High-resolution Imaging Research Articles

MRI Super-Resolution Analysis via MRISR: Deep Learning for Low-Field Imaging

Abstract: This paper presents a novel MRI super-resolution analysis model, MRISR. Through the utilization of generative adversarial networks for the estimation of degradation kernels and the injection of noise, we have constructed a comprehensive dataset of high-quality paired high- and low-resolution MRI images. The MRISR model seamlessly integrates VMamba and Transformer technologies, demonstrating superior performance across various no-reference image quality assessment metrics compared with existing methodologies. It effectively reconstructs high-resolution MRI images while meticulously preserving intricate texture details, achieving a fourfold enhancement in resolution. This research endeavor represents a significant advancement in the field of MRI super-resolution analysis, contributing a cost-effective solution for rapid MRI technology that holds immense promise for widespread adoption in clinical diagnostic applications.

CNCAN: Contrast and normal channel attention network for super-resolution image reconstruction of crops and weeds

Numerous studies have been performed to apply camera vision technologies in robot-based agriculture and smart farms. In particular, to obtain high accuracy, it is essential to procure high-resolution (HR) images, which requires a high-performance camera. However, due to high costs it is difficult to widely apply the camera in agricultural robots. To overcome this limitation, we propose contrast and normal channel attention network (CNCAN) for super-resolution reconstruction (SR), which is the first research for the accurate semantic segmentation of crops and weeds even with low-resolution (LR) images captured by low-cost and LR camera. Attention block and activation function that considers high frequency and contrast information of images are used in CNCAN, and the residual connection method is applied to improve the learning stability.As a result of experimenting with three open datasets, namely, Bonirob, rice seedling and weed, and crop/weed field image (CWFID) datasets, the mean intersection of union (MIOU) results of semantic segmentation for crops and weeds with SR images through CNCAN were 0.7685, 0.6346, and 0.6931 in the Bonirob, rice seedling and weed, and CWFID datasets, respectively, confirming higher accuracy than other state-of-the-art methods for SR.

On-Orbit Geometric Calibration and Accuracy Validation of the Jilin1-KF01B Wide-Field Camera

On-orbit geometric calibration is key to improving the geometric positioning accuracy of high-resolution optical remote sensing satellite data. Grouped calibration with geometric consistency (GCGC) is proposed in this paper for the Jilin1-KF01B satellite, which is the world’s first satellite capable of providing 150-km swath width and 0.5-m resolution data. To ensure the geometric accuracy of high-resolution image data, the GCGC method conducts grouped calibration of the time delay integration charge-coupled device (TDI CCD). Each group independently calibrates the exterior orientation elements to address the multi-time synchronization issues between imaging processing system (IPS). An additional inter-chip geometric positioning consistency constraint is used to enhance geometric positioning consistency in the overlapping areas between adjacent CCDs. By combining image simulation techniques associated with spectral bands, the calibrated panchromatic data are used to generate simulated multispectral reference band image as control data, thereby enhancing the geometric alignment consistency between panchromatic and multispectral data. Experimental results show that the average seamless stitching accuracy of the basic products after calibration is better than 0.6 pixels, the positioning accuracy without ground control points(GCPs) is better than 20 m, the band-to-band registration accuracy is better than 0.3 pixels, the average geometric alignment consistency between panchromatic and multispectral data are better than 0.25 multispectral pixels, the geometric accuracy with GCPs is better than 2.1 m, and the geometric alignment consistency accuracy of multi-temporal data are better than 2 m. The GCGC method significantly improves the quality of image data from the Jilin1-KF01B satellite and provide important references and practical experience for the geometric calibration of other large-swath high-resolution remote sensing satellites.

