Quantitative photoacoustic tomography faces challenges arising from ill-posedness caused by acoustic heterogeneity and multi-parameter coupling. We propose the forward-backward splitting diffusion network (FBS-DiffNet), to simultaneously reconstruct optical absorption coefficient and sound speed distributions. This framework unfolds the classical forward-backward splitting algorithm into a cascaded architecture. Each level integrates a data consistency module driven by a residual gradient step network and a generative joint diffusion module. This configuration ensures physical fidelity to measured signals while exploiting spatial correlations between parameters at anatomical boundaries to provide synergistic regularization. Experimental results on simulation, phantom, and in vivo datasets demonstrate that FBS-DiffNet surpasses uniform sound speed methods and prominent sound speed estimation techniques such as K-means-GMM or NF-APACT concerning both visual quality and quantitative metrics. Ablation studies verify the contribution of each key component to these results. By combining physical interpretability with deep learning representational capacity, FBS-DiffNet offers a promising approach for high-precision quantitative photoacoustic tomography.
Erythema body surface area is a key indicator of cutaneous chronic graft-versus-host disease (cGVHD) status, but clinician estimation is unreliable and confounded by hyperpigmentation. Existing automated methods either fit predictive algorithms to subjective human annotations or require costly hyperspectral imagers. To our knowledge, we present the first annotation-free method to delineate both erythema and hyperpigmentation from standard RGB images. Our pipeline (i) reconstructs hyperspectral images from RGB input and (ii) unmixes these into hemoglobin and melanin abundance maps, which predict erythema and hyperpigmentation, respectively. Trained only on synthetic images, the method achieved a median binary classification accuracy above 0.70 across both conditions and both synthetic images and clinical photographs, demonstrating generalizability to the clinical environment.
An erratum to correct a mistake on the name identification in the text in [ Biomed. Opt. Express 16 , 3589 ( 2025 ) 10.1364/BOE.571108 ]. The corrections have no influence on the results and conclusions of the original paper.
This publisher’s note contains a correction to the funding of [ Biomed. Opt. Express 16 , 3027 ( 2025 ) 10.1364/BOE.563310 ]. The article was corrected on 10 August 2026.
Optical coherence tomography (OCT) is a promising alternative to magnetic resonance imaging (MRI) for accessible, high-resolution measurement of posterior eye curvature, including for applications such as exploration of local eye shape deformations in pathologic myopia. However, OCT is a point-scanning interferometric imaging method that spatially encodes the effects of patient motion during acquisition into an acquired volume. These motion artifacts reduce accuracy and increase intra-acquisition variation in measured curvature, reducing the utility of OCT for monitoring posterior eye curvatures longitudinally. We present a method utilizing 3D pupil tracking and sparse orthogonal scanning for post-acquisition correction of motion artifacts in posterior eye OCT volumes and their corresponding curvature maps. We imaged six subjects exhibiting moderate (−6.0 to −3.0 D) to high (<−6.0 D) myopia and demonstrate a reduction in intra-session curvature variability when using our pupil tracking motion correction scheme. We also propose alternative metrics for representing 2-dimensional posterior eye curvature data to researchers and clinicians.
Modulating excitation polarization across camera frames can improve the precision of single-molecule orientation-localization microscopy (SMOLM), but principled methods for designing these excitation sequences remain underdeveloped. Here, we introduce a Fisher-information framework for optimizing linearly and circularly polarized excitation schemes under a fixed total illumination photon budget. For the multi-view reflector microscope, 3000 illumination photons, and 7.5 detected background photons per pixel, an optimized four-frame linear scheme improves median orientation measurement precision by 21.2% relative to the conventional six-frame equal-pumping scheme and achieves Cramér–Rao-bound-limited median precisions of σ θ ,50 =2.59 ∘ and σ ϕ ,50 =3.22 ∘ . Increasing to six optimized linear frames provides only modest additional improvement, demonstrating that four frames offer an effective balance between precision, uniformity across molecular orientations, and temporal resolution. With only two frames, circularly polarized excitation yields more uniform performance and 27% better median precision than two-frame linear excitation, whereas linear schemes perform better with four or more frames. Applying the framework to four engineered dipole-spread functions shows that excitation sequences should be tailored to the detection system. These results provide practical strategies for balancing orientation precision, photon budget, and acquisition speed in SMOLM.
