Automatic and real-time tissue sensing for autonomous intestinal anastomosis using hybrid MLP-DC-CNN classifier-based optical coherence tomography.

Anastomosis is a common and critical part of reconstructive procedures within gastrointestinal, urologic, and gynecologic surgery. The use of autonomous surgical robots such as the smart tissue autonomous robot (STAR) system demonstrates an improved efficiency and consistency of the laparoscopic small bowel anastomosis over the current da Vinci surgical system. However, the STAR workflow requires auxiliary manual monitoring during the suturing procedure to avoid missed or wrong stitches. To eliminate this monitoring task from the operators, we integrated an optical coherence tomography (OCT) fiber sensor with the suture tool and developed an automatic tissue classification algorithm for detecting missed or wrong stitches in real time. The classification results were updated and sent to the control loop of STAR robot in real time. The suture tool was guided to approach the object by a dual-camera system. If the tissue inside the tool jaw was inconsistent with the desired suture pattern, a warning message would be generated. The proposed hybrid multilayer perceptron dual-channel convolutional neural network (MLP-DC-CNN) classification platform can automatically classify eight different abdominal tissue types that require different suture strategies for anastomosis. In MLP, numerous handcrafted features (∼1955) were utilized including optical properties and morphological features of one-dimensional (1D) OCT A-line signals. In DC-CNN, intensity-based features and depth-resolved tissues' attenuation coefficients were fully exploited. A decision fusion technique was applied to leverage the information collected from both classifiers to further increase the accuracy. The algorithm was evaluated on 69,773 testing A-line data. The results showed that our model can classify the 1D OCT signals of small bowels in real time with an accuracy of 90.06%, a precision of 88.34%, and a sensitivity of 87.29%, respectively. The refresh rate of the displayed A-line signals was set as 300 Hz, the maximum sensing depth of the fiber was 3.6 mm, and the running time of the image processing algorithm was ∼1.56 s for 1,024 A-lines. The proposed fully automated tissue sensing model outperformed the single classifier of CNN, MLP, or SVM with optimized architectures, showing the complementarity of different feature sets and network architectures in classifying intestinal OCT A-line signals. It can potentially reduce the manual involvement of robotic laparoscopic surgery, which is a crucial step towards a fully autonomous STAR system.

Aberrant activated Notch1 promotes prostate enlargement driven by androgen signaling via disrupting mitochondrial function in mouse.

The prostate is a vital accessory gonad in the mammalian male reproductive system. With the ever-increasing proportion of the population over 60 years of age worldwide, the incidence of prostate diseases, such as benign prostatic hyperplasia (BPH) and prostate cancer (PCa), is on the rise and is gradually becoming a significant medical problem globally. The notch signaling pathway is essential in regulating prostate early development. However, the potential regulatory mechanism of Notch signaling in prostatic enlargement and hyperplasia remains unclear. In this study, we proved that overactivation of Notch1 signaling in mouse prostatic epithelial cells (OEx) led to prostatic enlargement via enhancing proliferation and inhibiting apoptosis of prostatic epithelial cells. Further study showed that N1ICD/RBPJ directly up-regulated the androgen receptor (AR) and enhanced prostatic sensitivity to androgens. Hyper-proliferation was not found in orchidectomized OEx mice without androgen supply but was observed after Dihydrotestosterone (DHT) supplementation. Our data showed that the number of mitochondrion in prostatic epithelial cells of OEx mice was increased, but the mitochondrial function was impaired, and the essential activity of the mitochondrial respiratory electron transport chain was significantly weakened. Disordered mitochondrial number and metabolic function further resulted in excessive accumulation of reactive oxygen species (ROS). Importantly, anti-oxidant N-Acetyl-L-Cysteine (NAC) therapy could alleviate prostatic hyperplasia caused by the over-activation of Notch1 signaling. Furthermore, we observed the incremental Notch signaling activity in progenitor-like club cells in the scRNA-seq data set of human BPH patients. Moreover, the increased number of TROP2+ progenitors and Club cells was also confirmed in our OEx mice. In conclusion, our study revealed that over-activated Notch1 signaling induces prostatic enlargement by increasing androgen receptor sensitivity, disrupting cellular mitochondrial metabolism, increasing ROS, and a higher number of progenitor cells, all of which can be effectively rescued by NAC treatment.

