Search Results (279)

The tympanic membrane (TM) is a thin, semi-transparent structure whose morphology and optical appearance provide important diagnostic cues. In the early stages of middle-ear pathology, subtle shape and compliance alterations may precede overt clinical signs, making them valuable early indicators of disease. Such structural changes are difficult to assess reliably using conventional (micro-)otoscopy, which lacks quantitative depth information and is operator-dependent. Data-driven monocular image analysis could enable quantitative assessment of TM geometry and compliance, but the limited availability of annotated three-dimensional datasets constrains the development of these methods. At the same time, realistic simulations of TM appearance remain challenging due to its complex reflectance and transmission behavior. The present study focuses on physiologically healthy tympanic membranes, which provide the baseline anatomical and optical model required before subtle pathological changes can be investigated. This work introduces a parametric physically grounded rendering model of the structures visible during otoscopy: the tympanic membrane, ear canal, and malleus–incus complex. Implemented in the open-source software Blender™ using procedural geometry nodes and physically motivated shaders, the framework generates anatomically plausible three-dimensional geometries via statistical parameter sampling and controlled mesh deformation. Optical appearance is simulated using a computationally efficient layered shading model based on literature-derived tissue reflectance, transmission, and scattering properties. A camera–projector setup models both conventional white-light otoscopy and structured-light imaging, enabling the generation of paired intensity images and corresponding depth maps. The proposed framework establishes a physically grounded representation of the human ear and enables a controllable, extensible modeling pipeline for virtual training, biomechanical finite element analysis, and synthetic data generation for supervised learning. Full article

Conventional histopathological diagnostics rely on morphological assessment of stained tissue sections, requiring extensive sample preparation and subjective expert interpretation. Mid-infrared (MIR) imaging offers a complementary approach by providing spatially resolved, label-free access to the intrinsic biochemical composition of tissue without exogenous contrast agents. This work introduces a preprocessing and analysis pipeline for multiphotometric MIR data, applied to formalin-fixed, paraffin-embedded tissue sections from two patients with histopathologically confirmed head and neck squamous cell carcinoma. Combining differential scattering correction, sub-pixel channel registration, and automated tissue segmentation with unsupervised K-Means clustering, the pipeline achieves label-free discrimination of biochemically distinct tissue compartments. K-Means clustering identified four distinct clusters, of which three corresponded to tissue compartments with protein-to-lipid ratios tentatively consistent with epithelial, tumor-associated, and stromal compartments. The resulting cluster maps showed partial spatial correspondence with mIF reference stainings targeting epithelial and stromal markers, supporting the potential of this approach for label-free tissue characterization in digital pathology. Full article

Background and Clinical Significance: Proliferative fasciitis (PF) is an infrequent benign fibroblastic/myofibroblastic proliferation that may closely resemble a soft tissue sarcoma, creating a diagnostic dilemma out of proportion to its biological behaviour. Because no single clinical, histological or immunohistochemical feature is diagnostic, accurate classification depends on the integration of complementary findings. We describe a challenging case of PF involving the lower leg and present a practical clinicopathological approach to its evaluation. Case Presentation: A 34-year-old man presented with a painless subcutaneous nodule on the lateral aspect of the left lower leg, discovered incidentally. Clinical examination suggested a benign superficial soft-tissue lesion, and because no features raised suspicion for malignancy, complete excision was performed without preoperative imaging. Gross examination revealed a 1.9 × 1.6 × 0.7 cm fascial-based lesion composed of spindle cells and scattered ganglion-like cells within a variably myxoid stroma. Focal nuclear pleomorphism, typical mitotic activity (2 mitoses/10 high-power fields), and limited extension into adjacent adipose tissue broadened the differential diagnosis. Immunohistochemistry demonstrated focal SMA positivity, weak focal desmin and S100 expression, absence of CD31 and CD34 staining, and a low Ki-67 proliferative index (approximately 2–3%). Negative surgical margins, together with integration of the clinical presentation, gross findings, histomorphology, and immunophenotype, supported the diagnosis of proliferative fasciitis. The patient remains free of local recurrence four months after surgery. Conclusions: PF should be considered in the differential diagnosis of superficial spindle-cell proliferations showing deceptively aggressive histological features. Careful clinicopathological correlation remains the cornerstone of diagnosis and helps distinguish this benign entity from its malignant mimics. The clinicopathological framework proposed in this report may assist pathologists in the systematic evaluation of similar diagnostically challenging lesions. Full article