A global colour mosaic of Mars from Mars Express HRSC high altitude observations

The ever-changing transparency of the Martian atmosphere hinders the determination of absolute surface colour from spacecraft images. While individual high-resolution images from low orbit reveal numerous colour details of the geology, the colour variation between images caused by scattering off atmospheric dust can easily be of greater magnitude. The construction of contiguous large-scale mosaics has thus required a strategy to suppress the influence of scattering, often a form of high-pass filtering, which limits their ability to convey colour variation information over distances greater than the dimensions of single images. Here we use a dedicated high altitude observation campaign with the Mars Express High Resolution Stereo Camera (HRSC) (Neukum and Jaumann, 2004; Jaumann et al., 2007), applying a novel iterative method to construct a globally self-consistent colour model. We apply the model to colour-reference a high-altitude mosaic incorporating long-range colour variation information. Using only the relative colour information internal to individual images, the influence of absolute image to image colour changes caused by scattering is minimised, while the model enables colour variations across image boundaries to be self-consistently reconstructed. The resulting mosaic shows a level of colour detail comparable to single images, while maintaining continuity of colour features over much greater distances, thereby increasing the utility of HRSC colour images in the tracing and analysis of martian surface structures.

Real-Time Resolution Enhancement of Confocal Laser Scanning Microscopy via Deep Learning

Confocal laser scanning microscopy is one of the most widely used tools for high-resolution imaging of biological cells. However, the imaging resolution of conventional confocal technology is limited by diffraction, and more complex optical principles and expensive optical-mechanical structures are usually required to improve the resolution. This study proposed a deep residual neural network algorithm that can effectively improve the imaging resolution of the confocal microscopy in real time. The reliability and real-time performance of the algorithm were verified through imaging experiments on different biological structures, and an imaging resolution of less than 120 nm was achieved in a more cost-effective manner. This study contributes to the real-time improvement of the imaging resolution of confocal microscopy and expands the application scenarios of confocal microscopy in biological imaging.

Estimating Total Rice Biomass and Crop Yield at Field Scale Using PlanetScope Imagery Through Hybrid Machine Learning Models

AbstractNear real-time crop monitoring has been a challenging due to the lack of high-resolution remote sensing images suitable for agricultural applications. The PlanetScope constellation, comprising approximately 130 Dove satellites, collects images of the entire Earth daily, with a resolution of 3.7 m. The high-resolution images from the PlanetScope satellite, along with vegetation indices, geo-environmental data, and soil and crop parameters, were utilized and analysed using machine learning models to enhance the accuracy of predicting total biomass and rice crop yield at the field scale. The study area, covering nearly 214 sample rice plots, was located in the Tarekswar block of Hooghly, West Bengal, India. Alongside ten vegetation indices and three Principal Component Analysis (PCA) soil nutrient levels, approximately thirty-six factors were analyzed to predict rice total biomass and crop yield using ten machine learning (ML) models, namely Random forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), Bagging Tree (Treebag), Generalized Additive Models (gamSpline), Elastic Net (enet), Ordinary regression with LASSO penalty (rqlasso), Tree Models from Genetic Algorithm (evtree), Bayesian Regularized Neutral Networks (brnn), cubist models, and there hybrid of ensembles. Boruta and multi-collinearity analysis were also conducted for the selected factors to explore their influence levels. The study area exhibited robust rice yields ranging from 5 to 10 t/ha, accompanied by healthy biomass growth. Four ML models ─cubist, random forest, enet, and the hybrid model—showed promising predictions with R2 > 0.88. Most models classified less than 20 ha of the study area as falling into the “very-low suitable class”, showing the region’s suitability for rice cultivation due to its highly fertile alluvial soil. Boruta sensitive analysis revealed that nearly 24 individual factors significantly influenced the final crop yield including, organic carbon (OC), phosphorus (P), electrical conductivity (EC), mechanization level, and the majority of the vegetation indices. A critical analysis carried out through the Map query tool showed that five vegetation indices estimated via PlanetScope displayed strong correlations (exceeding 89%) in identifying areas with high to very high rice yields. The study can serve as a guideline for near-real-time crop monitoring in the near future, using high-resolution PlanetScope images.

