Transfer Imaging Research Articles

The current situation of Telestroke in Spain.

The current situation of Telestroke in Spain.

Sponsoring Esports to Facilitate Positive and Prevent Negative Image Transfer: The Moderating Effects of Event–Sponsorship Fit

Despite the potential risks of negative associations’ affecting the sponsoring brand, prior sponsorship literature has not delved into this area. Consequently, the purpose of the current study was to investigate how negative and positive image associated with an esports event could be transferred differently to a sponsoring brand based on the level of fit between the brand and the event. The current research adopted the concept of negativity bias to explore how negative associations linked with an esports event might impact the perception of a sponsoring brand compared with positive associations associated with the same event. The authors conducted experiments involving 215 online participants, focusing on a single factor: the level of fit between the brand and the event (high vs. low). The findings revealed that negative associations were more likely to be associated with the sponsoring brand compared with positive associations. Furthermore, in conditions where there was a high fit between the event and the sponsoring brand, positive associations were more prone to transfer to the brand than negative ones. However, when the fit between the event and the sponsoring brand was low, no significant difference in image transfer was observed. This study enriches understanding of how both positive and negative associations can be transferred, contingent upon the level of event–sponsor fit, thereby underscoring the potential adverse consequences of esports sponsorships. Incongruent firms are advised to explore alternative forms of fit, such as image-based similarity, when selecting an esports event to mitigate negative image transfer while fostering positive associations.

Neural texture synthesis and style transfer of coal-rock images in coal mine heading faces using very deep convolutional networks

Neural texture synthesis and style transfer of coal-rock images in coal mine heading faces using very deep convolutional networks

Leveraging image fusion and transfer learning for enhanced tumor diagnosis

In India, the number of brain tumor cases are increasing rapidly. Compared to the current treatment approaches there is a need for efficient and advanced diagnostic approaches. Doctor primarily use magnetic resonance imaging (MRI) and computed tomography (CT) scans for diagnosis, each with its own set of advantages and limitations. Accurate diagnosis of brain tumors requires detailed information about the tumor’s type, size, location, and proliferation rate. But all this information must be estimated accurately and precisely. Even though MRI and CT provide anatomical information regarding tumors, they may not always correctly classify the tumor hence, often requiring the additional biopsy for more detailed analysis. This paper aims to demonstrate the fusion of MRI and CT scans by generating a fused image using transfer learning with convolution neural networks (CNN) that possesses all the important information from both modalities yields better results than using MRI and CT scans alone. This fused scan has the potential to highlight subtle details that may be overlooked on individual scans. This proposed system retains all the best possible functionalities for medical approaches to brain tumor detection. It paves the way to offer a much more impactful healthcare approach to patients by delivering accurate and reliable statistics, enhancing the diagnostic accuracy (96.43%) precedent to any single modality.

Optimization of FRET imaging in Arabidopsis protoplasts.

Optimization of FRET imaging in Arabidopsis protoplasts.

Hippocampal Glutamate Levels and Their Correlation With Subregion Volume in School-Aged Children With MRI-Negative Epilepsy: A Preliminary Study.

Abnormal levels of glutamate constitute a key pathophysiologic mechanism in epilepsy. The use of glutamate chemical exchange saturation transfer (GluCEST) imaging to measure glutamate levels in pediatric epilepsy is rarely reported in research. To investigate hippocampal glutamate level variations in pediatric epilepsy and the correlation between glutamate and hippocampal subregional volumes. Cross-sectional, prospective. A total of 38 school-aged pediatric epilepsy patients with structurally normal MRI as determined by at least two independent radiologists (60% males; 8.7 ± 2.5 years; including 20 cases of focal pediatric epilepsy [FE] and 18 cases of generalized pediatric epilepsy [GE]) and 17 healthy controls (HC) (41% males; 9.0 ± 2.5 years). 3.0 T; 3D magnetization prepared rapid gradient echo (MPRAGE) and 2D turbo spin echo GluCEST sequences. The relative concentration of glutamate was calculated through pixel-wise magnetization transfer ratio asymmetry (MTRasym) analysis of the GluCEST data. Hippocampal subfield volumes were computed from MPRAGE data using FreeSurfer. This study used t tests, one-way analysis of variance, Kruskal-Wallis tests, and Pearson correlation analysis. P < 0.05 was considered statistically significant. The MTRasym values of both the left and right hippocampi were significantly elevated in GE (left: 2.51 ± 0.23 [GE] vs. 2.31 ± 0.12 [HCs], right: 2.50 ± 0.22 [GE] vs. 2.27 ± 0.22 [HCs]). The MTRasym values of the ipsilateral hippocampus were significantly elevated in FE (2.49 ± 0.28 [ipsilateral] vs. 2.29 ± 0.16 [HCs]). The MTRasym values of the ipsilateral hippocampus were significantly increased compared to the contralateral hippocampus in FE (2.49 ± 0.28 [ipsilateral] vs. 2.35 ± 0.34 [contralateral]). No significant differences in hippocampal volume were found between different groups (left hippocampus, P = 0.87; right hippocampus, P = 0.87). GluCEST imaging have potential for the noninvasive measurement of glutamate levels in the brains of children with epilepsy. 2 TECHNICAL EFFICACY: Stage 1.