Raman spectroscopy is a commonly used label-free technique for the chemical analysis of microplastics (MPs), which are defined as plastic particles larger than 1 μm but smaller than 5 mm size; given its small signal strength and diffraction-limited spatial resolution, Raman spectroscopy has limited applicability in the study of nanoplastics (NPs), which are defined as particles smaller than 1 μm. Tip-enhanced Raman scattering (TERS) is a promising technique that can overcome these limitations by using a single plasmonic tip of an atomic force microscope (AFM) to enhance the Raman signal from a small sample volume. Here we used TERS for the analysis of NPs in human bronchoalveolar lavage (BAL) fluid. We developed a sub-sampling procedure for nanoscale TERS imaging on an SiO₂/Si substrate and performed control experiments. We investigated the experimental coffee-ring effect on the spatial distribution of NPs on the substrate. We compared the results of TERS imaging with the conventional confocal Raman microscopy and observed the presence of similar types of plastic, pigments, and mineral particles, revealing the origin of NPs from the corresponding MPs. The total nanoparticle density observed in BAL using TERS was ~50 billion particles per liter, most of which were NPs. Our TERS approach may be extended to other types of human fluids and tissue samples to provide insights into the health effects of NPs. Full article

Hyperspectral imaging holds promise for intraoperative brain tissue characterisation, but its high dimensionality complicates clinical deployment. Spectral unmixing offers a pathway to compress data into interpretable abundance maps, yet its impact on downstream tissue discrimination remains unclear. This study evaluated four unmixing models (ASM, LMM, PPNMM, FBM) and three normalisation strategies (SNV, $L_2$, Max) across two hyperspectral in vivo brain surgery datasets HELICoiD (n = 15), SLIMBRAIN (n = 27). Spectral reconstruction (SAM) and tissue classification (F1-score) were assessed using optimised SVMs trained on abundance vectors versus full spectra. SNV normalisation consistently yielded superior classification performance, while the Absorption and Scattering Model (ASM) achieved median PSNR improvements exceeding 15 dB over linear models due to its scattering correction term. Notably, ASM-SNV abundance maps maintained classification accuracy comparable to full-spectrum classifiers despite an order of magnitude data compression. Conversely, superior reconstruction fidelity did not guarantee improved discrimination: ASM-Max significantly degraded tumour classification in SLIMBRAIN ($p<0.001$), indicating that task-agnostic reconstruction can discard clinically relevant spectral features. Tumour classification remained the most challenging task across all conditions, reflecting inherent inter-tumour spectral heterogeneity. These findings establish ASM-SNV as the best tested configuration for intraoperative guidance, balancing spectral interpretability and classification robustness while highlighting the need for task-optimised unmixing frameworks to address pathological variability. Full article

Graphene quantum dots (GQDs) have attracted extensive interest in biomedical applications because of their favorable physicochemical properties, including environmental friendliness, excellent water solubility, high chemical stability, and facile surface modification. However, most GQDs exhibit fluorescence in the ultraviolet or visible region, which limits their biomedical applications because autofluorescence from biological systems reduces the signal-to-noise ratio in biosensing and bioimaging. Over the past decade, the emission of GQDs has been extended from the UV–visible region into the red-to-near-infrared (NIR) region. Red-to-NIR fluorescence enables higher-resolution imaging and deeper tissue penetration by reducing light scattering and minimizing tissue absorption and autofluorescence. In this review, we summarize recent advances in red-to-NIR-fluorescent GQDs for biomedical applications, including their synthesis, optical properties, surface engineering, and applications in biosensing, bioimaging and theranostics. Finally, we discuss the current challenges and future potential development of the red-to-NIR-fluorescent GQDs. Full article