Single-cell transcriptomic Atlas of aging macaque ocular outflow tissues.

The progressive degradation in the trabecular meshwork (TM) is related to age-related ocular diseases like primary open-angle glaucoma. However, the molecular basis and biological significance of the aging process in TM have not been fully elucidated. Here, we established a dynamic single-cell transcriptomic landscape of aged macaque TM, wherein we classified the outflow tissue into 12 cell subtypes and identified mitochondrial dysfunction as a prominent feature of TM aging. Furthermore, we divided TM cells into 13 clusters and performed an in- depth analysis on cluster 0, which had the highest aging score and the most significant changes in cell proportions between the two groups. Ultimately, we found that the APOE gene was an important differentially expressed gene in cluster 0 during the aging process, highlighting the close relationship between cell migration and extracellular matrix regulation, and TM function. Our work further demonstrated that silencing the APOE gene could increase migration and reduce apoptosis by releasing the inhibition on the PI3K-AKT pathway and downregulating the expression of extracellular matrix components, thereby increasing the aqueous outflow rate and maintaining intraocular pressure within the normal range. Our work provides valuable insights for future clinical diagnosis and treatment of glaucoma.

Depth-dependent attenuation and backscattering characterization of optical coherence tomography by stationary iterative method.

Significance: Extracting optical properties of tissue [e.g., the attenuation coefficient (µ) and the backscattering fraction] from the optical coherence tomography (OCT) images is a valuable tool for parametric imaging and related diagnostic applications. Previous attenuation estimation models depend on the assumption of the uniformity of the backscattering fraction (R) within layers or whole samples, which does not accurately represent real-world conditions. Aim: Our aim is to develop a robust and accurate model that calculates depth-wise values of attenuation and backscattering fractions simultaneously from OCT signals. Furthermore, we aim to develop an attenuation compensation model for OCT images that utilizes the optical properties we obtained to improve the visual representation of tissues. Approach: Using the stationary iteration method under suitable constraint conditions, we derived the approximated solutions of µ and R on a single scattering model. During the iteration, the estimated value of µ can be rectified by introducing the large variations of R, whereas the small ones were automatically ignored. Based on the calculation of the structure information, the OCT intensity with attenuation compensation was deduced and compared with the original OCT profiles. Results: The preliminary validation was performed in the OCT A-line simulation and Monte Carlo modeling, and the subsequent experiment was conducted on multi-layer silicone-dye-TiO2 phantoms and ex vivo cow eyes. Our method achieved robust and precise estimation of µ and R for both simulated and experimental data. Moreover, corresponding OCT images with attenuation compensation provided an improved resolution over the entire imaging range. Conclusions: Our proposed method was able to correct the estimation bias induced by the variations of R and provided accurate depth-resolved measurements of both µ and R simultaneously. The method does not require prior knowledge of the morphological information of tissue and represents more real-life tissues. Thus, it has the potential to help OCT imaging based disease diagnosis of complex and multi-layer biological tissue.

Identification and validation of key biomarkers and potential therapeutic targets for primary open-angle glaucoma.