Exploring dual energy CT synthesis in CBCT-based adaptive radiotherapy and proton therapy: application of denoising diffusion probabilistic models

Background.Adaptive radiotherapy (ART) requires precise tissue characterization to optimize treatment plans and enhance the efficacy of radiation delivery while minimizing exposure to organs at risk. Traditional imaging techniques such as cone beam computed tomography (CBCT) used in ART settings often lack the resolution and detail necessary for accurate dosimetry, especially in proton therapy.Purpose.This study aims to enhance ART by introducing an innovative approach that synthesizes dual-energy computed tomography (DECT) images from CBCT scans using a novel 3D conditional denoising diffusion probabilistic model (DDPM) multi-decoder. This method seeks to improve dose calculations in ART planning, enhancing tissue characterization.Methods.We utilized a paired CBCT-DECT dataset from 54 head and neck cancer patients to train and validate our DDPM model. The model employs a multi-decoder Swin-UNET architecture that synthesizes high-resolution DECT images by progressively reducing noise and artifacts in CBCT scans through a controlled diffusion process.Results.The proposed method demonstrated superior performance in synthesizing DECT images (High DECT MAE 39.582 ± 0.855 and Low DECT MAE 48.540± 1.833) with significantly enhanced signal-to-noise ratio and reduced artifacts compared to traditional GAN-based methods. It showed marked improvements in tissue characterization and anatomical structure similarity, critical for precise proton and radiation therapy planning.Conclusions.This research has opened a new avenue in CBCT-CT synthesis for ART/APT by generating DECT images using an enhanced DDPM approach. The demonstrated similarity between the synthesized DECT images and ground truth images suggests that these synthetic volumes can be used for accurate dose calculations, leading to better adaptation in treatment planning.

AffectiVR: A Database for Periocular Identification and Valence and Arousal Evaluation in Virtual Reality

This study introduces AffectiVR, a dataset designed for periocular biometric authentication and emotion evaluation in virtual reality (VR) environments. To maximize immersion in VR environments, interactions must be seamless and natural, with unobtrusive authentication and emotion recognition technologies playing a crucial role. This study proposes a method for user authentication by utilizing periocular images captured by a camera attached to a VR headset. Existing datasets have lacked periocular images acquired in VR environments, limiting their practical application. To address this, periocular images were collected from 100 participants using the HTC Vive Pro and Pupil Labs infrared cameras in a VR environment. Participants also watched seven emotion-inducing videos, and emotional evaluations for each video were conducted. The final dataset comprises 1988 monocular videos and corresponding self-assessment manikin (SAM) evaluations for each experimental video. This study also presents a baseline study to evaluate the performance of biometric authentication using the collected dataset. A deep learning model was used to analyze the performance of biometric authentication based on periocular data collected in a VR environment, confirming the potential for implicit and continuous authentication. The high-resolution periocular images collected in this study provide valuable data not only for user authentication but also for emotion evaluation research. The dataset developed in this study can be used to enhance user immersion in VR environments and as a foundational resource for advancing emotion recognition and authentication technologies in fields such as education, therapy, and entertainment. This dataset offers new research opportunities for non-invasive continuous authentication and emotion recognition in VR environments, and it is expected to significantly contribute to the future development of related technologies.

A Geometric Significance-Aware Deep Mutual Learning Network for Building Extraction from Aerial Images

Knowledge-driven building extraction method exhibits a restricted adaptability scope and is vulnerable to external factors that affect its extraction accuracy. On the other hand, data-driven building extraction method lacks interpretability, heavily relies on extensive training data, and may result in extraction outcomes with building boundary blur issues. The integration of pre-existing knowledge with data-driven learning is essential for the intelligent identification and extraction of buildings from high-resolution aerial images. To overcome the limitations of current deep learning building extraction networks in effectively leveraging prior knowledge of aerial images, a geometric significance-aware deep mutual learning network (GSDMLNet) is proposed. Firstly, the GeoSay algorithm is utilized to derive building geometric significance feature maps as prior knowledge and integrate them into the deep learning network to enhance the targeted extraction of building features. Secondly, a bi-directional guidance attention module (BGAM) is developed to facilitate deep mutual learning between the building feature map and the building geometric significance feature map within the dual-branch network. Furthermore, the deployment of an enhanced flow alignment module (FAM++) is utilized to produce high-resolution, robust semantic feature maps with strong interpretability. Ultimately, a multi-objective loss function is crafted to refine the network’s performance. Experimental results demonstrate that the GSDMLNet excels in building extraction tasks within densely populated and diverse urban areas, reducing misidentification of shadow-obscured regions and color-similar terrains lacking building structural features. This approach effectively ensures the precise acquisition of urban building information in aerial images.

