Cholesterol is an essential component of eukaryotic cell membranes, influencing membrane packing, fluidity, and domain formation. Replicating these properties in model membranes is critical for reconstitution studies, but common emulsion-based methods for producing giant unilamellar vesicles (GUVs) fail to incorporate cholesterol efficiently. Here, we use methyl-β-cyclodextrin-cholesterol (MβCD-CL) complexes to deliver cholesterol into GUVs produced by the emulsion droplet interface crossing encapsulation (eDICE) method and demonstrate a convenient way to quantify the degree of cholesterol incorporation using fluorescent membrane biosensors. Spectral imaging of NR12A as well as fluorescence lifetime imaging of Flipper-TR revealed dose-dependent increases in cholesterol content for DOPC GUVs upon MβCD-CL addition, consistent with increased membrane order. By calibrating these effects against GUVs with defined cholesterol contents prepared via gel-assisted swelling, we found that the cholesterol content of eDICE vesicles can be increased to at least 40 mol%. Binary mixtures of DOPC with saturated lipids (DMPC and PC (18 : 0-14 : 0)) showed a similar trend as pure DOPC GUVs. Interestingly, we could trigger liquid-ordered domain formation by adding cholesterol to DOPC : DMPC vesicles. Our findings provide a quantitative and non-disruptive method to modulate and assess cholesterol content in emulsion-based GUVs, advancing their use in bottom-up synthetic biology and membrane biophysics.

Low-rank tensor recovery methods within the tensor singular value decomposition (t-SVD) framework have demonstrated considerable success by leveraging the inherent low-dimensional structures of multi-dimensional data. However, previous approaches in this framework often rely on linear transforms or, in some cases, nonlinear transforms constructed with fully connected networks (FCNs). These methods typically promote a global low-rank structure, which may not fully exploit the nature of multiple subspaces in real-world data. In this work, we propose a nonlinear transform to capture long-range dependencies and diverse patterns across multiple subspaces of the data within the t-SVD framework. This approach provides a richer and more nuanced representation compared to the localized processing typically seen in FCN-based transforms. In the transform domain, we construct a low-rank self-representation layer that fully exploits the multi-subspace structure inherent in tensor data. Instead of merely enforcing overall low-rankness, our method minimizes the nuclear norm of a self-representation tensor, allowing for a more precise and joint characterization of multiple subspaces. This results in a more accurate representation of the data's intrinsic low-dimensional structures, leading to superior recovery performance. This new framework, termed the DEep Low-rank Tensor representAtion (DELTA), is evaluated across several typical multi-dimensional data recovery applications, including tensor completion, robust tensor completion, and spectral snapshot imaging. Experiments on various real-world multi-dimensional data illustrate the superior performance of our DELTA.

An interpretable cascaded residual iterative network for sparse-view spectral CT imaging

Spatial and spectral mapping of traffic-related nanoparticles in hippocampal subregions of an Alzheimer disease model.

Multimodal online monitoring of laser melt pool based on polarized infrared spectral imaging

Background: There is a need for intraoperative image guidance in gynecologic oncologic surgery to provide accurate identification of malignant tissue and ensure negative resection margins. Emerging imaging technologies can complement standard histopathology and reshape intraoperative decision-making. Spectral imaging can extract information on tissue composition and physiological status in real time, without the need for tissue contact, contrast agents, staining, or freezing. This systematic review synthesizes its current clinical applications in gynecologic oncology, decision support utility, and diagnostic performance with data processing frameworks for tissue classification. Materials and Methods: This systematic review (PROSPERO: CRD420251032899) adhered to PRISMA guidelines. PubMed, Google Scholar, Embase, ClinicalTrials.gov, and Scopus databases were searched until September 2025. Manuscripts reporting data on spectral imaging in gynecologic oncology were included in the analysis. Results: Twenty-nine studies and two clinical trials met the inclusion criteria. Most of them focused on cervical neoplasia (n = 17, 58.6%) and ovarian cancer (n = 7, 24.1%) detection, followed by assessment of the fallopian tubes (n = 2, 6.9%), endometrium (n = 1, 3.4%), and vulvar skin (n = 2, 6.9%). Using final pathology as the gold standard, overall specificity ranged from 30 to 99%, and overall sensitivity from 75 to 100%, with particularly high sensitivity for cervical lesions (79-100%) and ovarian cancer (81-100%). Among the included studies, thirteen (44.8%) used data interpretation algorithms, of which eleven (84.6%) applied machine learning, one (7.7%) deep learning, and one (7.7%) combined both. Conclusions: Spectral imaging, supported by computational methods, has shown promising results in the diagnostic evaluation of gynecologic disease by providing functional and molecular information beyond the capacities of standard visual assessment.

