Spatial Image Research Articles

Speckle-illumination spatial frequency domain imaging with a stereo laparoscope for profile-corrected optical property mapping.

Laparoscopic surgery presents challenges in localizing oncological margins due to poor contrast between healthy and malignant tissues. Optical properties can uniquely identify various tissue types and disease states with high sensitivity and specificity, making it a promising tool for surgical guidance. Although spatial frequency domain imaging (SFDI) effectively measures quantitative optical properties, its deployment in laparoscopy is challenging due to the constrained imaging environment. Thus, there is a need for compact structured illumination techniques to enable accurate, quantitative endogenous contrast in minimally invasive surgery. We introduce a compact, two-camera laparoscope that incorporates both active stereo depth estimation and speckle-illumination SFDI (si-SFDI) to map profile-corrected, pixel-level absorption ( ), and reduced scattering ( ) optical properties in images of tissues with complex geometries. We used a multimode fiber-coupled 639-nm laser illumination to generate high-contrast speckle patterns on the object. These patterns were imaged through a modified commercial stereo laparoscope for optical property estimation via si-SFDI. Compared with the original si-SFDI work, which required images of randomized speckle patterns for accurate optical property estimations, our approach approximates the DC response using a laser speckle reducer (LSR) and consequently requires only two images. In addition, we demonstrate 3D profilometry using active stereo from low-coherence RGB laser flood illumination. Sample topography was then used to correct for measured intensity variations caused by object height and surface angle differences with respect to a calibration phantom. The low-contrast RGB speckle pattern was blurred using an LSR to approximate incoherent white light illumination. We validated profile-corrected si-SFDI against conventional SFDI in phantoms with simple and complex geometries, as well as in a human finger in vivo time-series constriction study. Laparoscopic si-SFDI optical property measurements agreed with conventional SFDI measurements when measuring flat tissue phantoms, exhibiting an error of 6.4% for absorption and 5.8% for reduced scattering. Profile-correction improved the accuracy for measurements of phantoms with complex geometries, particularly for absorption, where it reduced the error by 23.7%. An in vivo finger constriction study further validated laparoscopic si-SFDI, demonstrating an error of 8.2% for absorption and 5.8% for reduced scattering compared with conventional SFDI. Moreover, the observed trends in optical properties due to physiological changes were consistent with previous studies. Our stereo-laparoscopic implementation of si-SFDI provides a simple method to obtain accurate optical property maps through a laparoscope for flat and complex geometries. This has the potential to provide quantitative endogenous contrast for minimally invasive surgical guidance.

A proof-of-concept study for precise mapping of pigmented basal cell carcinoma in asian skin using multispectral optoacoustic tomography imaging with level set segmentation.

Basal Cell Carcinoma (BCC), the most common subtype of non-melanoma skin cancers (NMSC), is prevalent worldwide and poses significant challenges due to their increasing incidence and complex treatment considerations. Existing clinical approaches, such as Mohs micrographic surgery, are time-consuming and labour-intensive, requiring meticulous layer-by-layer excision and examination, which can significantly extend the duration of the procedure. Current optical imaging solutions also lack the necessary spatial resolution, penetration depth, and contrast for effective clinical use. Here, we introduce photoacoustic imaging, also known as optoacoustic imaging, based Multispectral Optoacoustic Tomography (MSOT) as a promising solution for non-invasive, high-resolution imaging in dermatology, which also measures hemodynamic changes. MSOT offers high isotropic resolution (80μm), increased tissue penetration, and contrast-enhanced 3D spatial imaging map. For the first time, we integrated an automated level set image segmentation methodology on optoacoustic images to further enhance the precision in delineating tumor boundaries. Through this proof-of-concept study in 30 subjects, we demonstrate that this segmentation allows for precise measurement of tumor width, depth, and volume, aiding in preoperative tumor mapping and surgical planning. The MSOT measurements, validated against histology, achieved a correlation coefficient of 0.84 and 0.81 for width and depth respectively, ensuring reliable tumor metrics with a low margin of error. Clinicians can use these tumor metrics to optimize treatment efficacy, while preserving healthy tissue and cosmetic outcomes. This advancement has the potential to revolutionize diagnostics and treatment, significantly improving the patient outcomes in managing NMSC.

