NIR-II fluorescence imaging in liver tumor surgery: A narrative review

In liver tumor surgery,the recognition of tumor margin and radical resection of microcancer focis have always been the crucial points to reduce postoperative recurrence of tumor.However,naked-eye inspection and palpation have limited effectiveness in identifying tumor boundaries,and traditional imaging techniques cannot consistently locate tumors in real time.As an intraoperative real-time navigation imaging method,NIRfluorescence imaging has been extensively studied for its simplicity,reliable safety,and superior sensitivity,and is expected to improve the accuracy of liver tumor surgery.In recent years,the research focus of NIRfluorescence has gradually shifted from the-rst near-infrared window(NIR-I,700–900 nm)to the second near-infrared window(NIR-II,1000–1700 nm).Fluorescence imaging in NIR-II reduces the scattering effect of deep tissue,providing a preferable detection depth and spatial resolution while signi-cantly eliminating liver autofluorescence background to clarify tumor margin.Developingfluorophores combined with tumor antibodies will further improve the precision offluorescence-guided surgical navigation.With the development of a bunch offluorophores with phototherapy ability,NIR-II can integrate tumor detection and treatment to explore a new therapeutic strategy for liver cancer.Here,we review the recent progress of NIR-IIfluorescence technology in liver tumor surgery and discuss its challenges and potential development direction.

Self-confocal NIR-II fluorescence microscopy for multifunctional in vivo imaging

Fluorescence imaging in the second near-infrared window(NIR-II,900–1880 nm)with less scattering background in biological tissues has been combined with the confocal microscopic system for achieving deep in vivo imaging with high spatial resolution.However,the traditional NIR-IIfluorescence confocal microscope with separate excitation focus and detection pinhole makes it possess low confocal e±ciency,as well as di±cultly to adjust.Two types of upgraded NIR-IIfluorescence confocal microscopes,sharing the same pinhole by excitation and emission focus,leading to higher confocal e±ciency,are built in this work.One type is-ber-pinhole-based confocal microscope applicable to CW laser excitation.It is constructed forfluorescence intensity imaging with large depth,high stabilization and low cost,which could replace multiphotonfluorescence microscopy in some applications(e.g.,cerebrovascular and hepatocellular imaging).The other type is air-pinhole-based confocal microscope applicable to femtosecond(fs)laser excitation.It can be employed not only for NIR-IIfluorescence intensity imaging,but also for multi-channelfluorescence lifetime imaging to recognize different structures with similarfluorescence spectrum.Moreover,it can be facilely combined with multiphotonfluorescence microscopy.A single fs pulsed laser is utilized to achieve up-conversion(visible multiphotonfluorescence)and down-conversion(NIR-II one-photonfluorescence)excitation simultaneously,extending imaging spectral channels,and thus facilitates multi-structure and multi-functional observation.

Initial Experience of NIR-II Fluorescence Imaging-Guided Surgery in Foot and Ankle Surgery

Optical imaging in the second near-infrared(NIR-II;900-1880 nm)window is currently a popular research topic in the field of biomedical imaging.This study aimed to explore the application value of NIR-II fluorescence imaging in foot and ankle surgeries.A lab-established NIR-II fluorescence surgical navigation system was developed and used to navigate foot and ankle surgeries which enabled obtaining more high-spatial-frequency information and a higher signal-to-background ratio(SBR)in NIR-II fluorescence images compared to NIR-I fluorescence images;our result demonstrates that NIR-II imaging could provide higher-contrast and larger-depth images to surgeons.Three types of clinical application scenarios(diabetic foot,calcaneal fracture,and lower extremity trauma)were included in this study.Using the NIR-II fluorescence imaging technique,we observed the ischemic region in the diabetic foot before morphological alterations,accurately determined the boundary of the ischemic region in the surgical incision,and fully assessed the blood supply condition of the flap.NIR-II fluorescence imaging can help surgeons precisely judge surgical margins,detect ischemic lesions early,and dynamically trace the perfusion process.We believe that portable and reliable NIR-II fluorescence imaging equipment and additional functional fluorescent probes can play crucial roles in precision surgery.

Pay attention to the application of indocyanine green fluorescence imaging technology in laparoscopic liver cancer resection

Traditional laparoscopic liver cancer resection faces challenges,such as difficultiesin tumor localization and accurate marking of liver segments,as well as theinability to provide real-time intraoperative navigation.This approach falls shortof meeting the demands for precise and anatomical liver resection.The introductionof fluorescence imaging technology,particularly indocyanine green,hasdemonstrated significant advantages in visualizing bile ducts,tumor localization,segment staining,microscopic lesion display,margin examination,and lymphnode visualization.This technology addresses the inherent limitations oftraditional laparoscopy,which lacks direct tactile feedback,and is increasinglybecoming the standard in laparoscopic procedures.Guided by fluorescenceimaging technology,laparoscopic liver cancer resection is poised to become thepredominant technique for liver tumor removal,enhancing the accuracy,safetyand efficiency of the procedure.