Effect of Pupil, Spherical Aberration, Decentration, and Tilt on the Optical Performance of Five Different Monofocal IOLs.

To evaluate and compare the effect of pupil size, spherical aberration (SA), decentration, and tilt on the optical performance of five different monofocal intraocular lenses (IOLs). Four aspheric IOLs (Vivinex, Hoya; SN60WF, Alcon Laboratories, Inc; ZCB00, Johnson & Johnson Surgical Vision; Akreos Adapt AO, Bausch & Lomb) with different SA values and one spherical IOL (SN60AT, Alcon Laboratories, Inc) were tested using an OptiSpheric IOL PRO 2 optical bench. The IOL diopter was measured at different apertures. The optical quality of the IOLs was evaluated using the modulation transfer function (MTF), through-focus MTF, and images of the United States Air Force Target test with different apertures and corneal SA values. The IOLs were also measured while they were decentered and tilted. The smaller the aperture, the wider the through-focus MTF of all tested IOLs, and the best image quality was observed with a 3-mm aperture. The diopter of aspheric IOLs with negative SA were negatively correlated with aperture size, but the result was opposite for spherical IOLs. The IOL power difference was more than 0.50 diopters between different apertures. All aspheric IOLs had similar MTF curves with a 3-mm aperture. Aspheric IOLs had the best visual quality with the same or similar corneal SAs. Most of the tested IOLs had lower tolerances in tilt than in decentration. Pupil size, SA value, the degree of decentration, and tilt had different effects on the optical performance of aspheric IOLs. The results can provide guidance for surgeons in clinical practice. [J Refract Surg. 2025;41(3):e220-e230.].

Live-cell FRET assay on the stoichiometry and affinity of the YAP complexes in MCF-7cells.

Live-cell FRET assay on the stoichiometry and affinity of the YAP complexes in MCF-7cells.

Secure Transmission of Images Based on Chaos Encryption and Deep Neural Network

In today’s world of digital media, digital images are the most frequently used method of transmitting images, and therefore, their protection is significant. As it has been seen, the technology keeps on changing and thus, though there have been improvements in encryption of images, the problem of secure transmission is not completely solved. Therefore, the process must be made continuous and refined to protect data privacy and integrity, as well as transmission and storage reliability. This work focuses on the application of cryptography together with neural networks employing Python with Keras for secure image transfer. The proposed method involves image encryption through pixel shuffling, logistic-map chaotic values, and XOR operations to produce encrypted images. The neural network layer decodes the encrypted images and measures the quality of the decrypted images dependent on PSNR, MSE, and Correlation Coefficients. Thus, by comparing these metrics for the original and reconstructed images, the project will improve security and determine the effectiveness of the encryption-decryption method. The combination of the chaotic maps and neural networks gives a strong foundation for image encryption. The chaotic values bring in randomness into the encryption process, thus increasing the level of security while the neural network helps in perfect reconstruction and image quality. This approach is advantageous as it addresses both the issues of data security and, after decryption, image quality and so it offers a midway point for secure image transmission in this technology-driven world.

