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Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disorder with suspected neuroinflammatory pathophysiology. However, previous diffusion tensor imaging (DTI) studies have reported inconsistent white matter abnormalities in ME/CFS, and specific white matter inflammatory changes remain poorly characterised. This study employed an advanced diffusion‐based neuroinflammation imaging (NII) model to investigate white matter neuroinflammation in ME/CFS. Diffusion MRI data from 67 ME/CFS patients (median age, 38; and 54 women) and 67 rigorously matched healthy controls (HCs) (median age 38; and 52 women) were analysed. Seven NII‐derived metrics were computed: hindered water ratio (NII‐HR), restricted fraction (NII‐RF), fibre fraction (NII‐FF), axial diffusivity (NII‐AD), radial diffusivity (NII‐RD), mean diffusivity (NII‐MD) and fractional anisotropy (NII‐FA). Conventional DTI metrics were also calculated. Tract‐based spatial statistics were used to perform voxel‐wise group comparisons, and multiple regression analysis was conducted to examine the relationship between NII/DTI metrics and clinical measures of mental health, physical health, sleep quality, disability, disease severity and disease duration. Compared to HCs, ME/CFS patients exhibited widespread white matter abnormalities, including significantly lower NII‐HR and NII‐RF, and significantly higher NII‐FF, NII‐AD, NII‐MD and NII‐FA across association, commissural and projection fibres. Additionally, some regions showed decreased NII‐AD and NII‐MD in ME/CFS. Lower NII‐RF, NII‐AD and NII‐MD in ME/CFS were significantly associated with worse mental health, while lower NII‐RF was also associated with a higher level of disability. Among ME/CFS patients, higher NII‐FF was associated with lower disease severity. Conventional DTI showed minimal group differences and no significant clinical associations. This study provides in vivo evidence of white matter neuroinflammation in ME/CFS, characterised by cerebral edema (reduced NII‐HR), cellular infiltration (reduced NII‐RF) and axonal reorganisation (increased NII‐FF). This suggests NII‐derived indices may serve as sensitive biomarkers for neuroinflammation in ME/CFS. Diffusion‐based neuroinflammation imaging (NII) reveals widespread white matter abnormalities in ME/CFS patients, undetected by conventional DTI. NII metrics associate with mental health, disability and disease severity, providing novel evidence of neuroinflammation and highlighting NII's potential as a biomarker in ME/CFS.

Magnetic Resonance Imaging (MRI) is among the most important medical imaging techniques available today. There is an installed base of approximately 15,000 MRI scanners worldwide. Each of these scanners is capable of running many different \"pulse sequences\", which are governed by physics and engineering principles, and implemented by software programs that control the MRI hardware. To utilize an MRI scanner to the fullest extent, a conceptual understanding of its pulse sequences is crucial. This book offers a complete guide that can help the scientists, engineers, clinicians, and technologists in the field of MRI understand and better employ their scanner. ·Explains pulse sequences, their components, and the associated image reconstruction methods commonly used in MRI·Provides self-contained sections for individual techniques·Can be used as a quick reference guide or as a resource for deeper study·Includes both non-mathematical and mathematical descriptions ·Contains numerous figures, tables, references, and worked example problems

Traumatic brain injury (TBI) is a major contributor of long-term disability and a leading cause of death worldwide. A series of secondary injury cascades can contribute to cell death, tissue loss, and ultimately to the development of functional impairments. However, there are currently no effective therapeutic interventions that improve brain outcomes following TBI. As a result, a number of experimental TBI models have been developed to recapitulate TBI injury mechanisms and to test the efficacy of potential therapeutics. The pig model has recently come to the forefront as the pig brain is closer in size, structure, and composition to the human brain compared to traditional rodent models, making it an ideal large animal model to study TBI pathophysiology and functional outcomes. This review will focus on the shared characteristics between humans and pigs that make them ideal for modeling TBI and will review the three most common pig TBI models-the diffuse axonal injury, the controlled cortical impact, and the fluid percussion models. It will also review current advances in functional outcome assessment measures and other non-invasive, translational TBI detection and measurement tools like biomarker analysis and magnetic resonance imaging. The use of pigs as TBI models and the continued development and improvement of translational assessment modalities have made significant contributions to unraveling the complex cascade of TBI sequela and provide an important means to study potential clinically relevant therapeutic interventions.