PET Imaging Using 89Zr Labeled StarPEG Nanocarriers Reveals Heterogeneous Enhanced Permeability and Retention (EPR) in Prostate Cancer.

The enhanced permeability and retention (EPR) effect controls passive nanodrug uptake in tumors, and may provide a high tumor payload with prolonged retention for cancer treatment. However, EPR-mediated tumor uptake and distribution vary by cancer phenotype. Thus, we hypothesized that a companion PET-imaging surrogate may benefit EPR-mediated therapeutic drug delivery. We developed two 89Zr-radiolabeled nanocarriers based on 4-armed-starPEG40kDa with or without talazoparib (TLZ), a potent PARPi, as surrogates for the PEG-TLZ4 therapeutic scaffold. For PET imaging, PEG-DFB4 and PEG-DFB1-TLZ3 were radiolabeled with 89Zr by replacing one or all four TLZ on PEG-TLZ4 with deferoxamine B (DFB). The radiolabeled nanodrugs [89Zr]PEG-DFB4 and [89Zr]PEG-DFB1-TLZ3 were tested in vivo in prostate cancer subcutaneous xenografts (22Rv1, LTL-545, and LTL-610) and 22Rv1 metastatic models. Their EPR-mediated tumoral uptake and penetration was compared to CT26, a known EPR-high MicroPET/CT images, organ biodistribution, and calculated kinetic parameters showed high uptake in CT26 and LTL-545, moderate to low uptake in LTL-610 and 22Rv1. MicroPET/CT and high-resolution autoradiographic images showed nanocarrier penetration into highly permeable CT26, but heterogeneous peripheral accumulation was observed in LTL-545, LTL-610, and 22Rv1 subcutaneous xenografts and metastatic tumors. CD31 staining of tumor sections showed homogenous vascular development in CT26 tumors and heterogeneity in other xenografts. Both [89Zr]PEG-DFB4 and [89Zr]PEG-DFB1-TLZ3 showed similar accumulation and distribution in subcutaneous and metastatic tumor models. Both nanocarriers can measure tumor model passive uptake heterogeneity. Although heterogeneous, prostate cancer xenografts had low EPR. These starPEG nanocarriers could be used as PET imaging surrogates to predict drug delivery and efficacy.

Enhancing Brain Tumor Diagnosis with L-Net: A Novel Deep Learning Approach for MRI Image Segmentation and Classification.

Background: Brain tumors are highly complex, making their detection and classification a significant challenge in modern medical diagnostics. The accurate segmentation and classification of brain tumors from MRI images are crucial for effective treatment planning. This study aims to develop an advanced neural network architecture that addresses these challenges. Methods: We propose L-net, a novel architecture combining U-net for tumor boundary segmentation and a convolutional neural network (CNN) for tumor classification. These two units are coupled such a way that the CNN classifies the MRI images based on the features extracted by the U-net while segmenting the tumor, instead of relying on the original input images. The model is trained on a dataset of 3064 high-resolution MRI images, encompassing gliomas, meningiomas, and pituitary tumors, ensuring robust performance across different tumor types. Results: L-net achieved a classification accuracy of up to 99.6%, surpassing existing models in both segmentation and classification tasks. The model demonstrated effectiveness even with lower image resolutions, making it suitable for diverse clinical settings. Conclusions: The proposed L-net model provides an accurate and unified approach to brain tumor segmentation and classification. Its enhanced performance contributes to more reliable and precise diagnosis, supporting early detection and treatment in clinical applications.

A hot exciton organic glassy scintillator for high-resolution X-ray imaging.