Primary open-angle glaucoma (POAG) is a prevalent cause of blindness worldwide, resulting in degeneration of retinal ganglion cells and permanent damage to the optic nerve. However, the underlying pathogenetic mechanisms of POAG are currently indistinct, and there has been no effective nonsurgical treatment regimen. The objective of this study is to identify novel biomarkers and potential therapeutic targets for POAG. The mRNA expression microarray datasets GSE27276 and GSE138125, as well as the single-cell high-throughput RNA sequencing (scRNA-seq) dataset GSE148371 were utilized to screen POAG-related differentially expressed genes (DEGs). Functional enrichment analyses, protein-protein interaction (PPI) analysis, and weighted gene co-expression network analysis (WGCNA) of the DEGs were performed. Subsequently, the hub genes were validated at a single-cell level, where trabecular cells were annotated, and the mRNA expression levels of target genes in different cell clusters were analyzed. Immunofluorescence and quantitative real-time PCR (qPCR) were performed for further validation. DEGs analysis identified 43 downregulated and 32 upregulated genes in POAG, which were mainly enriched in immune-related pathways, oxidative stress, and endoplasmic reticulum (ER) stress. PPI networks showed that FN1 and DUSP1 were the central hub nodes, while GPX3 and VAV3 were screened out as hub genes through WGCNA and subsequently validated by qPCR. Finally, FN1, GPX3, and VAV3 were determined to be pivotal core genes via single-cell validation. The relevant biomarkers involved in the pathogenesis of POAG, may serve as potential therapeutic targets. Further studies are necessary to unveil the mechanisms underlying the expression variations of these genes in POAG.

Vanillin prevents the growth of endometriotic lesions through anti-inflammatory and antioxidant pathways in a mouse model.

Endometriosis is an estrogen-dependent chronic inflammatory gynecological disease defined by the presence of endometrial glands and mesenchyme outside the uterine cavity, named ectopic endometrium. Recent studies showed that endometriosis is associated with hormone imbalance, inflammation and oxidative stress. As the main component of vanilla bean extract, vanillin is widely used as a flavoring agent in the food, pharmaceutical, and cosmetic industries. It is known for its anti-inflammatory, antibacterial, and antitumor properties, but its therapeutic efficacy in endometriosis has not been studied. In this study, we evaluated the roles of vanillin in this disease using an induced endometriotic mouse model. The results showed that vanillin significantly inhibited the growth of endometrial lesions. Compared with the control group, the weight and volume of lesions were reduced considerably in the vanillin-treated group, showing its fantastic ability to inhibit cell proliferation and promote apoptosis. In addition, in the treatment group, mRNA expression of the pro-inflammatory cytokines Tnfa, Infg, Il1b, and Il6 was reduced, the number of macrophages and neutrophils was decreased, and the NF-κB signaling pathway was inhibited, indicating that vanillin suppressed the inflammatory response in the ectopic endometrium. Besides, we found that the intensity of tissue reactive oxygen species (ROS) was significantly lower, and mitochondrial complex IV expression was reduced in the vanillin-treated group. Meanwhile, treatment of the immortalized human endometriotic epithelial cell line (11Z) with vanillin resulted in the downregulation of cyclin genes that drive the cell proliferation process, inhibited cell proliferation, promoted apoptosis, and downregulated the expression of LPS-induced inflammatory cytokines. Most importantly, our data showed that the vanillin treatment had only minimal effects on the eutopic endometrium with respect to the pregnancy process, indicating its safety to be used in treating endometriosis in adults. In conclusion, our data suggest that vanillin has potential therapeutic properties for endometriosis as a regulatory molecule of cell proliferation, apoptosis, inflammation, and oxidative stress.

Aberrant activation of Notch1 signaling in the mouse uterine epithelium promotes hyper-proliferation by increasing estrogen sensitivity.