Three-dimensional amide proton transfer (APT) imaging appliable to navigation surgery can present comparable metabolic activity of glioblastoma to 11C-Methionine PET

BackgroundAmide proton transfer (APT) imaging has been proposed as a technique to assess tumor metabolic activity. We have previously 11C-methionine positron emission tomography (11C-Met-PET) can evaluate the metabolic activity of peritumoral area including infiltrating tumor cells in glioblastoma (GBM). To resolve disadvantages of 11C-Met-PET, in the present study, we aimed to evaluate whether three-dimensional fast spin echo-based APT (3D FSE-APT) imaging is usable for not only presenting the metabolic activity of brain tumors, but also detecting areas where infiltrating tumor cells including glioma stem cells (GSCs) could exist, by applying an image-guided navigation system incorporating 3D FSE-APT to glioblastoma surgery.MethodsTwenty-six consecutive patients with GBMs were enrolled in this study. Among these 26 cases, 10 patients underwent 11C-Met-PET examination. All 26 patients underwent two-dimensional single shot fast spine echo-based APT acquisition with a chemical exchange saturation transfer sequence (2D SSFSE-APT). The most recent 14 cases underwent 3D FSE-APT to examine whether 3D APT imaging was applicable to the navigation system. We investigated the clinical applicability of 3D FSE-APT by comparison with 2D SSFSE-APT and evaluated the utility of 3D FSE-APT as a metabolic imaging guide in the intraoperative navigation system. We also analyzed whether 3D FSE-APT can depict the extent of infiltrating tumor cells including GSCs in the peritumoral area in GBM.ResultsThe most recent 14 cases underwent 3D FSE-APT. The 3D FSE-APT was visually almost equivalent to 2D SSFSE-APT and mean APT intensity (APTmean) in GBM obtained by 3D FSE-APT was almost equal to that from 2D SSFSE-APT. Mean APTmean on 2D SSFSE-APT at the site showing a tumor-to-contralateral normal brain tissue ratio (TNR) of 1.4 on 11C-Met-PET was 1.52 ± 0.16%. In contrast, mean APTmean on 3D FSE-APT at the same site was 1.30 ± 0.06%. The optimal cut-off value for APTmean on 3D FSE-APT was evaluated as 1.28%, offering 100% sensitivity and 100% specificity. Incorporating 3D FSE-APT into the navigation system allowed tumor resection including infiltrating tumor cells under image-guided navigation. Mean Ki-67 staining index in the area with a mean APTmean of 1.28% was 11.8% (range, 5.0–20.0%).ConclusionsThe area of tumor invasion could be evaluated by 3D FSE-APT in a similar way to 11C-Met-PET, and the cut-off value for deciding the borderline between the area including infiltrating tumor cells and that with almost no tumor cells was 12.8%. In addition, 3D FSE-APT could be applied to navigation systems and may have great potential as an imaging modality replacing 11C-Met-PET in GBM surgery.

IR thermography & NN models for damaged component thickness detection

To achieve rapid detection of damage thickness in metal components using infrared thermography, a combination of heat transfer theory and image theory was employed. This involved theoretical analysis, finite element numerical simulation, a BP neural network prediction model, and infrared thermography experiments. Infrared thermal wave experiments were conducted under different heating temperatures. By analyzing the obtained temperature data, the response characteristics of surface temperature distribution to component thickness were investigated. The COMSOL numerical simulation software was used to simulate the surface temperature of the metal components. The bevel-cut metal components were heated to 80 °C, 105 °C, and 130 °C, and the fitted experimental temperature data were analyzed in conjunction with the simulated temperature data of the bevel-cut metal components. It was found that the fitted experimental temperature rise curve aligned with the simulated temperature rise curve trend. A comparative analysis of the simulation results and experimental values showed that the simulated temperature rise curve was basically consistent with the fitted experimental temperature curve, validating the feasibility of using numerical simulation as a substitute for experiments. The numerical simulation data were divided into a training set and a prediction set in an 8:2 ratio. Through training with the BP neural network, the predicted data were found to be basically consistent with the experimental data, verifying the feasibility of using the BP neural network for rapid detection of damage thickness in metal components. This laid the foundation for the subsequent promotion and application of BP neural network technology for rapid detection of damage thickness in metal components. This study holds significant importance for the application of neural network-based rapid detection technology for metal component thickness in the engineering field.