Assessing spatial sequencing and imaging approaches to capture the molecular and pathological heterogeneity of archived cancer tissues.

Spatial transcriptomics (ST) offers enormous potential to decipher the biological and pathological heterogeneity in precious archival cancer tissues. Traditionally, these tissues have rarely been used and only examined at a low throughput, most commonly by histopathological staining. ST adds thousands of times as many molecular features to histopathological images, but critical technical issues and limitations require more assessment of how ST performs on fixed archival tissues. In this work, we addressed this in a cancer-heterogeneity pipeline, starting with an exploration of the whole transcriptome by two sequencing-based ST protocols capable of measuring coding and non-coding RNAs. We optimised the two protocols to work with challenging formalin-fixed paraffin-embedded (FFPE) tissues, derived from skin. We then assessed alternative imaging methods, including multiplex RNAScope single-molecule imaging and multiplex protein imaging (CODEX). We evaluated the methods' performance for tissues stored from 4 to 14 years ago, covering a range of RNA qualities, allowing us to assess variation. In addition to technical performance metrics, we determined the ability of these methods to quantify tumour heterogeneity. We integrated gene expression profiles with pathological information, charting a new molecular landscape on the pathologically defined tissue regions. Together, this work provides important and comprehensive experimental technical perspectives to consider the applications of ST in deciphering the cancer heterogeneity in archived tissues. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Patial-frequency aware zero-centric residual unfolding network for MRI reconstruction.

Magnetic Resonance Imaging is a cornerstone of medical diagnostics, providing high-quality soft tissue contrast through non-invasive methods. However, MRI technology faces critical limitations in imaging speed and resolution. Prolonged scan times not only increase patient discomfort but also contribute to motion artifacts, further compromising image quality. Compressed Sensing (CS) theory has enabled the acquisition of partial k-space data, which can then be effectively reconstructed to recover the original image using advanced reconstruction algorithms. Recently, deep learning has been widely applied to MRI reconstruction, aiming to reduce the artifacts in the image domain caused by undersampling in k-space and enhance image quality. As deep learning continues to evolve, the undersampling factors in k-space have gradually increased in recent years. However, these layers are limited in compensating for reconstruction errors in the unsampled areas, impeding further performance improvements. To address this, we propose a learnable spatial-frequency difference-aware module that complements the learnable data consistency layer, mapping k-space domain differences to the spatial image domain for perceptual compensation. Additionally, inspired by wavelet decomposition, we introduce explicit priors by decomposing images into mean and residual components, enforcing a refined zero-mean constraint on the residuals while maintaining computational efficiency. Comparative experiments on the FastMRI and Calgary-Campinas datasets demonstrate that our method achieves superior reconstruction performance against seven state-of-the-art techniques. Ablation studies further confirm the efficacy of our model's architecture, establishing a new pathway for enhanced MRI reconstruction.

Spatial Filter Pinhole Image Acquisition Based on Beam Self-fold Back in Multi-pass Amplifier Laser System

Spatial Filter Pinhole Image Acquisition Based on Beam Self-fold Back in Multi-pass Amplifier Laser System

Quantum magnetometry of transient signals with a time resolution of 1.1 nanoseconds

Quantum magnetometers based on spin defects in solids enable sensitive imaging of various magnetic phenomena, such as ferro- and antiferromagnetism, superconductivity, and current-induced fields. Existing protocols primarily focus on static fields or narrow-band dynamical signals, and are optimized for high sensitivity rather than fast time resolution. Here, we report detection of fast signal transients, providing a perspective for investigating the rich dynamics of magnetic systems. We experimentally demonstrate our technique using a single nitrogen-vacancy (NV) center magnetometer at room temperature, reaching a best-effort time resolution of 1.1 ns, an instantaneous bandwidth of 0.9 GHz, and a time-of-flight precision better than 20 ps. The time resolution can be extended to the picosecond range by use of on-chip waveguides. At these speeds, NV quantum magnetometers will become competitive with time-resolved synchrotron X-ray techniques. Looking forward, adding fast temporal resolution to the spatial imaging capability further promotes single-spin probes as powerful research tools in spintronics, mesoscopic physics, and nanoscale device metrology.