Hot exciton organic scintillators offer promising prospects due to their efficient generation of bright triplet excitons and ultrafast response time, having potential applications in security detection and medical diagnostics. However, fabricating large-area, highly transparent scintillator screens still remains challenging, impeding the realization of high-resolution X-ray imaging. Herein, we firstly demonstrate a novel highly-transparent hot exciton organic glassy scintillator (>87% transmittance @ 450-800 nm), produced using a low-temperature melt-quenching method with 2',5'-difluoro-N4,N4,N4'',N4''-tetraphenyl-[1,1':4',1''-terphenyl]-4,4''-diamine (DTPA2F) powder. Remarkably, compared to crystalline DTPA2F, which has a photoluminescence quantum yield of 67.8% and a relative light yield of 46 400 ± 406 photons MeV-1, the DTPA2F glass retains 49.8% and 28 341 ± 246 photons MeV-1, respectively. This results in a low detection limit of about 53.7 nGy s-1 and an ultrafast decay time of 1.66 ns for DTPA2F glass. Besides, it exhibits excellent environmental stability with no recrystallization or degradation after over 100 days of exposure to ambient conditions. Furthermore, the scintillator screen demonstrates exceptional spatial resolution of 38.5 lp mm-1 for X-ray imaging. It provides a simple molecular design strategy and a screen fabrication method for developing large-area, highly-transparent, efficient and ultrafast organic glassy scintillators.

MRI Super-Resolution with Partial Diffusion Models.

Diffusion models have achieved impressive performance on various image generation tasks, including image super-resolution. Despite their impressive performance, diffusion models suffer from high computational costs due to the large number of denoising steps. In this paper, we proposed a novel accelerated diffusion model, termed Partial Diffusion Models (PDMs), for magnetic resonance imaging (MRI) super-resolution. We observed that the latents of diffusing a pair of low- and high-resolution images gradually converge and become indistinguishable after a certain noise level. This inspires us to use certain low-resolution latent to approximate corresponding high-resolution latent. With the approximation, we can skip part of the diffusion and denoising steps, reducing the computation in training and inference. To mitigate the approximation error, we further introduced 'latent alignment' that gradually interpolates and approaches the high-resolution latents from the low-resolution latents. Partial diffusion models, in conjunction with latent alignment, essentially establish a new trajectory where the latents, unlike those in original diffusion models, gradually transition from low-resolution to high-resolution images. Experiments on three MRI datasets demonstrate that partial diffusion models achieve competetive super-resolution quality with significantly fewer denoising steps than original diffusion models. In addition, they can be incorporated with recent accelerated diffusion models to further enhance the efficiency.

Feasibility of thin-slice, low noise images created using multi-kernel synthesis to replace multiple image series in head CT.

SynthesiZed Improved Resolution and Concurrent nOise reductioN (ZIRCON) is a multi-kernel synthesis method that creates a single series of thin-slice computed tomography (CT) images displaying low noise and high spatial resolution, increasing reader efficiency and minimizing partial volume averaging. To compare the diagnostic performance of a single set of ZIRCON images to two routine clinical image series using conventional CT head and bone reconstruction kernels for diagnosing intracranial findings and fractures in patients with trauma or suspected acute neurologic deficit. In total, 50 patients underwent clinically indicated head CT in the ER (15 normal, 35 abnormal cases). A non-reader neuroradiologist established the reference standard. Three neuroradiologists reviewed two routine clinical series (head and bone kernels) and a single ZIRCON series, detecting intracranial findings or fractures and rating confidence (0-100). Sensitivity, specificity, and jackknife free-response receiver operating characteristic (JAFROC) figure of merit (FOM) were compared (limit of non-inferiority: -0.10). ZIRCON and conventional images demonstrated comparable performance for fractures (sensitivity: 51.5% vs. 54.5%; specificity: 40.2% vs. 34.2%) and intracranial findings (sensitivity: 88.2% vs. 91.4%; specificity: 77.2% vs. 73.7%).The estimated difference of JAFROC FOM demonstrated ZIRCON non-inferiority for acute pathologies overall (0.003 [95% CI=-0.051-0.057]) and fractures (0.048 [95% CI=-0.050-0.145]) but not for intracranial findings alone (-0.024 [95% CI=-0.100-0.052]). Thin-slice, low noise, and high spatial resolution images can be created to display intracranial findings and fractures replacing multiple images series in head CT with similar performance. Future studies in more patients and further algorithmic development are warranted.

Chemical and Acoustical Mixed-Mapping of Geological Materials from Laser-Induced Plasmas: A Comprehensive Approach to Differentiate Mineral Phases.