Multi-Source Training-Free Controllable Style Transfer via Diffusion Models

Diffusion models, as representative models in the field of artificial intelligence, have made significant progress in text-to-image synthesis. However, studies of style transfer using diffusion models typically require a large amount of text to describe semantic content or specific painting attributes, and the style and layout of semantic content in synthesized images are frequently uncertain. To accomplish high-quality fixed content style transfer, this paper adopts text-free guidance and proposes a multi-source, training-free and controllable style transfer method by using single image or video as content input and single or multiple style images as style guidance. To be specific, the proposed method firstly fuses the inversion noise of a content image with that of a single or multiple style images as the initial noise of stylized image sampling process. Then, the proposed method extracts the self-attention mechanism’s query, key, and value vectors from the DDIM inversion process of content and style images and injects them into the stylized image sampling process to improve the color, texture and semantics of stylized images. By setting the hyperparameters involved in the proposed method, the style transfer effect of symmetric style proportion and asymmetric style distribution can be achieved. By comparing with state-of-the-art baselines, the proposed method demonstrates high fidelity and excellent stylized performance, and can be applied to numerous image or video style transfer tasks.

Automated identification of impact spatters and fly spots with a residual neural network.

Automated identification of impact spatters and fly spots with a residual neural network.

Mothership versus Drip-and-Ship for stroke in a rural area: A French prospective observational study

BackgroundThe availability of mechanical thrombectomy (MT) is limited. Thus, there are two paradigms for patients living closer to a primary stroke center (PSC) than a comprehensive stroke center (CSC) capable of MT: “Mothership” (direct referral to a CSC) and “Drip-and-Ship” (referral to a PSC for imaging and thrombolysis and transfer to a CSC for thrombectomy or monitoring). We aimed to compare the prognosis of patients at three months between the two paradigms in a rural area. MaterialsFrom September 2019 to March 2021, we prospectively included patients living closer to a PSC than the one CSC, regardless of the type of stroke or reperfusion treatment. The proportion of patients with a good functional outcome (Rankin≤2) at three months was compared between the two initial orientations for all patients and for subgroups: patients with ischemic stroke and patients treated by MT. ResultsAmong the 206 patients included, 103 were admitted directly to the CSC (82.5% had an ischemic stroke and 24.3% a MT) and 103 initially admitted to a PSC and then transferred to the CSC (100% had an ischemic stroke and 52.4% a MT). The proportion of patients with a good outcome was comparable between the two groups (54.5% vs. 43.7%, P=0.22). Among the 79 patients who underwent MT, the prognosis at three months was better in the Mothership group (49.3% vs. 15.3%, P=0.01). ConclusionThe functional prognosis is comparable between Mothership and Drip-and-Ship paradigms in our setting, despite a trend towards a better prognosis for the Mothership. As has been shown in urban settings, the mothership paradigm also leads to a better prognosis for patients treated with MT in a rural setting.

Ultra-high-resolution brain MRI at 0.55T: bSTAR and its application to magnetization transfer ratio imaging.

This study aims to evaluate the feasibility of structural sub-millimeter isotropic brain MRI at 0.55 T using a 3D half-radial dual-echo balanced steady-state free precession sequence, termed bSTAR and to assess its potential for high-resolution magnetization transfer imaging. Phantom and in-vivo imaging of three healthy volunteers was performed on a low-field 0.55 T MR-system with isotropic bSTAR resolution settings of 0.87 × 0.87 × 0.87 mm3 and 0.69 × 0.69 × 0.69 mm3. Furthermore, off-resonance mapping was performed using 3D double-echo spoiled gradient imaging. For magnetization transfer (MT) MRI, the RF pulse duration of the 0.87 mm bSTAR scan was modified. Data were reconstructed using a GPU-accelerated compressed sensing algorithm. Magnetization transfer ratio (MTR) maps were calculated from two bSTAR scans with and without RF pulse prolongation. The MTR scan took 5 minutes and the reproducibility was assessed through repeated scans. Off-resonance mapping revealed that bSSFP brain imaging with TR < 5ms is essentially free of off-resonance-related artifacts even near the nasal cavities. Phantom and in-vivo scans demonstrated the feasibility of sub-millimeter isotropic bSTAR imaging. MTR maps obtained with high isotropic resolution bSTAR showed contrast between white and gray matter in agreement with expectations from high-field studies. The MTR measurements were highly reproducible with an average inter-scan MTR peak value of 43.3 ± 0.3 percent units. This study demonstrated the potential of sub-millimeter and artifact-free morphologic brain imaging at 0.55 T using bSTAR leveraging the advantages of low-field MRI, such as reduced susceptibility artifacts and improved radio-frequency field homogeneity. Furthermore, MT-sensitized bSTAR brain MRI enabled whole-brain MTR assessment within clinically feasible times and with high reproducibility.