In mammals, the endometrium undergoes dynamic changes in response to estrogen and progesterone to prepare for blastocyst implantation. Two distinct types of endometrial epithelial cells, the luminal (LE) and glandular (GE) epithelial cells play different functional roles during this physiological process. Previously, we have reported that Notch signaling plays multiple roles in embryo implantation, decidualization, and postpartum repair. Here, using the uterine epithelial-specific Ltf-iCre, we showed that Notch1 signaling over-activation in the endometrial epithelium caused dysfunction of the epithelium during the estrous cycle, resulting in hyper-proliferation. During pregnancy, it further led to dysregulation of estrogen and progesterone signaling, resulting in infertility in these animals. Using 3D organoids, we showed that over-activation of Notch1 signaling increased the proliferative potential of both LE and GE cells and reduced the difference in transcription profiles between them, suggesting disrupted differentiation of the uterine epithelium. In addition, we demonstrated that both canonical and non-canonical Notch signaling contributed to the hyper-proliferation of GE cells, but only the non-canonical pathway was involved with estrogen sensitivity in the GE cells. These findings provided insights into the effects of Notch1 signaling on the proliferation, differentiation, and function of the uterine epithelium. This study demonstrated the important roles of Notch1 signaling in regulating hormone response and differentiation of endometrial epithelial cells and provides an opportunity for future studies in estrogen-dependent diseases, such as endometriosis.

Recurrent Self Fusion: Iterative Denoising for Consistent Retinal OCT Segmentation.

Optical coherence tomography (OCT) is a valuable imaging technique in ophthalmology, providing high-resolution, cross-sectional images of the retina for early detection and monitoring of various retinal and neurological diseases. However, discrepancies in retinal layer thickness measurements among different OCT devices pose challenges for data comparison and interpretation, particularly in longitudinal analyses. This work introduces the idea of a recurrent self fusion (RSF) algorithm to address this issue. Our RSF algorithm, built upon the self fusion methodology, iteratively denoises retinal OCT images. A deep learning-based retinal OCT segmentation algorithm is employed for downstream analyses. A large dataset of paired OCT scans acquired on both a Spectralis and Cirrus OCT device are used for validation. The results demonstrate that the RSF algorithm effectively reduces speckle contrast and enhances the consistency of retinal OCT segmentation.

Depth-resolved backscattering signal reconstruction based OCT attenuation compensation.

Optical coherence tomography (OCT) with a robust depth-resolved attenuation compensation method for a wide range of imaging applications is proposed and demonstrated. We derive a model for deducing the attenuation coefficients and the signal compensation value using the depth-dependent backscattering profiles, to mitigate under and over-estimation in tissue imaging. We validated the method using numerical simulation and phantoms, where we achieved stable and robust compensation results over the entire depth of samples. The comparison between other attenuation characterization models and our proposed model is also performed.

Zearalenone affects the growth of endometriosis via estrogen signaling and inflammatory pathways.

Endometriosis is a chronic, inflammatory, estrogen-dependent gynecological disease characterized by the growth of endometrial stromal cells and glands outside the uterine cavity in response to hormones, which commonly occurs in reproductive-age women. Zearalenone (ZEA) is a toxic metabolite produced by Fusarium, which acts as estrogen activity because of the similarity of its structure to estrogen. In this study, we used an endometriosis mouse model: 15 days after ovariectomy, endometrial fragments were sutured on the pelvic wall, and exogenous estrogen was supplied using an estrogen-releasing silicone tube embedded subcutaneously. Mice were treated with different doses of ZEA by gavage for 21 days. The results show that ZEA significantly inhibited the growth of ectopic endometrium in a dose-dependent manner. The proliferation of cells decreased while apoptosis increased in the ectopic tissues of ZEA-treated mice compared to the vehicle group. The expression of estrogen receptor-α and its downstream targets MUC1 and p-AKT decreased, indicating an impaired estrogen signaling activity by ZEA treatment. In addition, the decreased expression of pro-inflammatory cytokine Tnf-α, Il-1ß, and Il-6, the lower number of macrophages and neutrophils cells, and the inhibited NF-κB signaling pathway suggest the inflammatory response in the ectopic endometrium was also suppressed by ZEA treatment. However, when the exogenous estrogen supply is removed, ZEA, in turn, plays an estrogen-like role that promotes cell proliferation in the ectopic endometrium. In summary, our data suggest ZEA acts as an antagonist in endometriotic tissue when estrogen is sufficient but turns to estrogenic activity in the absence of estrogen in the development of endometriosis. ZEA also inhibits ectopic tissue growth by inhibiting inflammatory response in the endometriosis model.

