaging and iterative metal artifact reduction, thereby improving implant imaging. This review acknowledges limitations, such as the potential for overlooking other advanced imaging technologies, a narrow study timeframe, the lack of real-world clinical application data in this field, and costs. Conclusions: PCCT represents a promising advancement in dental imaging, offering high-resolution visuals, enhanced contrast, and rapid scanning with reduced radiation exposure.

Enhancing surgical outcomes: accurate identification and removal of prostate cancer with B7-H3-targeted NIR-II molecular imaging.

One of the main reasons for prostate cancer (PCa) recurrence is the difficulty in identifying and removing cancerous lesions during surgery. Accurately localizing and excising cancerous tissue remains a significant challenge. The second near-infrared window (NIR-II, 1000-1700nm) fluorescence offers enhanced resolution, a high signal-to-noise ratio, and the potential for deeper tissue penetration. However, this technology is not currently employed for intraoperative imaging of PCa. This study aims to construct a new NIR-II probe targeting B7-H3 (AbB7-H3-800CW) for accurate intraoperative identification and resection of PCa. Based on the differential expression of B7-H3 in PCa, we designed a novel imaging probe to accurately identify and guide the resection of preclinical PCa models and ex vivo human PCa tissues using NIR-II fluorescence imaging technology. Analyzing tissue samples from 60 clinical cases of PCa, along with benign prostatic hyperplasia and normal prostate tissue from 22 cases, we observed a significant difference in B7-H3 protein expression levels (P < 0.001). Subcutaneous and orthotopic mouse models of PCa were imaged using NIR-II fluorescence after AbB7-H3-800CW injection, showing promising results with successful tumor targeting and high-contrast images achieved within 24-48h post-injection. The imaging also enabled the detection of occult PCa lesions approximately 1mm in diameter. In addition, imaging analysis of human PCa and adjacent tissues using AbB7-H3-800CW incubation revealed that cancer tissues exhibited a significantly higher fluorescence intensity than adjacent tissues (P < 0.05), which was conducive to the evaluation of tumor resection margin in vitro. The findings revealed that B7-H3 was a compelling imaging target for PCa. The AbB7-H3-800CW molecular imaging probe is capable of accurately identifying PCa lesions and guiding their removal. This approach can potentially reduce the rate of surgical margins under NIR-II fluorescence guidance.

HoloDiffusion: Sparse Digital Holographic Reconstruction via Diffusion Modeling

In digital holography, reconstructed image quality can be primarily limited due to the inability of a single small aperture sensor to cover the entire field of a hologram. The use of multi-sensor arrays in synthetic aperture digital holographic imaging technology contributes to overcoming the limitations of sensor coverage by expanding the area for detection. However, imaging accuracy is affected by the gap size between sensors and the resolution of sensors, especially when dealing with a limited number of sensors. An image reconstruction method is proposed that combines physical constraint characteristics of the imaging object with a score-based diffusion model, aiming to enhance the imaging accuracy of digital holography technology with extremely sparse sensor arrays. Prior information of the sample is learned by the neural network in the diffusion model to obtain a score function, which alternately constrains the iterative reconstruction process with the underlying physical model. The results demonstrate that the structural similarity and peak signal-to-noise ratio of the reconstructed images using this method are higher than the traditional method, along with a strong generalization ability.

Vehicle-Type Recognition Method for Images Based on Improved Faster R-CNN Model.

The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is of great significance for integrated traffic management. In this paper, an improved faster region with convolutional neural network features (Faster R-CNN) model was proposed for vehicle-type recognition. Firstly, the output features of different convolution layers were combined to improve the recognition accuracy. Then, the average precision (AP) of the recognition model was improved through the contextual features of the original image and the object bounding box optimization strategy. Finally, the comparison experiment used the vehicle image dataset of three vehicle types, including cars, sports utility vehicles (SUVs), and vans. The experimental results show that the improved recognition model can effectively identify vehicle types in the images. The AP of the three vehicle types is 83.2%, 79.2%, and 78.4%, respectively, and the mean average precision (mAP) is 1.7% higher than that of the traditional Faster R-CNN model.

Morphological and Histological Features of Nephrectomy Materials: A Single-Center Experience and Short Review of the Literature