Spinal cord demyelination predicts neurological deterioration in patients with mild degenerative cervical myelopathy

BackgroundDegenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally. Clinical guidelines regarding surgery for patients with mild DCM and minimal symptoms remain uncertain. This...

Literature Review on Road Damage Detection and Severity Recognition: Leveraging Computer Vision

The increasing demand for quick and precise road maintenance has therefore been underscored by extensive research that was created over the last years, to find automated systems in which computer vision can work — harnessing deep learning methodologies to detect defects on roads &amp; figure out how severe these actually are. It provides a discussion of different methodologies and technologies in these pipelines such as Convolutional Neural Networks (CNNs), YOLO models, ensembling learning etc. They have a good performance in various kinds of road damage: cracks, potholes, and surface deformations identification with high accuracy. They also use methods like image tiling, transfer learning, and multiple spectral data to increase their robustness so that models can be used effectively in different environmental conditions.But the avoidance of problems is still a long way off. Nonetheless, the disparate road scenarios across geographies and absence of labeled high-quality datasets remained as some common bottlenecks in addition to real-time-driven requirements that mandate significant compute powers. Although much more headway has been made in recognizing severity (using segmentation approaches and multimodal data, in particular), the combination of detection and classification altogether within a single joint pipeline is significantly harder.A comprehensive analysis of state‑of-the‑art software tools, recent challenges, and advances to improve the detection/ severity identification capabilities of road damage systems. It emphasizes how to address data problems, enhance model generalization capability in unseen scenes(a.k.a novel), and minimize latency. One final takeaway is the valuable reminder of the need to create operational standards for classifier performance required to drive practice adoption and scale in larger deployment settings ideally incentivizing public-private partnerships.

Evidence Suggesting Prolonged Neuroinflammation in a Subset of Children after Moderate/Severe TBI: A UCLA RAPBI Study.

Traumatic brain injury (TBI) presents a public health concern as a leading cause of death and disability in children. Pediatric populations are particularly vulnerable to adverse outcomes following TBI due to periods of rapid growth, synaptic pruning, and myelination. Pediatric patients with moderate-severe TBI (msTBI) and healthy controls were evaluated from the post-acute (2-5 months) to chronic phase (13-19 months) of recovery using diffusion magnetic resonance imaging (dMRI) and interhemispheric transfer time (IHTT), which is an event-related potential measure the speed of information transfer across the corpus callosum. We previously identified two subgroups of patients based on IHTT, with one group showing a significantly slower IHTT (TBI-slow), poorer cognitive performance, and progressive structural damage. In contrast, the other group (TBI-normal) did not differ from controls on IHTT or cognitive performance and showed relative structural recovery over time. Here, we examined group differences in restricted diffusion imaging (RDI), which is a dMRI metric sensitive to inflammation. Comparing TBI-slow, TBI-normal, and controls on RDI cross-sectionally, dMRI connectometry analysis revealed higher RDI across the white matter in the TBI-slow group compared to both the control and TBI-normal groups. Longitudinal analyses indicated that while both TBI groups exhibited a decrease in RDI over time, suggesting resolution of neuroinflammation and recovery, the decreases in the TBI-slow group were smaller. The differences in RDI between TBI-slow and TBI-normal suggest that inflammation may play a key role in the prolonged recovery, including brain structure, cognitive performance, and symptom reports, of pediatric patients with msTBI.