High spatial resolution spectral imaging based on amplitude-phase joint modulation metasurfaces using global optimization algorithm

High spatial resolution spectral imaging based on amplitude-phase joint modulation metasurfaces using global optimization algorithm

Simultaneous Luminal and Hemodynamic Evaluation of the Cervical Arteries Using Nonenhanced 3D Quantitative Quiescent-Interval Slice-Selective Magnetic Resonance Angiography.

Luminal and hemodynamic evaluations of the cervical arteries inform the diagnosis and management of patients with cervical arterial disease. To demonstrate a 3D nonenhanced quantitative quiescent interval slice-selective (qQISS) magnetic resonance angiographic (MRA) strategy that provides simultaneous hemodynamic and luminal evaluation of the cervical arteries. Prospective. Six healthy volunteers (3 female, 3 male, age = 35.7 ± 10.3 years) and 14 patients with cerebrovascular disease (12 female, 2 male, age = 56.6 ± 14.0 years). 3 T, ungated 3D tilted-slab qQISS, pulse-gated 2D phase contrast (PC), ungated 3D PC, and 3D time-of-flight (TOF)gradient-echo protocols. Four readers scored 29 arterial segments on 3D qQISS volumes for image quality using a 4-point scale (1: non-diagnostic, 2: fair, 3: good, 4: excellent). Time-averaged arterial flow velocities and volume flow rates obtained with qQISS and PC protocols were compared. Arterial lumen area and radius measures obtained with 3D protocols were compared in a subgroup. Gwet's AC2; intraclass correlation coefficient (ICC); Pearson's correlation; Bland-Altman. P values <0.05 were considered statistically significant. 3D qQISS provided good-to-excellent image quality for depicting the cervical arteries (mean scores of 3.72 ± 0.55, 3.55 ± 0.66, 3.42 ± 0.72, and 3.66 ± 0.73 for readers 1, 2, 3, and 4) with significant inter-reader agreement (AC2 = 0.91, ICC = 0.53) in image scoring, significantly agreed with pulse-gated 2D PC for time-averaged total flow velocity (ICC = 0.83) and volume flow rate (ICC = 0.92), and significantly agreed with 3D PC for total flow velocity (ICC = 0.70), volume flow rate (ICC = 0.91), and component flow velocity (ICC = 0.89). Compared with 3D PC, 3D qQISS better agreed with 3D TOF for arterial lumen area (ICC = 0.97 vs. 0.72) and radius (ICC = 0.94 vs. 0.74). Nonenhanced 3D qQISS provides high-quality sub-1 mm3 spatial resolution imaging of the cervical arteries, excellent agreement of arterial structural measures with respect to 3D TOF, and time-averaged hemodynamic data without the need for additional PC imaging. Magnetic resonance angiography (MRA), a method for depicting blood vessels within the body, can be used to evaluate arterial diseases and disorders of the neck. MRA methods routinely used to evaluate the neck arteries do not measure blood flow speed and volume, while other methods for obtaining this information provide less accurate pictures of arterial structure and are not routinely collected. This article reports a new method for MRA that clearly and efficiently portrays the neck arteries without using injected dyes, and provides measurements of arterial blood flow speed and volume. 2 TECHNICAL EFFICACY: Stage 1.

Assessing the feasibility of mapping changes of ecosystem functional groups in South African estuaries using Landsat and Sentinel images of 1990, 2014, 2018 and 2020