The direct visualization of the gastrointestinal tract is of vital significance for the examination of inflammatory bowel disease (IBD). However, present iodine- and barium-based contrast agents exert unsatisfactory imaging effects in the gastrointestinal tract and are not specific for inflammation. Herein, we designed and synthesized hyaluronic acid-barium tungstate nanoparticles (HA-BaWO4 NPs) for spectral CT imaging of IBD. HA-BaWO4 NPs displayed good in vitro imaging ability, low cytotoxicity, and high in vivo biocompatibility and were excreted from mice 24 h post-administration. Moreover, HA-BaWO4 NPs showed superior in vivo spectral CT imaging than the clinical contrast agent iohexol in the range of 40-160 keV, enabling clear visualization of gastrointestinal transit with stable signals at different voltage settings. More importantly, HA-BaWO4 NPs specifically accumulated in the inflamed region of the large intestine in DSS-induced colitis mice 24 h after administration, as confirmed by H&E staining showing severe inflammatory lesions. HA-BaWO4 NPs effectively differentiated colitis from the surrounding tissues, indicating their great potential for improving the sensitivity and accuracy of IBD diagnosis. We hope that the developed HA-BaWO4 NPs could serve as a promising nanoplatform for high-performance spectral CT imaging of IBD.

Vibrational spectroscopy is a powerful tool for spectral imaging of biological samples, thanks to its narrow bandwidth (10 cm⁻¹) compared to fluorescence. Single-molecule vibrational spectroscopy has been demonstrated with near-field amplification as in surface-enhanced Raman spectroscopy or fluorescence detection as in stimulated Raman excited fluorescence and bond-selective fluorescence-detected infrared-excited spectro-microscopy. However, these methods often require elaborate sample preparation or sometimes generate background signals when unintended processes lead to fluorescence emission. In response to these issues, we developed electronic resonance stimulated Raman scattering (ER-SRS) to achieve single-molecule sensitivity in far-field vibrational microscopy without relying on fluorescence detection. ER-SRS has encountered difficulties due to large electronic backgrounds. To overcome this, we employed Raman-amplified nonfluorescent molecular probe (RANMP) alongside our synchronously pumped, independently tunable double optical parametric oscillators for effective optimization of the signal-to-background ratio. The optimization of probe and light source allowed us to successfully detect ER-SRS signal from single particles in solution and from single molecules embedded in polymer matrix. ER-SRS combined with RANMP provides single-molecule sensitivity without fluorescence detection, enabling applications in biological and chemical imaging, particularly in multiplexed imaging.

Surgical adhesives are widely used in clinical practice but pose a significant risk of severe vascular embolism complications. Nevertheless, there are currently no non-invasive direct methods for precise detection of detached emboli. Herein, we show a CT-visualized method for hypersensitive detection of single millimeter vascular emboli from adhesive in vivo by simply doping BiOCl into surgical adhesives. As proof of concept, BiOCl-BioGlue with excellent CT imaging capability is fabricated and applied to repair ruptured vessels and liver in male rats. The location, morphology, and degradation process of BiOCl-BioGlue can be dynamically monitored by CT imaging for 42 days, and pulmonary emboli caused by BiOCl-BioGlue, with sizes as small as 1.2 mm, are successfully detected. Additionally, the high K-edge of Bi enables precise detection of pulmonary emboli in spectral CT imaging, unaffected by confounding calcifications. The proposed non-invasive detection strategy for adhesive emboli significantly enhances the biosafety of surgical adhesives.