Application value of indocyanine green fluorescence imaging in guiding sentinel lymph node biopsy diagnosis of gastric cancer: Meta-analysis

BACKGROUND Gastric cancer is a common malignant tumor of the digestive system worldwide,and its early diagnosis is crucial to improve the survival rate of patients.Indocyanine green fluorescence imaging(ICG-FI),as a new imaging technology,has shown potential application prospects in oncology surgery.The meta-analysis to study the application value of ICG-FI in the diagnosis of gastric cancer sentinel lymph node biopsy is helpful to comprehensively evaluate the clinical effect of this technology and provide more reliable guidance for clinical practice.AIM To assess the diagnostic efficacy of optical imaging in conjunction with indocya-nine green(ICG)-guided sentinel lymph node(SLN)biopsy for gastric cancer.METHODS Electronic databases such as PubMed,Embase,Medline,Web of Science,and the Cochrane Library were searched for prospective diagnostic tests of optical imaging combined with ICG-guided SLN biopsy.Stata 12.0 software was used for analysis by combining the"bivariable mixed effect model"with the"midas"command.The true positive value,false positive value,false negative value,true negative value,and other information from the included literature were extracted.A literature quality assessment map was drawn to describe the overall quality of the included literature.A forest plot was used for heterogeneity analysis,and P<0.01 was considered to indicate statistical significance.A funnel plot was used to assess publication bias,and P<0.1 was considered to indicate statistical significance.The summary receiver operating characteristic(SROC)curve was used to calculate the area under the curve(AUC)to determine the diagnostic accuracy.If there was interstudy heterogeneity(I2>50%),meta-regression analysis and subgroup analysis were performed.analysis were performed.RESULTS Optical imaging involves two methods:Near-infrared(NIR)imaging and fluorescence imaging.A combination of optical imaging and ICG-guided SLN biopsy was useful for diagnosis.The positive likelihood ratio was 30.39(95%CI:0.92-1.00),the sensitivity was 0.95(95%CI:0.82-0.99),and the specificity was 1.00(95%CI:0.92-1.00).The negative likelihood ratio was 0.05(95%CI:0.01-0.20),the diagnostic odds ratio was 225.54(95%CI:88.81-572.77),and the SROC AUC was 1.00(95%CI:The crucial values were sensitivity=0.95(95%CI:0.82-0.99)and specificity=1.00(95%CI:0.92-1.00).The Deeks method revealed that the"diagnostic odds ratio"funnel plot of SLN biopsy for gastric cancer was significantly asymmetrical(P=0.01),suggesting significant publication bias.Further meta-subgroup analysis revealed that,compared with fluorescence imaging,NIR imaging had greater sensitivity(0.98 vs 0.73).Compared with optical imaging immediately after ICG injection,optical imaging after 20 minutes obtained greater sensitivity(0.98 vs 0.70).Compared with that of patients with an average SLN detection number<4,the sensitivity of patients with a SLN detection number≥4 was greater(0.96 vs 0.68).Compared with hematoxylin-eosin(HE)staining,immunohistochemical(+HE)staining showed greater sensitivity(0.99 vs 0.84).Compared with subserous injection of ICG,submucosal injection achieved greater sensitivity(0.98 vs 0.40).Compared with 5 g/L ICG,0.5 and 0.05 g/L ICG had greater sensitivity(0.98 vs 0.83),and cT1 stage had greater sensitivity(0.96 vs 0.72)than cT2 to cT3 clinical stage.Compared with that of patients≤26,the sensitivity of patients>26 was greater(0.96 vs 0.65).Compared with the literature published before 2010,the sensitivity of the literature published after 2010 was greater(0.97 vs 0.81),and the differences were statistically significant(all P<0.05).CONCLUSION For the diagnosis of stomach cancer,optical imaging in conjunction with ICG-guided SLN biopsy is a therapeut-ically viable approach,especially for early gastric cancer.The concentration of ICG used in the SLN biopsy of gastric cancer may be too high.Moreover,NIR imaging is better than fluorescence imaging and may obtain higher sensitivity.