This study evaluates the feasibility of using medium-resolution satellite sensors to monitor changes in the extent of ecosystem functional groups (EFGs) in South African estuaries, for reporting on the 2030 targets of the Global Biodiversity Framework (GBF). Landsat and Sentinel-1 and -2 image collections in Google Earth Engine (GEE) were used to generate output layers for each of the national land cover years—1990, 2014, 2018 and 2020. Image composites of each year’s two growth seasons and one dry season, vegetation indices and topographic data were generated. Changes in the extent and accuracies of three estuarine (mangroves, salt marshes and submerged macrophytes) and three freshwater (forested wetlands, freshwater marshes and large macrophytes) EFGs were calculated and compared to a manually mapped through image interpretation, high-confidence layer. Overall, estuarine EFGs comprised between 10 and 18% of the extent of the EFGs, while freshwater EFGs made up 15% of the extent of estuaries. The overall accuracies of detection of EFGs for 1990 were < 64% compared to the > 71% attained for 2014, 2018 and 2020. In comparison to manual delineations of some of these habitats, the outputs generated from these medium-resolution sensors resulted in overestimation of extent for all EFGs; for mangroves by 115% and for salt marshes and submerged macrophytes by 150–230%. Finer spatial resolution images, and time-series mapping would be critical for improved delineation and monitoring of South Africa’s estuarine habitats.

Sialic acid aptamer and RNA in situ hybridization-mediated proximity ligation assay for spatial imaging of glycoRNAs in single cells.

Glycosylated RNAs (glycoRNAs) have recently emerged as a new class of molecules of substantial interest owing to their potential roles in cellular processes and diseases. However, studying glycoRNAs is challenging owing to the lack of effective research tools including, but not limited to, imaging techniques to study the spatial distribution of glycoRNAs. Recently, we reported the development of a glycoRNA imaging technique, called sialic acid aptamer and RNA in situ hybridization-mediated proximity ligation assay (ARPLA), to visualize sialic acid-containing glycoRNAs with high sensitivity and specificity. Here we describe the experimental design principles and detailed step-by-step procedures for ARPLA-assisted glycoRNA imaging across multiple cell types. The procedure includes details for target selection, oligo design and preparation, optimized steps for RNA in situ hybridization, glycan recognition, proximity ligation, rolling circle amplification and a guideline for image acquisition and analysis. With properly designed probe sets and cells prepared, ARPLA-based glycoRNA imaging can typically be completed within 1 d by users with expertise in biochemistry and fluorescence microscopy. The ARPLA approach enables researchers to explore the spatial distribution, trafficking and functional contributions of glycoRNAs in various cellular processes.

Scanning dual-comb spectroscopic microscopy for multidimensional optical information acquisition.

We developed a scanning dual-comb spectroscopic microscopy (S-DCSM) system to acquire multidimensional optical information of transparent or semi-transparent samples. The system demonstrated the capability to perform spectral imaging of absorbance, optical phase, optical thickness, linear dichroism, and birefringence within the spectral range covered by optical frequency combs (OFCs). The spatial distribution of optical thickness in HeLa cells was measured as 8.19 ± 0.34 μm, aligning with known cellular dimensions. Additionally, the S-DCSM system was applied for the imaging of polarization-dependent optical properties, such as linear dichroism and birefringence in cellophane tape, whose spatial distributions were determined as 0.00464 ± 0.00043 and -0.014 ± 0.072, respectively, at 1560.25 nm. These results demonstrate the system's versatility in combining spatial imaging with detailed spectral and polarization information, providing a powerful tool for advanced optical characterization. This study marks a significant step forward in multidimensional optical information acquisition, facilitating detailed quantitative analysis in materials science and biological studies.

Enhancing the Diagnostic Accuracy of Amyloid PET: The Impact of MR-Guided PET Reconstruction.

18 F-Florbetaben (FBB) uptake in the supratentorial cortex is indicative of amyloid positivity. Due to PET's low spatial resolution, image noise, and spill-over of signal from adjacent white-matter into gray-matter, there are inconsistencies in ratings among trained readers. A set of 264 18 F-Florbetaben (amyloid) PET/MRI exams were reconstructed using conventional ordered subset expectation maximization (OSEM) method and MR-guided block sequential regularized expectation maximization (MRgBSREM) method. Images from 264 patients reconstructed by OSEM method and rated by 3 trained readers. Fifty-three exams were rated inconsistently and were mixed with another 53 exams which were rated consistently. These 106 subjects were then rated by our readers using the MRgBSREM PET reconstruction method. Centiloids (CL) were measured using both reconstruction methods. Signal to noise ratio (SNR) was calculated in frontal, anterior/posterior cingulate, lateral parietal, and lateral temporal regions for both reconstruction methods. There is significant correlation between CL measured by OSEM and MRgBSREM methods with R2=0.99. MRgBSREM enhanced the SNR in all regions by average of 21%. The number of inconsistent exams dropped by 64% using MRgBSREM method as compared with OSEM method. Using Fleiss-Kappa statistical test, the agreement between readers was raised from "Fair" to "Significant" in the 106-subjects subset. PET reconstruction with MR priors can significantly improve the consistency of ratings among trained readers. Given the prevalence of inconsistent ratings in amyloid PET, methods that enhance the ability to distinguish intermediate amyloid levels could be valuable for the widespread adoption of this modality.