Colorectal cancer (CRC) is the third most common malignancy worldwide, and early detection is vital to prevent metastasis and postoperative recurrence. This review summarizes current applications of spectral computed tomography (CT) in CRC, including its principles, spectral parameters used for evaluating primary and metastatic lesions, and key findings from recent literature. A systematic search of PubMed, Web of Science, and Google Scholar identified English-language studies published between April 2018 and April 2025 using the keywords: "spectral CT," "spectral imaging," "dual-layer spectral CT," "dual-energy spectral CT," "colorectal cancer," and "colon cancer." Spectral CT has shown promise in improving CRC detection and T staging accuracy, increasing sensitivity for lesion characterization, and aiding prognostic assessment after chemotherapy using baseline spectral parameters. Early evidence suggests it may also help predict lymph node metastasis and identify patients at risk of early postoperative metastases or surgical complications. Spectral parameters have been correlated with KRAS mutation, Ki-67 index, microsatellite instability, lymphovascular, perineural, and extramural vascular invasion, as well as microvessel density. However, most studies remain small and observational, highlighting the need for validation in larger, multicenter cohorts. Standardization and the time-intensive nature of image segmentation currently limit widespread adoption. Nevertheless, spectral CT is expected to play an increasing role in CRC evaluation by providing quantitative, predictive imaging biomarkers. Integration with artificial intelligence, particularly deep learning and automated segmentation, will likely expand both research and clinical applications. CRITICAL RELEVANCE STATEMENT: This article explores the current applications of spectral CT in colorectal cancer by outlining the fundamentals of spectral CT, the spectral parameters used to assess, stage, and predict the prognosis of primary and metastatic disease, as well as the main findings from the current literature. KEY POINTS: Spectral CT may be helpful in the detection of colorectal primary tumors, lymph node metastases, and liver metastases, as well as in predicting treatment response. Spectral CT offers a non-invasive method to assess genetic mutations and prognostic factors associated with colorectal primaries. The lack of standardization in technology and measurement methods limits its applicability in clinical practice.

Objectives: We aimed to visualize the interaction of intravitreal gas with the adjacent vitreomacular interface by using prone position (PP) SD-OCT and suggest possible mechanisms of action behind gas-induced posterior vitreous detachment (PVD) in pneumatic vitreolysis (PV). Methods: This was a descriptive-interpretative morphological study. Spectral domain OCT imaging in PP was carried out using a flexible scanning module (SD-OCT-Flex, Heidelberg Engineering) originally designed for bedside imaging. Routine imaging in sitting position was carried out using a regular SD-OCT-device (Heidelberg Engineering). Patients with symptomatic vitreomacular traction (VMT) scheduled for PV with perfluoropropane (C3F8, 0.3 mL) received both sitting and PP imaging immediately before and at regular follow-up visits during the first 3 post-procedural weeks, beginning 3 h after PV. Imaging was reviewed for positional changes of the gas bubble, posterior hyaloid membrane (PHM), VMT configuration, and retrohyaloidal fluid (RHF). Results: Three consecutive patients with VMT were included (age: 79, 80, 82 years). Before the procedure, no positional alterations were detected. After the intravitreal injection of gas, PP allowed for the precise discrimination of the PHM and the posterior border of the gas bubble. In contrast to regular SD-OCT in sitting position, PP imaging showed a flattened VMT by the gas bubble with consecutive displacement of RHF from the macular region to the midperiphery. Conclusions: This exploratory study describes PP imaging as a tool for the assessment of the morphologic dynamics between the posterior hyaloid membrane, retina, and gas bubble in pneumatic vitreolysis. PP in pneumatic vitreolysis causes the gas bubble to flatten the VMT and to push retrohyaloidal fluid to the midperiphery, possibly allowing for the release of persistent vitreoretinal adhesions and consequent PVD induction.