Time-domain dynamic full-field optical coherence tomography (D-FFOCT) is a powerful label-free imaging modality that enables functional visualization of cellular activity in living tissues with subcellular resolution. However, its sensitivity remains a major limitation for imaging highly scattering three-dimensional (3D) biological models such as retinal organoids, where incoherent background and inefficient optical flux distribution reduce dynamic contrast and limit imaging depth. In this work, we introduce a ratio-free optical configuration for time-domain D-FFOCT that enables continuous tuning of the sample-to-reference field ratio while minimizing photon losses and suppressing parasitic reflections. This polarization-based architecture allows optimal redistribution of optical flux according to sample scattering conditions and improves sensitivity under both power-limited and dose-limited conditions. Compared with conventional non-polarizing beam splitter configurations, the proposed approach provides a $\sqrt{2}$-fold (3 dB) sensitivity improvement through optical optimization alone. In addition, we investigate for the first time the use of partial-field illumination (PFI) in time-domain D-FFOCT to reduce incoherent background arising from multiple scattering. In retinal organoids imaged at 120 $\mathsf{\mu }$m depth, PFI yields up to a 14.5-fold (23.2 dB) increase in dynamic signal sensitivity, while preserving functional contrast. When combined, ratio-free detection and PFI provide a cumulative sensitivity improvement of 20.5-fold (26.2 dB). These gains enable improved cellular-scale visualization in retinal organoids, including cell-resolved imaging within rosette regions, as well as improved detection of intracellular dynamics in Müller glial cell cultures. This work establishes a practical framework for sensitivity optimization in D-FFOCT and expands its potential for functional imaging, disease modeling, and live-cell monitoring in complex biological systems. Full article

Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy because of its low radiation dose and on-board acquisition capability. However, CBCT images often suffer from scatter artifacts, increased noise, reduced soft-tissue contrast, and inaccurate Hounsfield Unit (HU) values, which limit their direct use for accurate dose calculation and quantitative analysis. To address this limitation, we propose a CBCT-to-CT synthesis framework based on 2.5D context encoding (concatenating five adjacent slices along the channel dimension) and latent-space variational diffusion. The proposed method combines a Vector Quantized Variational Autoencoder (VQ-VAE) and a U-shaped Vision Transformer (U-ViT)-based latent-space Variational Diffusion Model (VDM) to translate CBCT images into synthetic CT (sCT) images in a compressed latent space. To incorporate inter-slice anatomical context while preserving the computational efficiency of 2D processing, five adjacent CBCT slices are concatenated along the channel dimension and used as input. We evaluated the proposed method on the SynthRAD2025 paired CBCT-CT dataset covering head-and-neck, thoracic, and abdominal regions. Under the provided benchmark setting, quantitative evaluation on the validation set showed that the proposed 2.5D model improved peak signal-to-noise ratio (PSNR) from 25.39 dB to 27.44 dB (averaged across regions), structural similarity index measure (SSIM) from 0.813 to 0.846, reduced mean squared error (MSE) from 0.00313 to 0.00200, and lowered Fréchet inception distance (FID) from 1009.33 to 869.53 compared with the 2D baseline. Qualitative results also showed improved anatomical consistency and reduced artifact-related distortions. These findings suggest that neighboring-slice context can enhance HU fidelity and overall image quality in a computationally practical synthesis framework, supporting the usefulness of efficient AI-based cross-modality reconstruction for radiotherapy-related imaging workflows. Full article

Background: Wilson’s disease (WD) is a genetic disorder of copper metabolism with over 600 disease-causing mutations, leading to variable hepatic and neurological symptoms. Early diagnosis and treatment are crucial. To evaluate hepatic involvement, serum scores and non-invasive imaging techniques complement histology. Methods: This pilot study assessed the utility of ultrasound-based tissue attenuation imaging (TAI), tissue scatter distribution imaging (TSI), and shear-wave elastography (SWE) for quantifying steatosis and fibrosis in WD. Results: Among 131 treated patients, 53 (mean age 40.5 ± 13.1 years, M/F = 35/18) underwent measurements. Based on literature-validated thresholds, 41 patients did not have significant liver fibrosis, 5 patients had moderate (F2) and 4 advanced (F3) fibrosis, while 3 patients had cirrhosis. The LS (liver stiffness) was in moderate correlation with FIB-4 (r = 0.306, p < 0.03), NAFLD fibrosis index (r = 0.336, p < 0.02), and APRI (r = 0.31, p = 0.0857). Among the WD patients, 37 had no steatosis (S0), 14 had mild steatosis (S1), and 2 had intermediate steatosis (S2); none of them had severe steatosis (S3) based on UEFF calculation. LS correlated positively with calculated free copper and negatively with serum ceruloplasmin. Normal-BMI patients exhibited no significant steatosis (R2 = 0. 0065, p = 0.966) by ultrasound-estimated fat fraction (UEFF), while those with BMI > 25 kg/m² had increased UEFF correlating with BMI (R2 = 0.288, p < 0.015). Over a five-year follow-up using liver elastography, the fibrosis score did not progress significantly in adequately treated patients. Conclusions: Ultrasound with artificial intelligence-derived parameters supports the non-invasive evaluation of hepatic steatosis and fibrosis in WD, complementing clinical and laboratory data. However, population-specific liver stiffness thresholds are still needed. Full article