Near-Infrared Fluorescence Imaging Contrast Agents for Clinical Research: Limitations and Alternatives

Introduction: Near-infrared fluorescence imaging is a technique that will establish itself in the short term at the international level because it is recognized for its potential to improve the performance of surgical interventions, its moderate investment and operating costs and its portability. Although the technology is now mature, there is currently the problem of the availability of contrast agents to be injected IV. The aim of this methodology article is to propose an alternative solution to the need for contrast agents for clinical research, particularly in oncology. Methodology: They consist of coupling a fluorescent marker in the form of an NHS derivative, such as IR DYE manufactured in compliance with GMP, with therapeutic monoclonal antibodies having marketing authorization for molecular imaging. For a given antibody, the marking procedure must be the subject of a validation file on the final preparation filtered on a sterilizing membrane at 0.22 μm. Once the procedure has been validated, it would be unnecessary to repeat the tests before each clinical research examination. A check of the marking by thin-layer chromatography (TLC) and place it in a sample bank at +4˚C for 1 month of each injected formulation would be sufficient for additional tests if necessary. Conclusion: Molecular near-infrared fluorescence imaging is experiencing development, the process of which could be accelerated by greater availability of clinical contrast agents. Alternative solutions are therefore necessary to promote clinical research in this area. These methods must be shared to make it easier for researchers.

Photoblinkingpolymer dots shine in super-resolution fluorescence imaging

Subject Code:B03With the support by the National Natural Science Foundation of China,a collaborative study by the research groups led by Prof.Sun Yujie(孙育杰)from the State Key Laboratory of Membrane Biology,Biodynamic Optical Imaging Center(BIOPIC),School of Life Sciences,Peking University and Prof.

A generalized deep neural network approach for improving resolution of fluorescence microscopy images

Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed,imaging resolution,and imagingflux.This paper proposes a deep neural network based on a generative adversarial network(GAN).The generator employs a U-Net-based network,which integrates Dense Net for the downsampling component.The proposed method has excellent properties,for example,the network model is trained with several different datasets of biological structures;the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging;and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets.In addition,experimental results showed that the method improved the resolution of caveolin-coated pits(CCPs)structures from 264 nm to 138 nm,a 1.91-fold increase,and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels.

Activatable fluorescent probes for imaging and diagnosis of rheumatoid arthritis

Rheumatoid arthritis(RA)is a systemic autoimmune disease that is primarily manifested as synovitis and polyarticular opacity and typically leads to serious joint damage and irreversible disability,thus adversely affecting locomotion ability and life quality.Consequently,good prognosis heavily relies on the early diagnosis and effective therapeutic monitoring of RA.Activatable fluorescent probes play vital roles in the detection and imaging of biomarkers for disease diagnosis and in vivo imaging.Herein,we review the fluorescent probes developed for the detection and imaging of RA biomarkers,namely reactive oxygen/nitrogen species(hypochlorous acid,peroxynitrite,hydroxyl radical,nitroxyl),pH,and cysteine,and address the related challenges and prospects to inspire the design of novel fluorescent probes and the improvement of their performance in RA studies.

PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for Image Super-Resolution

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)research.However,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction performance.These methods commonly use standard convolutions,which increase the convolutional operation cost of the model.In this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this problem.Specifically,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image reconstruction.Additionally,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature representations.Inspired by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an image.MFFB can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature information.As the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

Pyramid Separable Channel Attention Network for Single Image Super-Resolution

Single Image Super-Resolution(SISR)technology aims to reconstruct a clear,high-resolution image with more information from an input low-resolution image that is blurry and contains less information.This technology has significant research value and is widely used in fields such as medical imaging,satellite image processing,and security surveillance.Despite significant progress in existing research,challenges remain in reconstructing clear and complex texture details,with issues such as edge blurring and artifacts still present.The visual perception effect still needs further enhancement.Therefore,this study proposes a Pyramid Separable Channel Attention Network(PSCAN)for the SISR task.Thismethod designs a convolutional backbone network composed of Pyramid Separable Channel Attention blocks to effectively extract and fuse multi-scale features.This expands the model’s receptive field,reduces resolution loss,and enhances the model’s ability to reconstruct texture details.Additionally,an innovative artifact loss function is designed to better distinguish between artifacts and real edge details,reducing artifacts in the reconstructed images.We conducted comprehensive ablation and comparative experiments on the Arabidopsis root image dataset and several public datasets.The experimental results show that the proposed PSCAN method achieves the best-known performance in both subjective visual effects and objective evaluation metrics,with improvements of 0.84 in Peak Signal-to-Noise Ratio(PSNR)and 0.017 in Structural Similarity Index(SSIM).This demonstrates that the method can effectively preserve high-frequency texture details,reduce artifacts,and have good generalization performance.

Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-imagefree phase retrieval from single-shot hologram

Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.