RGBE-Gaze: A Large-scale Event-based Multimodal Dataset for High Frequency Remote Gaze Tracking.

High-frequency gaze tracking demonstrates significant potential in various critical applications, such as foveated rendering, gaze-based identity verification, and the diagnosis of mental disorders. However, existing eye-tracking systems based on CCD/CMOS cameras either provide tracking frequencies below 200 Hz or employ high-speed cameras, leading to substantial power consumption and increased device volume. While there have been some high-speed eye-tracking datasets and methods based on event cameras, they are primarily tailored for near-eye camera scenarios. They lack the advantages associated with remote camera scenarios, such as the absence of the need for direct contact, enhanced user comfort and convenience, and the freedom of head pose in natural environments. In this work, we present RGBE-Gaze, the first large-scale and multimodal dataset for remote gaze tracking in high-frequency through synchronizing RGB and event cameras. This dataset is collected from 66 participants with diverse genders and age groups. The custom hybrid RGB-Event camera setup is leveraged to collect 3.6 million full-face high spatial resolution RGB images and 26.3 billion high temporal resolution event samples. Additionally, the dataset includes 10.7 million gaze references from the Gazepoint GP3 HD eye tracker and 15,972 sparse points of gaze (PoG) ground truth obtained through manual stimuli clicks by participants. We also present the dataset distribution across characteristics such as head pose and distance, gaze direction, pupil size, and tracking frequency. Furthermore, we introduce a hybrid frame-event based gaze estimation method specifically designed for the collected dataset. Moreover, we perform extensive evaluations of this method along with existing frame-based gaze estimation methods under various gaze-related factors, including different subjects, gaze directions, head poses, head distances, and pupil diameters. The evaluation results illustrate that introducing event stream as a new modality into the dataset improves gaze tracking frequency and demonstrates greater estimation robustness across diverse gaze-related factors.

Enhancing Cybersecurity: Detecting Hidden Information in Spatial Domain Images Using Convolutional Neural Networks

Enhancing Cybersecurity: Detecting Hidden Information in Spatial Domain Images Using Convolutional Neural Networks

Exploring the public space typology of tourist villages from the perspective of digital social media to build sustainable tourism

Abstract The image of the tourist village space plays an essential role in attracting tourists to visit. Currently, various digital social media platforms can shape the perception of the spatial image of a tourist village to invite tourists to visit the village. This study aims to analyze the public space typology of a tourist village from the perspective of digital social media. The research methodology used is a qualitative approach. Data was collected by web scraping on Instagram social media and continued with content analysis to identify the image of the tourist village public space. The results of this study indicate that the category of public space with external type and internal “quasi” public space is the type of public space that often appears in the Instagram content of tourist villages. These findings are expected to provide a better understanding of how the image of tourist village spaces from the perspective of digital social media so that it can be an input for planners and designers of sustainable tourist village spaces.

Dendritic cells type 1 control the formation, maintenance, and function of tertiary lymphoid structures in cancer.