Accurate delineation of tumor margins is critical for complete resection and minimizing recurrence, yet existing imaging modalities such as MRI, CT, and fluorescence imaging suffer from limitations including high cost, limited accessibility, and intraoperative constraints. In this study, we propose a low-cost, non-invasive approach for subsurface imaging based on near-infrared (NIR) illumination combined with deep learning. A controlled experimental setup was developed in which structured patterns displayed on an electronic paper screen were concealed beneath a tissue-mimicking chicken phantom and imaged using a NIR-sensitive camera under halogen illumination. A convolutional autoencoder based on a U-Net architecture was trained on approximately 10,000 paired samples to reconstruct hidden structures from highly scattered surface images. The proposed method achieved strong reconstruction performance, with the best model reaching a peak signal-to-noise ratio (PSNR) of 20.14 dB, structural similarity index (SSIM) of 0.92, and feature similarity index (FSIM) of 0.94, significantly outperforming conventional Wiener filtering. Qualitative results demonstrated accurate recovery of subsurface shapes with minor smoothing artifacts. While generalization to out-of-distribution samples remains limited, the findings highlight the potential of combining NIR imaging and deep learning for safe, rapid, and cost-effective subsurface visualization. This work establishes a foundation for future development toward clinically relevant tumor margin detection. Full article

Raman spectroscopy offers unique molecular fingerprinting capability for cancer diagnosis and monitoring, yet its biomedical application is fundamentally limited by weak intrinsic signals and complex biological backgrounds. Composite Raman probes, particularly surface-enhanced Raman scattering (SERS)—based systems, overcome these limitations through synergistic electromagnetic and chemical enhancement combined with functional integration. By engineering plasmonic nanostructures, interfacial electronic states, and molecular architectures, composite Raman probes achieve synergistic electromagnetic and chemical enhancement while incorporating biorecognition units, reporter molecules, and protective coatings to improve stability, specificity, and biocompatibility. In recent years, these probes have evolved from simple signal tags into multifunctional platforms capable of ultrasensitive tumor biomarker detection, high-contrast imaging, surgical guidance, therapy monitoring, and dynamic analysis of the tumor microenvironment (TME). This review systematically summarizes recent advances in composite Raman probes for oncological applications, with an emphasis on material design strategies, enhancement mechanisms, and stimulus-responsive regulation. Representative applications at both molecular and tissue levels are highlighted, including nucleic acid, protein, and exosome detection, as well as in vivo imaging and microenvironmental sensing. Finally, current challenges and future perspectives toward clinical translation are discussed, aiming to provide guidance for the rational design of next-generation Raman probes for precision oncology. Full article

Many biomedical applications rely on the accurate recovery of absorption and scattering properties of human tissue. These characteristics serve as useful diagnostic indicators, holding information regarding the health and physiological status of a human subject. Many experimental methods exist for the determination of these optical properties, though many, such as integrating sphere methods, are not easily used in an in vivo setting. We have constructed and validated a spatially resolved reflectance imaging system that can be used to measure the absolute optical properties of absorbing turbid media in a non-contact, non-invasive fashion. We present detailed calibration procedures that consider our unique incident beam profile and system response with quantitative comparisons between experimentally and computationally obtained reflectance using Monte Carlo methods. Using highly scattering sphere suspensions with added absorption by ink, we show the spatially resolved reflectance imaging system’s ability to recover absorption within 20% of reference collimated transmission measurements and reduced scatter within 6% of those obtained by an extensively tested integrating sphere system, validating our system in preparation for in vivo measurements of the optical properties of human skin. Full article