The acoustic wave produced alongside laser-induced plasmas can be used in conjunction with the recorded atomic spectra of plasma emission to expand the physicochemical information acquired from a single inspection event. Among the most interesting uses of acoustic information is the differentiation of mineral phases with similar optical responses coexisting in geological targets. In addition, laser-induced plasma acoustics (LIPAc) can provide data related to the inspected material's hardness, density, and compactness. In this paper, we present a dual acoustic-optic laser-based strategy for the generation of high-resolution surface images of mineral samples. By combining simultaneous multimodal LIBS (laser-induced breakdown spectroscopy) and LIPAc spectral data from laser-induced plasmas, we explore the mineralogical composition of rocks embedded in resin matrixes to distinguish their chemical composition as well as their crystal phases based on physical changes caused by the different spatial arrangements of the constituent atoms. The multispectral polyhedron created by merging singular optical maps, one per detected elements, and the coincidental acoustic map enhance the distinction between regions present within the matrix of a host rock as compared to the differentiation yielded by each technique when used separately. The chemical information guides the composition of the mineral phases in the host rock. Then, the physical information obtained from acoustics may reinforce the identification of the detected mineral phase, draw the geological history of the inspected section, and showcase possible transformations, mainly of polymorphic nature. To test the combination proposed herein, we also inspected a septarian nodule featuring an ensemble of mineral phases with different origins. Mixed optical and acoustic responses from laser-produced plasmas of this complex sample allowed us to obtain more specific information. This approach constitutes a reliable and high-throughput tool for studying the surface of geological samples, which can substantially supplement well-established techniques for mineralogical analysis such as Raman spectroscopy and X-ray diffraction.

An optimization method for deformable mirror configuration in multi-conjugate adaptive optics systems

Multi-conjugate adaptive optics (MCAO) is a crucial technology for achieving high-resolution imaging over a wide field of view with modern ground-based optical telescopes. The configuration of deformable mirrors (DMs) is a key component in the analysis and optimization of MCAO performance. Currently, the search for the optimal DM configuration often relies on iterative and time-consuming Monte Carlo simulations. This issue arises from the lack of an appropriate optimization method for DM configurations. The primary objective of this paper is to establish an optimization method for DM configurations in MCAO systems. We established a quantitative criterion for evaluating DM configurations by analyzing their correction capabilities for turbulence aberrations at different altitudes. Then, we optimized the DM configurations based on this criterion. This method provides a new theoretical foundation and practical tool for the design and performance optimization of MCAO systems. Based on the pupil phase structure function, we established a DM configuration evaluation criterion, namely the non-conjugate correction index (NCCI). Using NCCI as the optimal criterion, combined with the particle swarm optimization algorithm, we searched for the optimal solution across different DM configuration spaces. We conducted simulations based on the turbulence profiles of typical telescope sites. We validated our proposed theoretical model against Monte Carlo simulation models and find that the NCCI error ranges from 0.05 to 0.1. For optimizing DM conjugate heights, the results of our optimization algorithm differ by less than 1 km from those obtained via Monte Carlo simulations. Regarding the performance of the DM optimization algorithm, the average convergence accuracy error is less than 0.1 km, and the average convergence speed is approximately ten iterations. Additionally, our optimization method runs in just a few minutes; Monte Carlo simulations, in comparison, require several dozen hours.

Development of an intraductal papillary mucinous neoplasm malignancy prediction scoring system.

Despite the presence of various guidelines, diagnosing malignant intraductal papillary mucinous neoplasm (IPMN) continues to pose challenges. Furthermore, although endoscopic ultrasonography (EUS) offers high-resolution images, it has not yet recognized as the primary tool for malignancy diagnosis. The study objective was to develop a simplified and user-friendly scoring system to improve the diagnostic accuracy of malignant IPMNs. Additionally, the utility of EUS and its effect on diagnostic accuracy were assessed. We retrospectively collected the clinical data on 160 cases of resected IPMN at Tokyo Medical and Dental University Hospital from January 2008 to December 2022. We examined clinical features, computed tomography (CT) and magnetic resonance imaging (MRI) findings, and EUS results if available. We then calculated the odds ratio of malignancy for these factors and developed an IPMN malignancy prediction (IMAP) scoring system. There were 89 (55.6%) cases of benign IPMNs and 71 (44.4%) of malignant IPMNs. Eight clinical and imaging findings, including age, diabetes mellitus status, jaundice, CA19-9 level, enhancing mural nodules ≥5mm, thickened wall, and main duct dilatation, were significantly associated with malignancy. The IMAP score was calculated by assigning 0 to 2 points to these factors based on the odds ratio. The area under the receiver operating characteristic curve for the IMAP score was 0.78 [95% confidence interval (CI): 0.71-0.85] based on CT/MRI alone and improved to 0.81 (95% CI: 0.74-0.87) when EUS was added. When the total exceeds 5 points, the positive predictive value becomes 100% (95% CI: 95.9-100). In conclusion, the IMAP scoring system has demonstrated promise as a clinically useful tool, offering both simplicity and sufficient accuracy. It holds potential as an important decision criterion for determining the treatment approach for IPMN. Additionally, EUS contributes to enhancing the diagnostic accuracy of the IMAP scoring system, thereby enabling more precise decision-making.