Objective: To determine the demographic characteristics of patients undergoing nephrectomy at a tertiary care hospital and to analyze the spectrum of renal tumors based on histopathological findings of nephrectomy specimens by current literature. Material and Method: The results of nephrectomy materials admitted to the pathology clinic between January 2019 and December 2023 were included in the study. The demographic characteristics of the included patients, presenting complaints, reasons for nephrectomy, surgical method, nephrectomy area, tumor dimension, and histopathological reports were recorded in a standard data form. Results: A total of 325 nephrectomy materials were included in the study. 61.5% of the patients were male. The mean age of the patients was 54.5±20.2 years (min:3 max:91). The most common presenting complaints were flank pain (28.3%). The most commonly observed pathological malignancy was clear cell carcinoma (32%), and it was found to be significantly higher in male patients (38.5%) (p=0.001). The most frequently detected pathological TNM grade of the patients was grade 1, and the histological WHO/ISUP grade was 2. 28.9% of the patients (n:94) received a diagnosis incidentally. Among those incidentally diagnosed patients, 87.2% (n:82) were histopathologically malignant. When benign pathological diagnoses were examined, the most common diagnosis was pyelonephritis, followed by oncocytoma, benign cystic disease, and angiomyolipoma, respectively. Conclusion: According to our study results, malignant tumors are more commonly observed than benign neoplasms. Despite advancements in imaging technologies, the histopathological diagnosis of renal masses cannot be determined preoperatively, and surgical intervention is required for diagnosis

A lipid activated color switchable probe for the imaging of diseased aortic valves

Lipid deposition has been considered one of the key factors in the occurrence of valvular heart disease (VHD) and a great potential target for the diagnosis of VHD. However, the development of lipid imaging technologies and efficient lipid specific probes is in urgent demand. In this work, we have prepared a lipid droplets (LDs) targeted fluorescence probe CPTM based on a push-pull electronic structure for the imaging of diseased aortic valves. CPTM showed obvious twisted intramolecular charge transfer (TICT) effect and its emission changed from 600 nm in water to 508 nm in oil. CPTM not only exhibited good biocompatibility and high photostability, but also impressive LDs specific imaging performance in human primary valvular interstitial cells and human diseased aortic valves. Moreover, the dynamic changes of intracellular LDs could be monitor in real-time after staining with CPTM. These results were expected to offer new ideals for the designing of novel LDs specific probes for further bioimaging applications.

Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN

The recent advancements in automated lung cancer diagnosis through the application of Convolutional Neural Networks (CNN) on Computed Tomography (CT) scans have marked a significant leap in medical imaging and diagnostics. The precision of these CNN-based classifiers in detecting and analyzing lung cancer symptoms has opened new avenues in early detection and treatment planning. However, despite these technological strides, there are critical areas that require further exploration and development. In this landscape, computer-aided diagnostic systems and artificial intelligence, particularly deep learning methods like the region proposal network, the dual path network, and local binary patterns, have become pivotal. However, these methods face challenges such as limited interpretability, data variability handling issues, and insufficient generalization. Addressing these challenges is key to enhancing early detection and accurate diagnosis, fundamental for effective treatment planning and improving patient outcomes. This study introduces an advanced approach that combines a Convolutional Neural Network (CNN) with DenseNet, leveraging data fusion and mobile edge computing for lung cancer identification and classification. The integration of data fusion techniques enables the system to amalgamate information from multiple sources, enhancing the robustness and accuracy of the model. Mobile edge computing facilitates faster processing and analysis of CT scan images by bringing computational resources closer to the data source, crucial for real-time applications. The images undergo preprocessing, including resizing and rescaling, to optimize feature extraction. The DenseNet-CNN model, strengthened by data fusion and edge computing capabilities, excels in extracting and learning features from these CT scans, effectively distinguishing between healthy and cancerous lung tissues. The classification categories include Normal, Benign, and Malignant, with the latter further sub-categorized into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In controlled experiments, this approach outperformed existing state-of-the-art methods, achieving an impressive accuracy of 99%. This indicates its potential as a powerful tool in the early detection and classification of lung cancer, a significant advancement in medical imaging and diagnostic technology.

Structural analysis of the female reptile reproductive system by micro-computed tomography and optical coherence tomography†.

Volumetric data provide unprecedented structural insight to the reproductive tract and add vital anatomical context to the relationships between organs. The morphology of the female reproductive tract in non-avian reptiles varies between species, corresponding to a broad range of reproductive modes and providing valuable insight to comparative investigations of reproductive anatomy. However, reproductive studies in reptilian models, such as the brown anole studied here, have historically relied on histological methods to understand the anatomy. While these methods are highly effective for characterizing the cell types present in each organ, histological methods lose the 3D relationships between images and leave the architecture of the organ system poorly understood. We present the first comprehensive volumetric analyses of the female brown anole reproductive tract using two non-invasive, non-destructive imaging modalities: micro-computed tomography (microCT) and optical coherence tomography (OCT). Both are specialized imaging technologies that facilitate high-throughput imaging and preserve three-dimensional information. This study represents the first time that microCT has been used to study all reproductive organs in this species and the very first time that OCT has been applied to this species. We show how the non-destructive volumetric imaging provided by each modality reveals anatomical context including orientation and relationships between reproductive organs of the anole lizard. In addition to broad patterns of morphology, both imaging modalities provide the high resolution necessary to capture details and key anatomical features of each organ. We demonstrate that classic histological features can be appreciated within whole-organ architecture in volumetric imaging using microCT and OCT, providing the complementary information necessary to understand the relationships between tissues and organs in the reproductive system. This side-by-side imaging analysis using microCT and OCT allows us to evaluate the specific advantages and limitations of these two methods for the female reptile reproductive system.