Abstract We present a compact, cost-effective, and highly accurate dispersion-based four-dimensional (4D) feature learning (3D spatial and 1D spectral) method for real-time monitoring of water-suspended microparticles, requiring neither sample preparation nor explicit reconstruction. By jointly leveraging digital holography and spectral imaging, the system uses a single prism to compress holograms with transmission spectral information onto an imaging sensor. Coupling an optical model with deep learning, our system recovers the 3D holographic phase and class identity for six waterborne particle types, exemplified by microplastics, from a single snapshot. This enables all-in-one reconstruction of both geometry and identity. Both supervised and unsupervised pipelines outperform hologram-only baselines, achieving a macro F1 of 98.10%. The F1 score combines precision and recall, offering a comprehensive evaluation of classification performance. With the unsupervised feature-extraction route, our method improves F1 by 15%, greatly reducing reliance on labeled data. The approach enables long-duration, real-time monitoring in dynamic aquatic environments without sample pretreatment or labeling, and it extends readily from transmission spectroscopy to other spectral modalities.

Deep learning enhanced quantum dot mosaic snapshot spectral imaging system

Photon-counting CT (PCD-CT) provides spectral imaging in every clinical scan through virtual monoenergetic image (VMI) reconstruction. However, because conventional iodine-based contrast medium has a low k-edge, image contrast in the VMIs decreases at high energies. Contrast media with higher k-edges may improve contrast in high-energy VMIs. Therefore, this study investigates BAY3685750, a novel iodine-gadolinium hybrid contrast medium. In vitro experiments compared BAY3685750 with conventional iodine-based and gadolinium-based contrast agents in an anthropomorphic abdominal phantom. Relaxivity (r1) of BAY3685750 was measured at 1.41 T in water and human plasma to assess its potential for both MRI and CT applications. Next, 3 Göttingen minipigs underwent multiphasic contrast-enhanced upper abdominal CT on a first-generation PCD-CT. In 2 animals, BAY3685750 was administered for the first scan, followed by a 60-minute washout and a second multiphasic scan with a standard iodine-based contrast medium. In the third animal, the order was reversed. VMIs were reconstructed for all scans, enabling intraindividual comparison of the 2 contrast agents. In the phantom study, BAY3685750 produced higher attenuation than the iodine-based contrast medium and lower attenuation than the gadolinium-based medium. Relative to the iodine medium, BAY3685750 attenuation increased continuously with energy, reaching ~20% (70keV), 36% (100keV), and 78% (140keV) higher values. In vivo, all measured structures (aorta, inferior vena cava, portal vein, liver) had higher attenuation with BAY3685750 than with iodine at all phases and VMIs. The r1 relaxivity of BAY3685750 in human plasma (1.41 T) was 8.95L mmol-1s-1 compared with 5.93L mmol-1s-1 for gadobutrol. The hybrid iodine-gadolinium contrast medium BAY3685750 provides higher attenuation than conventional iodine-based CT contrast, suggesting improved image quality in high-energy VMIs.

Synchrotron split-beam method for clinical photon-counting computed tomography.

High precision fabrication method and spectral imaging performance of harmonic diffractive lenses based on grayscale laser direct writing technology

Spectral Imaging Deep Learning Mapper - SpecDeepMap: An open-source EnMAP-Box semantic segmentation application for hyper- and multispectral mapping

Dynamic spectral imaging colposcopy vs. conventional colposcopy for detecting CIN2+ in women with HPV-positive or low-grade cytology referrals

Multi-fractional spatial-spectral constraint for detecting sub-mosaic objects in snapshot spectral imaging

Development of a coded aperture snapshot spectral imaging system with high-precision focusing