Influence mechanism of the temporal duration of laser irradiation on photoacoustic technique: a review.

Significance: In the photoacoustic (PA) technique, the laser irradiation in the time domain (i.e., laser pulse duration) governs the characteristics of PA imaging-it plays a crucial role in the optical-acoustic interaction, the generation of PA signals, and the PA imaging performance. Aim: We aim to provide a comprehensive analysis of the impact of laser pulse duration on various aspects of PA imaging, encompassing the signal-to-noise ratio, the spatial resolution of PA imaging, the acoustic frequency spectrum of the acoustic wave, the initiation of specific physical phenomena, and the photothermal-PA (PT-PA) interaction/conversion. Approach: By surveying and reviewing the state-of-the-art investigations, we discuss the effects of laser pulse duration on the generation of PA signals in the context of biomedical PA imaging with respect to the aforementioned aspects. Results: First, we discuss the impact of laser pulse duration on the PA signal amplitude and its correlation with the lateral resolution of PA imaging. Subsequently, the relationship between the axial resolution of PA imaging and the laser pulse duration is analyzed with consideration of the acoustic frequency spectrum. Furthermore, we examine the manipulation of the pulse duration to trigger physical phenomena and its relevant applications. In addition, we elaborate on the tuning of the pulse duration to manipulate the conversion process and ratio from the PT to PA effect. Conclusions: We contribute to the understanding of the physical mechanisms governing pulse-width-dependent PA techniques. By gaining insight into the mechanism behind the influence of the laser pulse, we can trigger the pulse-with-dependent physical phenomena for specific PA applications, enhance PA imaging performance in biomedical imaging scenarios, and modulate PT-PA conversion by tuning the pulse duration precisely.

Correction of refractive distortion in whole-eye optical coherence tomography imaging of the mouse eye.

Optical coherence tomography (OCT) is an imaging modality that acquires high-resolution cross-sectional images of living tissues and it has become the standard in ophthalmological diagnoses. However, most quantitative morphological measurements are based on the raw OCT images which are distorted by several mechanisms such as the refraction of probe light in the sample and the scan geometries and thus the analysis of the raw OCT images inevitably induced calculation errors. In this paper, based on Fermat's principle and the concept of inverse light tracing, image distortions due to refraction occurred at tissue boundaries in the whole-eye OCT imaging of mouse by telecentric scanning were corrected. Specially, the mathematical correction models were deducted for each interface, and the high-precision whole-eye image was recovered segment by segment. We conducted phantom and in vivo experiments on mouse and human eyes to verify the distortion correction algorithm, and several parameters of the radius of curvature, thickness of tissues and error, were calculated to quantitatively evaluate the images. Experimental results demonstrated that the method can provide accurate and reliable measurements of whole-eye parameters and thus be a valuable tool for the research and clinical diagnosis.

RA V-Net: deep learning network for automated liver segmentation.

Objective.Segmenting liver from CT images is the first step for doctors to diagnose a patient's disease. Processing medical images with deep learning models has become a current research trend. Although it can automate segmenting region of interest of medical images, the inability to achieve the required segmentation accuracy is an urgent problem to be solved.Approach.Residual Attention V-Net (RA V-Net) based on U-Net is proposed to improve the performance of medical image segmentation. Composite Original Feature Residual Module is proposed to achieve a higher level of image feature extraction capability and prevent gradient disappearance or explosion. Attention Recovery Module is proposed to add spatial attention to the model. Channel Attention Module is introduced to extract relevant channels with dependencies and strengthen them by matrix dot product.Main results.Through test, evaluation index has improved significantly. Lits2017 and 3Dircadb are chosen as our experimental datasets. On the Dice Similarity Coefficient, RA V-Net exceeds U-Net 0.1107 in Lits2017, and 0.0754 in 3Dircadb. On the Jaccard Similarity Coefficient, RA V-Net exceeds U-Net 0.1214 in Lits2017, and 0.13 in 3Dircadb.Significance.Combined with all the innovations, the model performs brightly in liver segmentation without clear over-segmentation and under-segmentation. The edges of organs are sharpened considerably with high precision. The model we proposed provides a reliable basis for the surgeon to design the surgical plans.

SS-net: split and spatial attention network for vessel segmentation of retinal OCT angiography.

Optical coherence tomography angiography (OCTA) has been widely used in clinical fields because of its noninvasive, high-resolution qualities. Accurate vessel segmentation on OCTA images plays an important role in disease diagnosis. Most deep learning methods are based on region segmentation, which may lead to inaccurate segmentation for the extremely complex curve structure of retinal vessels. We propose a U-shaped network called SS-Net that is based on the attention mechanism to solve the problem of continuous segmentation of discontinuous vessels of a retinal OCTA. In this SS-Net, the improved SRes Block combines the residual structure and split attention to prevent the disappearance of gradient and gives greater weight to capillary features to form a backbone with an encoder and decoder architecture. In addition, spatial attention is applied to extract key information from spatial dimensions. To enhance the credibility, we use several indicators to evaluate the function of the SS-Net. In two datasets, the important indicators of accuracy reach 0.9258/0.9377, respectively, and a Dice coefficient is achieved, with an improvement of around 3% compared to state-of-the-art models in segmentation.

WVMDA: Predicting miRNA-Disease Association Based on Weighted Voting.

An increasing number of experiments had verified that miRNA expression is related to human diseases. The miRNA expression profile may be an indicator of clinical diagnosis and provides a new direction for the prevention and treatment of complex diseases. In this work, we present a weighted voting-based model for predicting miRNA-disease association (WVMDA). To reasonably build a network of similarity, we established credibility similarity based on the reliability of known associations and used it to improve the original incomplete similarity. To eliminate noise interference as much as possible while maintaining more reliable similarity information, we developed a filter. More importantly, to ensure the fairness and efficiency of weighted voting, we focus on the design of weighting. Finally, cross-validation experiments and case studies are undertaken to verify the efficacy of the proposed model. The results showed that WVMDA could efficiently identify miRNAs associated with the disease.

Identification of Differentially Expressed Genes Associated with Apple Fruit Ripening and Softening by Suppression Subtractive Hybridization.

Apple is one of the most economically important horticultural fruit crops worldwide. It is critical to gain insights into fruit ripening and softening to improve apple fruit quality and extend shelf life. In this study, forward and reverse suppression subtractive hybridization libraries were generated from 'Taishanzaoxia' apple fruits sampled around the ethylene climacteric to isolate ripening- and softening-related genes. A set of 648 unigenes were derived from sequence alignment and cluster assembly of 918 expressed sequence tags. According to gene ontology functional classification, 390 out of 443 unigenes (88%) were assigned to the biological process category, 356 unigenes (80%) were classified in the molecular function category, and 381 unigenes (86%) were allocated to the cellular component category. A total of 26 unigenes differentially expressed during fruit development period were analyzed by quantitative RT-PCR. These genes were involved in cell wall modification, anthocyanin biosynthesis, aroma production, stress response, metabolism, transcription, or were non-annotated. Some genes associated with cell wall modification, anthocyanin biosynthesis and aroma production were up-regulated and significantly correlated with ethylene production, suggesting that fruit texture, coloration and aroma may be regulated by ethylene in 'Taishanzaoxia'. Some of the identified unigenes associated with fruit ripening and softening have not been characterized in public databases. The results contribute to an improved characterization of changes in gene expression during apple fruit ripening and softening.