Simultaneous Localization and Mapping (SLAM) technology is one of the best methods for fast 3D reconstruction and mapping. However, the accuracy of SLAM is not always high enough, which is currently the subject of much research interest. Panoramic vision can provide us with a wide range of angles of view, many feature points, and rich information. The panoramic multi-view cross-imaging feature can be used to realize instantaneous omnidirectional spatial information acquisition and improve the positioning accuracy of SLAM. In this study, we investigated panoramic visual SLAM positioning technology, including three core research points: (1) the spherical imaging model; (2) spherical image feature extraction and matching methods, including the Spherical Oriented FAST and Rotated BRIEF (SPHORB) and ternary scale-invariant feature transform (SIFT) algorithms; and (3) the panoramic visual SLAM algorithm. The experimental results show that the method of panoramic visual SLAM can improve the robustness and accuracy of a SLAM system.

In traditional 3D reconstruction using UAV images, only radiance information, which is treated as a geometric constraint, is used in feature matching, allowing for the restoration of the scene’s structure. After introducing radiance supervision, NeRF can adjust the geometry in the fixed-ray direction, resulting in a smaller search space and higher robustness. Considering the lack of NeRF construction methods for aerial scenarios, we propose a new NeRF point sampling method, which is generated using a UAV imaging model, compatible with a global geographic coordinate system, and suitable for a UAV view. We found that NeRF is optimized entirely based on the radiance while ignoring the direct geometry constraint. Therefore, we designed a radiance correction strategy that considers the incidence angle. Our method can complete point sampling in a UAV imaging scene, as well as simultaneously perform digital surface model construction and ground radiance information recovery. When tested on self-acquired datasets, the NeRF variant proposed in this paper achieved better reconstruction accuracy than the original NeRF-based methods. It also reached a level of precision comparable to that of traditional photogrammetry methods, and it is capable of outputting a surface albedo that includes shadow information.

Considering that structured light depth imagers are difficult to use for precision measurements due to their limited measurement accuracy, we propose an innovative method for correcting structural parameters of structured light depth imagers to reduce the depth measurement error caused by structural parameter errors. For the structured light depth imager, the analytical imaging model is established, and the model of depth error caused by structural parameter errors is established based on the analysis of the depth measurement error analysis. Then, structural parameters are corrected according to the depth measurement error analysis and processing based on experimental depth imaging data of the standard reference plane at the maximum depth. As a result, the corrected analytical imaging model and corrected depth measurement values are obtained. Experimental results have demonstrated the success of this proposed method and its simplicity and convenience.

The thyroid gland, which is sensitive to electromagnetic radiation, plays a crucial role in the regulation of the hormonal levels of the human body. Biosensors, on the other hand, are essential to access information and derive metrics about the condition of the thyroid by means of of non-invasive techniques. This paper provides a systematic overview of the recent literature on bioelectromagnetic models and methods designed specifically for the study of the thyroid. The survey, which was conducted within the scope of the radiation transmitter–thyroid model–sensor system, is centered around the following three primary axes: the bands of the frequency spectrum taken into account, the design of the model, and the methodology and/or algorithm. Our review highlights the areas of specialization and underscores the limitations of each model, including its time, memory, and resource requirements, as well as its performance. In this manner, this specific work may offer guidance throughout the selection process of a bioelectromagnetic model of the thyroid, as well as a technique for its analysis based on the available resources and the specific parameters of the electromagnetic problem under consideration.