Deep point cloud landmark localization for fringe projection profilometry.

Point clouds have been widely used due to their information being richer than images. Fringe projection profilometry (FPP) is one of the camera-based point cloud acquisition techniques that is being developed as a vision system for robotic surgery. For semi-autonomous robotic suturing, fluorescent fiducials were previously used on a target tissue as suture landmarks. This not only increases system complexity but also imposes safety concerns. To address these problems, we propose a numerical landmark localization algorithm based on a convolutional neural network (CNN) and a conditional random field (CRF). A CNN is applied to regress landmark heatmaps from the four-channel image data generated by the FPP. A CRF leveraging both local and global shape constraints is developed to better tune the landmark coordinates, reject extra landmarks, and recover missing landmarks. The robustness of the proposed method is demonstrated through ex vivo porcine intestine landmark localization experiments.

OCT-guided Robotic Subretinal Needle Injections: A Deep Learning-Based Registration Approach.

Subretinal injection (SI) is an ophthalmic surgical procedure that allows for the direct injection of therapeutic substances into the subretinal space to treat vitreoretinal disorders. Although this treatment has grown in popularity, various factors contribute to its difficulty. These include the retina's fragile, nonregenerative tissue, as well as hand tremor and poor visual depth perception. In this context, the usage of robotic devices may reduce hand tremors and facilitate gradual and controlled SI. For the robot to successfully move to the target area, it needs to understand the spatial relationship between the attached needle and the tissue. The development of optical coherence tomography (OCT) imaging has resulted in a substantial advancement in visualizing retinal structures at micron resolution. This paper introduces a novel foundation for an OCT-guided robotic steering framework that enables a surgeon to plan and select targets within the OCT volume. At the same time, the robot automatically executes the trajectories necessary to achieve the selected targets. Our contribution consists of a novel combination of existing methods, creating an intraoperative OCT-Robot registration pipeline. We combined straightforward affine transformation computations with robot kinematics and a deep neural network-determined tool-tip location in OCT. We evaluate our framework's capability in a cadaveric pig eye open-sky procedure and using an aluminum target board. Targeting the subretinal space of the pig eye produced encouraging results with a mean Euclidean error of 23.8µm.

Notch1 signaling enhances collagen expression and fibrosis in mouse uterus.

Fibrosis is a pathological process characterized by abnormal activation of fibroblasts with increased synthesis of extracellular matrix components, including collagens. It may lead to loss of proper tissue architecture and organ function in clinical diseases such as systemic sclerosis and liver fibrosis. Excess accumulation of collagens is considered the primary indicator of fibrosis. Notch signaling has been reported to be involved in the fibrosis of many different organs, including the liver. Our previous study showed that the uterine-specific over-activation of canonical Notch1 signaling in the mouse uterus (Pgrcre/+ Rosa26N1ICD/+ , OEx) results in complete infertility as a consequence of multiple developmental and physiological defects, together with increased collagen accumulation evidenced by Masson's staining. In this study, we further detected expressions of all 44 collagen genes in these Notch1 gain-of-function transgenic mice and found that 18 collagens have been largely affected. In another aspect, using an intrauterine adhesion model (IUA), we mimicked fibrosis in the mouse uterine. The results suggested that Notch receptors were upregulated only 3 days after induction, and most of the fibril-forming collagen began to upregulate 6 days after the surgery. Furthermore, when induced IUA in the N1ICD-OEx mice, the expression of collagens and fibrosis levels were significantly enhanced. At last, as a Notch signaling inhibitor, the γ-secretase inhibitor N-[N-(3,5-difl uorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT) pretreatment could alleviate the expression of collagens and the symptoms of fibrosis. These results demonstrate that Notch signaling may play a role in upregulating collagens expression in endometrial fibrosis and might be a potential target of fibrosis therapy in the endometrium.

