Journal of Biophotonics最新文献

Cervical cancer can be detected at an early stage through the changes occurring in biochemical and morphological properties of epithelium layer. Fluorescence spectroscopy has the ability to identify these subtle changes non-invasively and in real time with good accuracy in comparison with conventional techniques. In this paper, we report the usage of a custom designed spatially resolved fibre-optic probe (SRFOP), which consists of 77 fibres in two concentric rings, for the detection of cervical cancer using fluorescence spectroscopy technique. The aim of this study is to classify different grades of cervical precancer on the basis of their fluorescence spectra followed by a robust classification algorithm. Fluorescence spectra of 28 cervical tissue samples of different categories have been recorded using six detector fibres of FOP at different spatial locations with the source fibre (SF). A 405 nm laser diode source has been utilised to excite the samples and a USB 4000 Ocean Optics spectrometer to collect the output spectra in the wavelength range 400–700 nm. Principal component analysis (PCA) was applied to the collected spectra to reduce the dimensionality of the data while preserving the most significant features for classification. The first 10 principal components, which captured the majority of the variance in the spectra, were selected as input features for the classification model. Classification was then performed using an artificial neural network (ANN) with a specific architecture, including an input layer, hidden layers, and a softmax activation function in the output layer. Experimental and classification results both demonstrate that proximal fibres (PFs) perform better than distal fibres (DFs) in capturing the discriminatory features present in the epithelium layer of cervical tissue samples as PF collect most of the signal from the epithelium layer. The combined approach of spatially resolved fluorescence spectroscopy and PCA-ANN classification techniques is able to discriminate different grades of cervical precancer and normal with an average sensitivity, specificity and accuracy of 93.33%, 96.67% and 95.57%, respectively.

Diabetic retinopathy is one of the most prevalent microvascular complications of diabetes mellitus, and photoacoustic imaging is an effective method for imaging diabetic retinal vessels. Photoacoustic imaging is an emerging noninvasive imaging method based on the photoacoustic effect, which offers advantages of contrast, resolution, and depth imaging. Appropriate photoacoustic reconstruction methods are essential for obtaining high-quality photoacoustic images. In this study, a multi-input self-attention multiscale feature fusion network (SAMF-Net) is proposed for photoacoustic reconstruction. The algorithm accepts two inputs, namely the original photoacoustic signal and the traditional reconstructed image. Furthermore, a global feature extraction module based on the self-attention mechanism is employed to focus on the global information. The results demonstrate that the proposed method exhibits superior reconstruction capability under different sparse detection views. The method has instructive value for photoacoustic image reconstruction and has the potential for further application in the diagnosis of diabetic retinopathy.

Bioluminescence tomography (BLT) is one kind of noninvasive optical molecular imaging technology, widely used to study molecular activities and disease progression inside live animals. By combining the optical propagation model and inversion algorithm, BLT enables three-dimensional imaging and quantitative analysis of light sources within organisms. However, challenges like light scattering and absorption in tissues, and the complexity of biological structures, significantly impact the accuracy of BLT reconstructions. Here, we propose a dictionary learning method based on K-sparse approximation and Orthogonal Procrustes analysis (KSAOPA). KSAOPA uses an iterative alternating optimization strategy, enhancing solution sparsity with k-coefficients Lipschitzian mappings for sparsity(K-LIMAPS) in the sparse coding stage, and reducing errors with Orthogonal Procrustes analysis in the dictionary update stage, leading to stable and precise reconstructions. We assessed the method performance through simulations and in vivo experiments, which showed that KSAOPA excels in localization accuracy, morphological recovery, and in vivo applicability compared to other methods.

Thymoma, a rare tumor from thymic epithelial cells, presents diagnostic challenges because of the subjective nature of traditional methods, leading to high false-negative rates and long diagnosis times. This study introduces a thymoma classification technique that integrates hyperspectral imaging with deep learning. We initially capture pathological slice images of thymoma using a hyperspectral camera and delineate regions of interest to extract spectral data. This data undergoes reflectance calibration and noise reduction. Subsequently, we transform the spectral data into two-dimensional images via the Gramian Angular Field (GAF) method. A variant residual network is then utilized to extract features and classify these images. Our results demonstrate that this model significantly enhances classification accuracy and efficiency, achieving an average accuracy of 95%. The method proves highly effective in automated thymoma diagnosis, optimizing data utilization, and feature representation learning.

One of the urgent tasks of modern medicine is to detect microcirculation disorder during surgery to avoid possible consequences like tissue hypoxia, ischemia, and necrosis. To address this issue, in this article, we propose a compact probe with sapphire tip and optical sensing based on the principle of spatially resolved diffuse reflectance analysis. It allows for intraoperative measurement of tissue effective attenuation coefficient and its alteration during the changes of tissue condition, caused by microcirculation disorder. The results of experimental studies using (1) a tissue-mimicking phantom based on lipid emulsion and hemoglobin and (2) a model of hindlimb ischemia performed in a rat demonstrated the ability to detect rapid changes of tissue attenuation confirming the feasibility of the probe to sense the stressful exposure. Due to a compact design of the probe, it could be useful for rather wide surgical operations and diagnostic purposes as an auxiliary instrument.

The paper is devoted to the study of perfusion and amplitude-frequency spectra of laser Doppler flowmetry (LDF) signals in patients with diabetes mellitus (DM) in different skin areas of the upper and lower extremities using a distributed system of wearable LDF analysers. LDF measurements were performed in the areas of the fingers, toes, wrists and shins. The mean perfusion values, the amplitudes of blood flow oscillations in endothelial, neurogenic, myogenic, respiratory and cardiac frequency ranges, and the values of nutritive blood flow were analysed. The results revealed a decrease in tissue perfusion and nutritive blood flow in the lower extremities and an increase in these parameters in the upper extremities in patients with DM. A decrease in the amplitudes of endothelial and neurogenic oscillations was observed. The obtained results confirm the possibility of using wearable LDF analysers to detect differences in the blood flow regulation in normal and pathological conditions.

A 3D phase scanning method was applied to study blood plasma facies, generating layered polarization maps of the object field. The most sensitive parameters to changes in birefringence distribution were identified. Multifractal analysis using wavelet transforms and fractal dimension spectra provided specific insights into the scale self-similarity of the polarization maps. The multifractal spectra of ellipticity distributions were algorithmically derived, revealing that the third- and fourth-order statistical moments were most sensitive to changes in the supramolecular networks of the facies. These findings were successfully applied to differentiate post-COVID-19 effects with high accuracy.

Zebrafish serves as a valuable model for studying tissue regeneration due to their comprehensive regenerative abilities, particularly in bone tissue. In this study, a Mueller matrix optical coherence tomography (OCT) system was applied to monitor the regenerative processes of zebrafish caudal fins in vivo. The analysis focused on evaluating the thickness of the caudal fin tip and the distribution of internal bone tissue during the regenerative process. Subsequently, the effect of ectoine solution on the regeneration process was observed and discussed. Our findings revealed that the caudal fin blastema did not exhibit phase-induced polarization characteristics in the Mueller matrix OCT images. Statistical analyses indicated that the caudal fins did not fully regenerate to their original state within 21 days. Furthermore, the results suggested that ectoine solution could enhance tissue regeneration. This approach provides a method for quantifying zebrafish caudal fin regeneration and advances observation techniques for biomedical and clinical applications.