P0399 Combination of MR Cell Size Imaging and Magnetization Transfer Imaging Reflecting Intestinal Fibrosis to Predict Intestinal Disease Progression in Patients with Crohn’s Disease

Abstract Background More than half of Crohn’s disease (CD) patients develop complications such as strictures due to intestinal fibrosis1. Intestinal fibrosis is characterized by proliferation of mesenchymal cells, especially myofibroblasts transdifferentiated from fibroblasts, and thus excessive accumulation of extracellular collagen2. Therefore, simultaneously assessing mesenchymal cells and extracellular collagen can offer a more comprehensive perspective of intestinal fibrosis and enhance predictions of disease progression. Magnetization transfer imaging (MTI) can accurately quantify intestinal collagen levels3. Recently, time-dependent diffusion MRI (TD-dMRI) demonstrated the efficacy in mapping cellular microstructures4. Methods From July 2023 through March 2024, 124 CD patients were prospectively enrolled with 31 underwent surgery at baseline. Participants underwent TD-dMRI to obtain microstructural parameters, such as cell diameter (d), intracellular volume fraction (fin), extracellular diffusivity (Dex) and Cellularity. MT-MRI were performed to obtain the normalized MT ratio (MTR) with a reported threshold of ≥0.71 indicating high collagen levels. For surgical patients, the target intestines, including diseased and normal segments were collected and myofibroblasts/fibroblasts-area-ratio was calculated to reflect mesenchymal cell states and correlated with TD-dMRI parameters. 93 nonsurgical patients were followed up for at least 6 months to evaluate intestinal disease progression. Results In 63 samples from 31 surgical patients, d had the highest correlation with myofibroblasts/fibroblasts-area-ratio (r=0.58; P&amp;lt;0.001). d, fin, cellularity and Dex were significantly different between diseased and normal samples (all P&amp;lt;0.05). Among TD-dMRI parameters, d had the highest AUC (AUC=0.86; P&amp;lt;0.001) for distinguishing diseased and normal samples. The optimal threshold of d was 11 μm and d≥11 μm indicated an activated profibrotic mesenchymal cell state. Therefore, 93 nonsurgical patients were divided into four groups based on the baseline d and normalized MTR: group 1 (d&amp;lt;11 μm, normalized MTR&amp;lt;0.71), group 2 (d≥11 μm, normalized MTR&amp;lt;0.71), group 3 (d≥11 μm, normalized MTR≥0.71), and group 4 (d&amp;lt;11 μm, normalized MTR≥0.71). In follow-up cohort, group 3 had the shortest disease-progression-free survival, followed by groups 2, 4, and 1 (P=0.002). Additionally, multivariable Cox regression analysis indicated that d was the only significant risk factor for disease progression (HR: 1.2; P=0.001). Conclusion TD-dMRI-derived d effectively characterizes fibrosis-associated cell features in CD, and combined with normalized MTR enables a comprehensive assessment of intestinal fibrosis and accurate prediction of disease progression.

Advanced Magnetic Resonance Imaging for Early Diagnosis and Monitoring of Movement Disorders.

Advanced magnetic resonance imaging (MRI) techniques are transforming the study of movement disorders by providing valuable insights into disease mechanisms. This narrative review presents a comprehensive overview of their applications in this field, offering an updated perspective on their potential for early diagnosis, disease monitoring, and therapeutic evaluation. Emerging MRI modalities such as neuromelanin-sensitive imaging, diffusion-weighted imaging, magnetization transfer imaging, and relaxometry provide sensitive biomarkers that can detect early microstructural degeneration, iron deposition, and connectivity disruptions in key regions like the substantia nigra. These techniques enable earlier and more accurate differentiation of movement disorders, including Parkinson's disease, progressive supranuclear palsy, multiple system atrophy, corticobasal degeneration, Lewy body and frontotemporal dementia, Huntington's disease, and dystonia. Furthermore, MRI provides objective metrics for tracking disease progression and assessing therapeutic efficacy, making it an indispensable tool in clinical trials. Despite these advances, the absence of standardized protocols limits their integration into routine clinical practice. Addressing this gap and incorporating these techniques more systematically could bring the field closer to leveraging advanced MRI for personalized treatment strategies, ultimately improving outcomes for individuals with movement disorders.