This work presents the design of a compact, wideband circular microstrip patch antenna for microwave imaging-based brain tumor detection. The main contribution is the development of a compact antenna structure incorporating enhanced ground-plane slot modifications, which significantly improves impedance bandwidth while maintaining a small electrical size, making it highly suitable for medical imaging systems. In addition, the study integrates antenna design, safety evaluation, and microwave imaging analysis within a unified framework to assess tumor localization feasibility using a realistic head model in CST Microwave Studio. The proposed antenna is fabricated on an FR-4 substrate with dimensions of 37 × 54.5 × 1.6 mm³, corresponding to an electrical size of 0.176λ × 0.260λ × 0.0076λ at the lowest operating frequency of 1.43 GHz. Ground-plane slot enhancements are introduced to achieve wideband performance, resulting in an impedance bandwidth from 1.43 to 4 GHz and a fractional bandwidth of 94.7%. The antenna exhibits a maximum realized gain of 3.7 dB. To evaluate its suitability for medical applications, specific absorption rate (SAR) analysis is performed using a realistic human head model at multiple antenna positions and at 1.5, 2.1, 2.5, 3.3, and 3.9 GHz frequencies. The computed SAR values range from 0.109 to 1.56 W/kg averaged over 10 g of tissue, satisfying the IEEE C95.1 safety guideline limit of 2 W/kg. For tumor detection assessment, time-domain simulations are conducted in CST Microwave Studio using a monostatic radar configuration, where the antenna operates as both transmitter and receiver at twelve angular positions around the head with 30° increments. The collected scattered signals are processed using the Delay-and-Sum (DAS) beamforming algorithm to reconstruct dielectric contrast maps and localize the tumor. It should be noted that the tumor-imaging demonstrations presented in this work are based on numerical simulations, while experimental validation is limited to the characterization of the fabricated antenna. Nevertheless, the findings indicate that the proposed antenna is a promising candidate for noninvasive, low-cost microwave brain tumor imaging applications. Full article

In X-ray imaging, tissue scattering is an important factor that degrades image clarity, especially using a portable gridless X-ray imaging device. This study focuses on using Monte Carlo simulation to quantify the effect of scatter radiation on image resolution, by analyzing the point spread function (PSF) and the corresponding modulation transfer function (MTF). Lateral energy absorption profiles in tissue and a cesium iodide (CsI) scintillator were calculated at different X-ray tube voltages (70–90 kV) and filter configurations. Results showed that 85.7% of the total scattered radiation is concentrated at a distance of 4 cm from the central axis for the tissue and 67.37% for the CsI scintillator. The MTF remained high at low spatial frequencies (23% at 0.04 cycles/cm) but dropped at mid frequencies (0.015–0.025 at 0.3–0.6 cycles/cm) and was almost zero at high frequencies (0.004 at 0.8 cycles/cm), indicating loss of detail due to scattering. Increasing the thickness of the filter or adding a copper (Cu) filter reduced the contrast at low spatial frequencies (from 23% to 21%). The study quantitatively investigated the MTF degradation in portable X-ray imaging devices without grid, due to scatter. These results may aid in the development of scatter correction algorithms to improve image quality without the need for an anti-scatter grid. Full article

Background: Glioblastoma (GBM) remains the most aggressive primary brain tumor, and intraoperative frozen section analysis is the current standard for rapid histopathological assessment. However, this approach is time-consuming and resource-intensive. Stimulated Raman scattering (SRS) imaging has emerged as a label-free technique enabling near real-time microscopic evaluation of fresh tissue. This study compares the visualization of selected histopathological features in a newly developed intraoperative SRS system with conventional hematoxylin–eosin (HE) staining in confirmed GBM. Methods: Tumor samples from 30 patients with neuropathologically confirmed GBM were analyzed. For each case, both HE-stained frozen sections and SRS-generated virtual HE-like images were prepared from separate portions of the specimen. Twelve neuropathologists with varying levels of experience assessed 60 images according to seven predefined GBM criteria, resulting in 720 image evaluations. Feature detection was analyzed using cluster-adjusted generalized estimating equation models, and interobserver agreement was assessed using Fleiss’ κ. Results: Descriptively, hypercellularity and hypervascularization were identified at similar frequencies in both modalities, whereas pleomorphism, endothelial proliferation, mitotic activity, and necrosis were more often recognized in HE images. In cluster-adjusted analyses, SRS showed significantly lower detection rates for hypercellularity, pleomorphism, endothelial proliferation, and mitotic activity, while no significant difference was observed for hypervascularization, necrosis, or pseudopalisading after false discovery rate correction. Interobserver agreement was feature-dependent and generally higher for HE than SRS, particularly for hypercellularity. Conclusions: In this feature-level analysis of neuropathologically confirmed GBM, SRS imaging provided rapid, label-free morphological information and showed comparable visualization of selected histopathological features, particularly hypervascularization. While conventional HE-stained frozen sections remained superior for certain WHO-defining features, SRS represents a promising intraoperative adjunct that may complement established neuropathological workflows. Further studies including non-tumor tissue and a broader range of glioma grades are needed to determine the full diagnostic accuracy and clinical applicability of this technique. Full article