Tertiary lymphoid structures (TLS) are organized immune cell aggregates that arise in chronic inflammatory conditions. In cancer, TLS are associated with better prognosis and enhanced response to immunotherapy, making these structures attractive therapeutic targets. However, the mechanisms regulating TLS formation and maintenance in cancer are incompletely understood. Using spatial transcriptomics and multiplex imaging across various human tumors, we found an enrichment of mature dendritic cells (DC) expressing high levels of CCR7 in TLS, prompting us to investigate the role of DC in the formation and maintenance of TLS in solid tumors. To address this, we developed a novel murine model of non-small cell lung cancer (NSCLC) that forms mature TLS, containing B cell follicles with germinal centers and T cell zones with T follicular helper cells (TFH) and TCF1+PD-1+ progenitor exhausted CD8+ T cells (Tpex). Here we show that, during the early stages of tumor development, TLS formation relies on IFNγ-driven maturation of the conventional DC type 1 (cDC1) subset, their migration to tumor-draining lymph nodes (tdLN), and recruitment of activated T cells to the tumor site. As tumors progress, TLS maintenance becomes independent of T cell egress from tdLN, coinciding with a significant reduction of cDC1 migration to tdLN. Instead, mature cDC1 accumulate within intratumoral CCR7 ligand-enriched stromal hubs. Notably, timed depletion of cDC1 or disruption of their migration to these stromal hubs after TLS are formed alters TLS maintenance. Importantly, we found that cDC1-mediated antigen presentation to both CD4+ and CD8+ T cells and intact CD40 signaling, is critical for the maintenance of TLS, the preservation of the TFH cell pool, the formation of germinal center and the production of tumor-specific IgG antibodies. These findings underscore the key role of mature cDC1 in establishing and maintaining functional TLS within tumor lesions and highlight the potential for cDC1-targeting therapies as a promising strategy to enhance TLS function and improve anti-tumor immunity in patients with cancer.

Optimized MALDI2-Mass Spectrometry Imaging for Stable Isotope Tracing of Tissue-Specific Metabolic Pathways in Mice.

Spatial stable isotope tracing metabolic imaging is a cutting-edge technique designed to investigate tissue-specific metabolic functions and heterogeneity. Traditional matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) techniques often struggle with low coverage of low-molecular-weight (LMW) metabolites, which are often crucial for spatial metabolic studies. To address this, we developed a high-coverage spatial isotope tracing metabolic method that incorporates optimized matrix selection, sample preparation protocols, and enhanced post-ionization (MALDI2) techniques. We employed this approach to mouse kidney, brain, and breast tumors to visualize the spatial dynamics of metabolic flow. Our results revealed diverse regional distributions of nine labeled intermediates derived from 13C6-glucose across glycolysis, glycogen metabolism, and the tricarboxylic acid (TCA) cycle in kidney tissues. In brain sections, we successfully mapped six intermediates from the TCA cycle and glutamate-glutamine (Glu-Gln) cycle simultaneously in distinct neurological regions. Furthermore, in breast cancer tumor tissues, our approach facilitated the mapping of nine metabolic intermediates in multiple pathways, including glycolysis, the pentose phosphate pathway (PPP), and the TCA cycle, illustrating metabolic heterogeneity within the tumor microenvironment. This methodology enhances metabolite coverage, enabling more comprehensive imaging of isotope-labeled metabolites and opening new avenues for exploring the metabolic landscape in various biological contexts.

UAV-Multispectral Based Maize Lodging Stress Assessment with Machine and Deep Learning Methods

Maize lodging is a prevalent stress that can significantly diminish corn yield and quality. Unmanned aerial vehicles (UAVs) remote sensing is a practical means to quickly obtain lodging information at field scale, such as area, severity, and distribution. However, existing studies primarily use machine learning (ML) methods to qualitatively analyze maize lodging (lodging and non-lodging) or estimate the maize lodging percentage, while there is less research using deep learning (DL) to quantitatively estimate maize lodging parameters (type, severity, and direction). This study aims to introduce advanced DL algorithms into the maize lodging classification task using UAV-multispectral images and investigate the advantages of DL compared with traditional ML methods. This study collected a UAV-multispectral dataset containing non-lodging maize and lodging maize with different lodging types, severities, and directions. Additionally, 22 vegetation indices (VIs) were extracted from multispectral data, followed by spatial aggregation and image cropping. Five ML classifiers and three DL models were trained to classify the maize lodging parameters. Finally, we compared the performance of ML and DL models in evaluating maize lodging parameters. The results indicate that the Random Forest (RF) model outperforms the other four ML algorithms, achieving an overall accuracy (OA) of 89.29% and a Kappa coefficient of 0.8852. However, the maize lodging classification performance of DL models is significantly better than that of ML methods. Specifically, Swin-T performs better than ResNet-50 and ConvNeXt-T, with an OA reaching 96.02% and a Kappa coefficient of 0.9574. This can be attributed to the fact that Swin-T can more effectively extract detailed information that accurately characterizes maize lodging traits from UAV-multispectral data. This study demonstrates that combining DL with UAV-multispectral data enables a more comprehensive understanding of maize lodging type, severity, and direction, which is essential for post-disaster rescue operations and agricultural insurance claims.