Compact, high-quality imaging systems are highly desired for scientific, industrial, and consumer applications. Metalenses combined with computational imaging offer a promising solution for developing such systems, yet their performance is fundamentally limited by the commonly used point-to-point imaging model, which forces trade-offs between aperture size, F-number, field of view (FOV), waveband width, and image quality. Here, we experimentally demonstrate that a neural array imaging model can overcome these long-standing trade-offs, achieving a 25-Hz full-color imaging camera with a 2.76-mm aperture, 1.45 F-number, 50 ∘ FOV, and a spectral range of 400–700 nm. The camera achieves image quality comparable to commercial compound lenses (e.g., Edmund 33-300) in both indoor and outdoor environments, while reducing the total track length by a factor of 13. We further demonstrate its suitability for object detection and depth estimation in real-world scenarios. This neural array imaging model is also applied to polarization imaging, showcasing its scalability and versatility for broadband applications.

Prognostic biomarkers that can reliably predict early disease progression of non-small cell lung cancer (NSCLC) are needed for identifying those patients at high risk for progression, who may benefit from more intensive treatment. In this work, we aimed to identify an imaging signature for predicting progression-free survival (PFS) of locally advanced NSCLC. : This retrospective study included 82 patients with stage III NSCLC treated with definitive chemoradiotherapy for whom both baseline and mid-treatment PET/CT scans were performed. They were randomly placed into two groups: training cohort (n=41) and testing cohort (n=41). All primary tumors and involved lymph nodes were delineated. Forty-five quantitative imaging features were extracted to characterize the tumors and involved nodes at baseline and mid-treatment as well as differences between two scans performed at these two points. An imaging signature was developed to predict PFS by fitting an L1-regularized Cox regression model. : The final imaging signature consisted of three imaging features: the baseline tumor volume, the baseline maximum distance between involved nodes, and the change in maximum distance between the primary tumor and involved nodes measured at two time points. According to multivariate analysis, the imaging model was an independent prognostic factor for PFS in both the training (hazard ratio [HR], 1.14, 95% confidence interval [CI], 1.04-1.24; = 0.003), and testing (HR, 1.21, 95% CI, 1.10-1.33; = 0.048) cohorts. The imaging signature stratified patients into low- and high-risk groups, with 2-year PFS rates of 61.9% and 33.2%, respectively ( = 0.004 [log-rank test]; HR, 4.13, 95% CI, 1.42-11.70) in the training cohort, as well as 43.8% and 22.6%, respectively ( = 0.006 [log-rank test]; HR, 3.45, 95% CI, 1.35-8.83) in the testing cohort. In both cohorts, the imaging signature significantly outperformed conventional imaging metrics, including tumor volume and SUV value (C-indices: 0.77-0.79 for imaging signature, and 0.53-0.73 for conventional metrics). : Evaluation of early treatment response by combining primary tumor and nodal imaging characteristics may improve the prediction of PFS of locally advanced NSCLC patients.

The automated detection of yarn margins is crucial for ensuring the continuity and quality of production in textile workshops. Traditional methods rely on workers visually inspecting the yarn margin to determine the timing of replacement; these methods fail to provide real-time data and cannot meet the precise scheduling requirements of modern production. The complex environmental conditions in textile workshops, combined with the cylindrical shape and repetitive textural features of yarn bobbins, limit the application of traditional visual solutions. Therefore, we propose a visual measurement method based on the geometric characteristics of binocular imaging: First, all contours in the image are extracted, and the distance sequence between the contours and the centroid is extracted. This sequence is then matched with a predefined template to identify the contour information of the yarn bobbin. Additionally, four equations for the tangent line from the camera optical center to the edge points of the yarn bobbin contour are established, and the angle bisectors of each pair of tangents are found. By solving the system of equations for these two angle bisectors, their intersection point is determined, giving the radius of the yarn bobbin. This method overcomes the limitations of monocular vision systems, which lack depth information and suffer from size measurement errors due to the insufficient repeat positioning accuracy when patrolling back and forth. Next, to address the self-occlusion issues and matching difficulties during binocular system measurements caused by the yarn bobbin surface’s repetitive texture, an imaging model is established based on the yarn bobbin’s cylindrical characteristics. This avoids pixel-by-pixel matching in binocular vision and enables the accurate measurement of the remaining yarn margin. The experimental data show that the measurement method exhibits high precision within the recommended working distance range, with an average error of only 0.68 mm.