Unraveling Interdiffusion Phenomena and the Role of Nanoscale Diffusion Barriers in the Copper-Gold System.

Diffusion is one of the most fundamental concepts in materials science, playing a pivotal role in materials synthesis, forming, and degradation. Of particular importance is solid state interdiffusion of metals which defines the usable parameter space for material combinations in the form of alloys. This parameter space can be explored on the macroscopic scale by using diffusion couples. However, this method reaches its limit when going to low temperatures, small scales, and when testing ultrathin diffusion barriers. Therefore, this work transfers the principle of the diffusion couples to small scales by using core-shell nanowires and in situ heating. This allows us to delve into the interdiffusion dynamics of copper and gold, revealing the interplay between diffusion and the disorder-order phase transition. Our in situ TEM experiments in combination with chemical mapping reveal the interdiffusion coefficients of Cu and Au at low temperatures and highlight the impact of ordering processes on the diffusion behavior. The formation of ordered domains within the solid-solution is examined using high-resolution imaging and nanodiffraction including strain mapping. In addition, we examine the effectiveness of ultrathin Al2O3 barrier layers to control interdiffusion of the diffusion couple. Our findings indicate that a 5 nm thick layer serves as an efficient diffusion barrier. This research provides valuable insights into the interdiffusion behavior of Cu and Au on the nanoscale, offering potential applications in the development of miniaturized integrated circuits and nanodevices.

Noninvasive high-resolution deep-brain photoacoustic imaging with negatively-focused fiber-laser ultrasound transducer

Noninvasive high-resolution deep-brain photoacoustic imaging with negatively-focused fiber-laser ultrasound transducer

Current Applications of Raman Spectroscopy in Intraoperative Neurosurgery.

Neurosurgery demands exceptional precision due to the brain's complex and delicate structures, necessitating precise targeting of pathological targets. Achieving optimal outcomes depends on the surgeon's ability to accurately differentiate between healthy and pathological tissues during operations. Raman spectroscopy (RS) has emerged as a promising innovation, offering real-time, in vivo non-invasive biochemical tissue characterization. This literature review evaluates the current research on RS applications in intraoperative neurosurgery, emphasizing its potential to enhance surgical precision and patient outcomes. Following PRISMA guidelines, a comprehensive systematic review was conducted using PubMed to extract relevant peer-reviewed articles. The inclusion criteria focused on original research discussing real-time RS applications with human tissue samples in or near the operating room, excluding retrospective studies, reviews, non-human research, and other non-relevant publications. Our findings demonstrate that RS significantly improves tumor margin delineation, with handheld devices achieving high sensitivity and specificity. Stimulated Raman Histology (SRH) provides rapid, high-resolution tissue images comparable to traditional histopathology but with reduced time to diagnosis. Additionally, RS shows promise in identifying tumor types and grades, aiding precise surgical decision-making. RS techniques have been particularly beneficial in enhancing the accuracy of glioma surgeries, where distinguishing between tumor and healthy tissue is critical. By providing real-time molecular data, RS aids neurosurgeons in maximizing the extent of resection (EOR) while minimizing damage to normal brain tissue, potentially improving patient outcomes and reducing recurrence rates. This review underscores the transformative potential of RS in neurosurgery, advocating for continued innovation and research to fully realize its benefits. Despite its substantial potential, further research is needed to validate RS's clinical utility and cost-effectiveness.