Noninvasive therapeutic ultrasound to increase perfusion in chronic limb-threatening ischemia: An early feasibility study.

Preclinical studies have demonstrated that therapeutic ultrasound (TUS) increases perfusion in peripheral artery disease (PAD). This pilot study assessed the safety and effectiveness of a noninvasive TUS device in patients with advanced PAD. A phased array of TUS transducers was fabricated on a wearable sleeve, designed to sonicate the posterior and anterior tibial arteries (and their collaterals) at the calf level. Twelve patients with PAD (Rutherford classes 3-5) were enrolled in a single-arm study in which they underwent 30-40 daily 90-minute TUS sessions to the diseased limb. Changes in pedal flow and tissue oxygenation (StO2) were measured by laser speckle and spatial frequency domain imaging, respectively. A subset of five patients underwent evaluation by laser Doppler, transcutaneous oximetry (TcPO2), and quality of life questionnaires (Vascular Quality of Life Questionnaire [VascuQoL] and the Walking Impairment Questionnaire [WIQ]). Eleven out of 12 enrolled patients completed the study. During 90-minute TUS sessions pedal flow improved by 180% (p < 0.001) on laser speckle imaging, and 18% (p = 0.12) on laser Doppler. Tissue oxygenation improved by 18% (p = 0.43) on TcPO2 and by 6% (p = 0.097) on StO2. After all sessions, tissue oxygenation improved by 17% (p = 0.020) on StO2, without significant changes in laser Doppler (+39%, p = 0.41) or TcPO2 (-3%, p = 0.70), which was largely in the normal range (56 ± 15 mmHg) at baseline. VascuQoL improved by 2.4 points (14%, p = 0.080) and WIQ improved by 8.2 points (11%, p = 0.053). TUS for patients with symptomatic PAD was safe and well tolerated. Most metrics of tissue perfusion and oxygenation improved, but future sham-controlled studies are needed and planned.

Determination of Bioactive Components in Chrysanthemum Tea (Gongju) Using Hyperspectral Imaging Technique and Chemometrics.

The bioactive components of chrysanthemum tea are an essential indicator in evaluating its nutritive and commercial values. Combining hyperspectral imaging (HSI) with key wavelength selection and pattern recognition methods, this study developed a novel approach to estimating the content of bioactive components in chrysanthemums, including the total flavonoids (TFs) and chlorogenic acids (TCAs). To determine the informative wavelengths of hyperspectral images, we introduced a variable similarity regularization term into particle swarm optimization (denoted as VSPSO), which can focus on improving the combinatorial performance of key wavelengths and filtering out the features with higher collinearity simultaneously. Moreover, considering the underlying relevance of the phytochemical content and the exterior morphology characteristics, the spatial image features were also extracted. Finally, an ensemble learning model, LightGBM, was established to estimate the TF and TCA contents using the fused features. Experimental results indicated that the proposed VSPSO achieved a superior accuracy, with R2 scores of 0.9280 and 0.8882 for TF and TCA prediction. Furthermore, after the involvement of spatial image information, the fused spectral-spatial features achieved the optimal model accuracy on LightGBM. The R2 scores reached 0.9541 and 0.9137, increasing by 0.0308-0.1404 and 0.0181-0.1066 in comparison with classical wavelength-related methods and models. Overall, our research provides a novel method for estimating the bioactive components in chrysanthemum tea accurately and efficiently. These discoveries revealed the potential effectiveness for constructing feature fusion in HSI-based practical applications, such as nutritive value evaluation and heavy metal pollution detection, which will also facilitate the development of quality detection in the food industry.