Stabilizing the phase of swept-source optical coherence tomography by a wrapped Gaussian mixture model.

The phase of an optical coherence tomography (OCT) signal carries critical information about particle micro-displacements. However, swept-source OCT (SSOCT) suffers from phase instability problems due to trigger jitters from the swept source. In this Letter, a wrapped Gaussian mixture model (WGMM) is proposed to stabilize the phase of SSOCT systems. A closed-form iteration solution of the WGMM is derived using the expectation-maximization algorithm. Necessary approximations are made for real-time graphic processing unit implementation. The performance of the proposed method is demonstrated through ex vivo, in vivo, and flow phantom experiments. The results show the robustness of the method in different application scenarios.

Optimized OCT-based depth-resolved model for attenuation compensation using point-spread-function calibration.

Optical coherence tomography (OCT) with a robust depth-resolved attenuation compensation method for a wide range of imaging applications is proposed and demonstrated. The proposed novel OCT attenuation compensation algorithm introduces an optimized axial point spread function (PSF) to modify existing depth-resolved methods and mitigates under and overestimation in biological tissues, providing a uniform resolution over the entire imaging range. The preliminary study is implemented using A-mode numerical simulation, where this method achieved stable and robust compensation results over the entire depth of samples. The experiment results using phantoms and corneal imaging exhibit agreement with the simulation result evaluated using signal-to-noise (SNR) and contrast-to-noise (CNR) metrics.

Optical flow optical coherence tomography for determining accurate velocity fields.

Determining micron-scale fluid flow velocities using optical coherence tomography (OCT) is important in both biomedical research and clinical diagnosis. Numerous methods have been explored to quantify the flow information, which can be divided into either phase-based or amplitude-based methods. However, phase-based methods, such as Doppler methods, are less sensitive to transverse velocity components and suffer from wrapped phase and phase instability problems for axial velocity components. On the other hand, amplitude-based methods, such as speckle variance OCT, correlation mapping OCT and split-spectrum amplitude-decorrelation angiography, focus more on segmenting flow areas than quantifying flow velocities. In this paper, we propose optical flow OCT (OFOCT) to quantify accurate velocity fields. The equivalence between optical flow and real velocity fields is validated in OCT imaging. The sensitivity fall-off of a Fourier-domain OCT (FDOCT) system is considered in the modified optical flow continuity constraint. Spatial-temporal smoothness constraints are used to make the optical flow problem well-posed and reduce noises in the velocity fields. An iteration solution to the optical flow problem is implemented in a graphics processing unit (GPU) for real-time processing. The accuracy of the velocity fields is verified through phantom flow experiments by using a diluted milk powder solution as a scattering medium. Velocity fields are then used to detect flow turbulence and reconstruct flow trajectory. The results show that OFOCT is accurate in determining velocity fields and applicable to research concerning fluid dynamics.

Selective retina therapy monitoring by speckle variance optical coherence tomography for dosimetry control.