Complete Neuron Reconstruction Based on Branch Confidence.

In the past few years, significant advancements in microscopic imaging technology have led to the production of numerous high-resolution images capturing brain neurons at the micrometer scale. The reconstructed structure of neurons from neuronal images can serve as a valuable reference for research in brain diseases and neuroscience. Currently, there lacks an accurate and efficient method for neuron reconstruction. Manual reconstruction remains the primary approach, offering high accuracy but requiring significant time investment. While some automatic reconstruction methods are faster, they often sacrifice accuracy and cannot be directly relied upon. Therefore, the primary goal of this paper is to develop a neuron reconstruction tool that is both efficient and accurate. The tool aids users in reconstructing complete neurons by calculating the confidence of branches during the reconstruction process. The method models the neuron reconstruction as multiple Markov chains, and calculates the confidence of the connections between branches by simulating the reconstruction artifacts in the results. Users iteratively modify low-confidence branches to ensure precise and efficient neuron reconstruction. Experiments on both the publicly accessible BigNeuron dataset and a self-created Whole-Brain dataset demonstrate that the tool achieves high accuracy similar to manual reconstruction, while significantly reducing reconstruction time.

Prehospital identification of intracerebral haemorrhage: a scoping review of early clinical features and portable devices

IntroductionPrehospital identification of intracerebral haemorrhage (ICH) in suspected stroke cases may enable the initiation of appropriate treatments and facilitate better-informed transport decisions. This scoping review aims to examine the literature...

Retinal OCT biomarkers associated with reading performance in patients with persistent vs. resolved diabetic macular edema.

Recent advancements in imaging technologies, particularly structural optical coherence tomography (OCT), have improved the understanding of diabetic macular edema (DME) pathophysiology and provided valuable biomarkers for disease progression and visual outcomes. This prospective study aimed to investigate the association between specific retinal biomarkers identified through OCT imaging and reading performance metrics in patients with previously treated persistent versus resolved DME and good visual acuity. Forty-nine eyes from 35 patients with a history of DME were enrolled. Reading performance was assessed using the Radner reading charts, which include standardized sentences with geometrically progressing print sizes. Structural alterations in the inner and outer retina, as well as the retinal pigment epithelium (RPE), were graded based on OCT images. Reading performance, measured as maximum reading speed, was associated with specific retinal biomarkers. The disruption of the ellipsoid zone (EZ) in the parafoveal region and the presence of disorganization of the inner retinal layers (DRIL) in the parafovea were correlated with reduced reading speed. These associations were independent of the presence of intraretinal or subretinal fluid. Understanding the relationship between retinal biomarkers and reading performance could contribute to a comprehensive evaluation of visual function and quality of life in patients with DME, leading to better management strategies and treatment outcomes.

Towards high-performance polarimeters with large-area uniform chiral shells: a comparative study on the polarization detection precision enabled by the Mueller matrix and deep learning algorithm

Polarization detection and imaging technologies have attracted significant attention for their extensive applications in remote sensing, biological diagnosis, and beyond. However, previously reported polarimeters heavily relied on polarization-sensitive materials and pre- established mapping relationships between the Stokes parameters and detected light intensities. This dependence, along with fabrication and detection errors, severely constrain the working waveband and detection precision. In this work, we demonstrated a highly precise, stable, and broadband full-Stokes polarimeter based on large-area uniform chiral shells and a post-established mapping relationship. By precisely controlling the geometry through the deposition of Ag on a large-area microsphere monolayer with a uniform lattice, the optical chirality and anisotropy of chiral shells can reach about 0.15 (circular dichroism, CD) and 1.7, respectively. The post-established mapping relationship between the Stokes parameters and detected light intensities is established through training a deep learning algorithm (DLA) or fitting the derived mapping-relationship formula based on the Mueller matrix theory with a large dataset collected from our home-built polarization system. For the detection precision with DLA, the mean squared errors (MSEs) at 710 nm can reach 0.10% (S1), 0.41% (S2), and 0.24% (S3), while for the Mueller matrix theory, the corresponding values are 0.14% (S1), 0.46% (S2), and 0.48% (S3). The in-depth comparative studies indicate that the DLA outperforms the Mueller matrix theory in terms of detection precision and robustness, especially for weak illumination, small optical anisotropy and chirality. The averaged MSEs over a broad waveband ranging from 500 nm to 750 nm are 0.16% (S1), 0.46% (S2), and 0.61% (S3), which are significantly smaller than those derived from the Mueller matrix theory (0.45% (S1), 1% (S2), and 39.8% (S3)). The optical properties of chiral shells, the theory and DLA enabled mapping-relationships, the combination modes of chiral shells, and the MSE spectra have been systematically investigated.