Optical coherence tomography (OCT) requires accurate calibration of wavenumber sampling and dispersion compensation to achieve optimal axial resolution. Conventional calibration methods rely on dedicated mirror measurements acquired at multiple optical path differences, requiring dedicated acquisitions, careful alignment, and additional acquisition time. We present a physics-informed optimisation framework that recovers OCT calibration directly from a single routine B-scan. Within the complex leader-follower reconstruction framework, the method jointly estimates wavenumber remapping and dispersion compensation functions by optimising image quality in reconstructed OCT data. Experiments on a swept-source OCT system demonstrate that the recovered calibration functions produce image quality comparable to conventional mirror-based calibration while requiring only a single acquired image and 30–60 seconds of optimisation. Samples containing depth-distributed structure recover calibration functions closely matching the underlying system calibration, whereas structurally limited samples produce image-specific effective calibration within the observed depth range. These results demonstrate the feasibility of rapid software-defined OCT calibration directly from imaging data, reducing dependence on dedicated calibration measurements, and simplifying OCT system operation.
Abstract Purpose The optic nerve head (ONH) is a central feature of the retina, affected in many human ocular pathologies, yet it has remained underexplored in most mouse models of disease. We hypothesize that the analysis of the ONH can yield valuable insight into the phenotype of retinal diseases and that pathological changes can be detected using state-of-the-art optical coherence tomography (OCT). Methods Four mouse models – the 5xFAD, PS19 and APP/PS1 models of Alzheimer’s disease (AD) as well as the SOD1 knockout mouse model – were imaged using a polarization-sensitive OCT system to investigate potential disease related changes of the ONH. 5xFAD and SOD1 animals were investigated longitudinally to study disease progression. Additionally, aging effects in wild type mice were studied. Results Two different analysis methods for the segmentation of the ONH were implemented and evaluated. Longitudinal changes to the ONH in 5xFAD animals were observed, specifically an increase of ONH volume from 3 to 5 months of age followed by a strong decrease until 9 months of age. Significant differences between transgenic (tg) and non-transgenic (ntg) animals, as well as sex dependent distinctions were found. Also, for the APP/PS1 model disease related differences between ntg and tg APP/PS1 were significant. Conclusions We demonstrated a simple segmentation of the ONH structure based on OCT intensity images and show its potential as a preclinical biomarker in amyloid mouse models of AD.
Lymphatic vessels play essential roles in immune regulation and tissue homeostasis, yet their distribution within the oral mucosa is incompletely quantified due to the lack of noninvasive, depth-resolved imaging tools. In this study, optical coherence tomography (OCT) combined with an optimized optical attenuation coefficient estimation (OAC) was used to segment and quantify lymphatic vessel networks in healthy and ulcerated human oral mucosa. The results showed descriptive depth-related patterns across the three oral regions (lip, hard palate, and buccal mucosa). The lip showed an overall increase across the measured depth intervals; the hard palate remained consistently low in mean lymphatic density with little variation, whereas the buccal mucosa increased in superficial intervals and gradually decreased at greater depths. In ulcerative lesions, lymphatic density was lower within the ulcer region compared with the surrounding tissue in superficial and mid-depth layers, with this difference decreasing at greater depths. OCT angiography further demonstrated spatial parallelism between lymphatic vessel networks and blood vessels in both healthy and ulcerated tissues. These findings demonstrate that OAC-enhanced OCT enables non-invasive, depth-resolved visualization and quantification of oral lymphatic vessel networks, providing reference characteristics for normal oral mucosa and revealing region and pathology-associated lymphatic alterations relevant to inflammatory conditions and early pathological changes.