SIGNIFICANCE: Selective retina therapy (SRT) selectively targets the retinal pigment epithelium (RPE) and reduces negative side effects by avoiding thermal damages of the adjacent photoreceptors, the neural retina, and the choroid. However, the selection of proper laser energy for the SRT is challenging because of ophthalmoscopically invisible lesions in the RPE and different melanin concentrations among patients or even regions within an eye. AIM: We propose and demonstrate SRT monitoring based on speckle variance optical coherence tomography (svOCT) for dosimetry control. APPROACH: M-scans, time-resolved sequence of A-scans, of ex vivo bovine retina irradiated by 1.7-µs duration laser pulses were obtained by a swept-source OCT. SvOCT images were calculated as interframe intensity variance of the sequence. Spatial and temporal temperature distributions in the retina were numerically calculated in a 2-D retinal model using COMSOL Multiphysics. Microscopic images of treated spots were obtained before and after removing the upper neural retinal layer to assess the damage in both RPE and neural layers. RESULTS: SvOCT images show abrupt speckle variance changes when the retina is irradiated by laser pulses. The svOCT intensities averaged in RPE and photoreceptor layers along the axial direction show sharp peaks corresponding to each laser pulse, and the peak values were proportional to the laser pulse energy. The calculated temperatures in the neural retina layer and RPE were linearly fitted to the svOCT peak values, and the temperature of each lesion was estimated based on the fitting. The estimated temperatures matched well with previously reported results. CONCLUSION: We found a reliable correlation between the svOCT peak values and the degree of retinal lesion formation, which can be used for selecting proper laser energy during SRT.

Analysis and evaluation of BC-mode OCT image visualization for microsurgery guidance.

Optical coherence tomography (OCT) has been gaining acceptance in image-guided microsurgery as a noninvasive imaging technique. However, when using B-mode OCT imaging, it is difficult to continuously keep the surgical tool in the imaging field, and the image of the tissue beneath the tool is corrupted by shadow effects. The alternative using C-mode OCT imaging is either too slow in imaging speed when operating in a high-resolution mode, or provides a poor image resolution in a high-speed mode, with the sweep rate less than one million hertz. Moreover, the 3-dimensional rendering of C-mode OCT image makes it difficult to visualize the tissue structure and track the surgical tool beneath the tissue surface. To solve these problems, we propose a BC-mode OCT image visualization method. This method uses a sparse C-scanning scheme, which provides a set of high-resolution B-mode OCT images at sparsely spaced cross sections. The final BC-mode OCT image is obtained by averaging the image set, with inter frame variance processing to enhance the signal of the surgical tool and tissue layers. The performance of BC-mode OCT images, such as image resolution, signal to noise ratio (SNR), imaging speed, and surgical tool tracking accuracy, is analyzed theoretically and verified experimentally. The feasibility of the proposed method is evaluated by guiding the insertion of a 30-gauge needle into the cornea of an ex-vivo human eye freehand. The results show that this provides better visualization of both the surgical tool and the tissue structure than the conventional B- or C- mode OCT image.

Intraoperative Speckle Variance Optical Coherence Tomography for Tissue Temperature Monitoring During Cutaneous Laser Therapy.

Background: Tissue temperature monitoring during cutaneous laser therapy can lead to safer and more effective treatments. In this study, we investigate the use of speckle variance optical coherence tomography (svOCT) to monitor real-time temperature changes in the excised human skin tissue sample during laser irradiation. Methods: To accomplish this, we combined the pulse laser system with a reference-based svOCT system. To calibrate the svOCT, the ex-vivo human skin samples from three individuals with tissues collected from the arm, face, and back were heated with 1-degree increments. Additionally, linear regression was used to extract and evaluate the linear relationship between the temperature and normalized speckle variance value. Experiments were conducted on excised human skin sample to monitor the temperature change during laser therapy with a svOCT system. Thermal modeling of ex-vivo human skin was used to numerically simulate the laser-tissue interaction and estimate the thermal diffusion and peak temperature of the tissue during the laser treatment. Results and Conclusion: These results showed that normalized speckle variance had a linear relationship with the tissue temperature before the onset of tissue coagulation (52°) and we were able to measure the rapid increase of the tissue temperature during laser therapy. The result of the experiment is also in good agreement with the numerical simulation result that estimated the laser-induced peak temperature and thermal relaxation time.

Suture Maps Based on Structural Enhanced Imaging Endoscope for Laparoscopic Robotic Surgery.

A detection method for cutting scheme in 3D is proposed to assist robotic surgical manipulation, leading to an automatic suturing suggestion mapping.