Recent Advances in Super-Resolution Fluorescence Imaging and Its Applications in Biology

Fluorescence microscopy has become an essential tool for biological research because it can be minimally invasive, acquire data rapidly, and target molecules of interest with specific labeling strategies. However, the diffraction-limited spatial resolution, which is classically limited to about 200 nm in the lateral direction and about 500 nm in the axial direction, hampers its application to identify delicate details of subcellular structure. Extensive efforts have been made to break diffraction limit for obtaining high-resolution imaging of a biological specimen. Various methods capable of obtaining super-resolution images with a resolution of tens of nanometers are currently available. These super-resolution techniques can be generally divided into three primary classes： （1） patterned illumination- based super-resolution imaging, which employs spatially and temporally modulated illumination light to reconstruct sub-diffraction structures; （2） single-molecule localization-based super-resolution imaging, which localizes the profile center of each individual fluo- rophore at subdiffraction precision; （3） bleaching/blinking-based super-resolution imaging. These super-resolution techniques have been utilized in different biological fields and provide novel insights into several new aspects of life science. Given unique technical merits and commercial availability of super-resolution fluorescence microscope, increasing applications of this powerful technique in life science can be expected.

Automated apoptosis identification in fluorescence imaging of nucleus based on histogram of oriented gradients of high-frequency wavelet coefficients

The automatic and accurate identification of apoptosis facilitates large-scale cell analysis.Most identification approaches using nucleus fluorescence imaging are based on specific morphological parameters.However,these parameters cannot completely describe nuclear morphology,thus limiting the identification accuracy of models.This paper proposes a new feature extraction method to improve the performance of the model for apoptosis identification.The proposed method uses a histogram of oriented gradient(HOG)of high-frequency wavelet coefficients to extract internal and edge texture information.The HOG vectors are classified using support vector machine.The experimental results demonstrate that the proposed feature extraction method well performs apoptosis identification,attaining 95:7% accuracy with low cost in terms of time.We confirmed that our method has potential applications to cell biology research.

Deep-learning-based methods for super-resolution fluorescence microscopy

The algorithm used for reconstruction or resolution enhancement is one of the factors affectingthe quality of super-resolution images obtained by fluorescence microscopy.Deep-learning-basedalgorithms have achieved stateof-the-art performance in super-resolution fluorescence micros-copy and are becoming increasingly attractive.We firstly introduce commonly-used deep learningmodels,and then review the latest applications in terms of the net work architectures,the trainingdata and the loss functions.Additionally,we discuss the challenges and limits when using deeplearning to analyze the fluorescence microscopic data,and suggest ways to improve the reliability and robustness of deep learning applications.

Adaptive Design of Fluorescence Imaging Systems for Custom Resolution, Fields of View, and Geometries

Objective and Impact Statement:We developed a generalized computational approach to design uniform,high-intensity excitation light for low-cost,quantitative fluorescence imaging of in vitro,ex vivo,and in vivo samples with a single device.Introduction:Fluorescence imaging is a ubiquitous tool for biomedical applications.Researchers extensively modify existing systems for tissue imaging,increasing the time and effort needed for translational research and thick tissue imaging.These modifications are applicationspecific,requiring new designs to scale across sample types.Methods:We implemented a computational model to simulate light propagation from multiple sources.Using a global optimization algorithm and a custom cost function,we determined the spatial positioning of optical fibers to generate 2 illumination profiles.These results were implemented to image core needle biopsies,preclinical mammary tumors,or tumor-derived organoids.Samples were stained with molecular probes and imaged with uniform and nonuniform illumination.Results:Simulation results were faithfully translated to benchtop systems.We demonstrated that uniform illumination increased the reliability of intraimage analysis compared to nonuniform illumination and was concordant with traditional histological findings.The computational approach was used to optimize the illumination geometry for the purposes of imaging 3 different fluorophores through a mammary window chamber model.Illumination specifically designed for intravital tumor imaging generated higher image contrast compared to the case in which illumination originally optimized for biopsy images was used.Conclusion:We demonstrate the significance of using a computationally designed illumination for in vitro,ex vivo,and in vivo fluorescence imaging.Applicationspecific illumination increased the reliability of intraimage analysis and enhanced the local contrast of biological features.This approach is generalizable across light sources,biological applications,and detectors.

Compact and robust dual-color linearly polarized illumination source for three-photon fluorescence imaging

The miniaturized femtosecond laser in near infrared-Ⅱregion is the core equipment of threephoton microscopy.In this paper,we design a compact and robust illumination source that emits dual-color linearly polarized light for three-photon microscopy.Based on an all-polarizationmaintaining passive mode-locked fiber laser,we shift the center wavelength of the pulses to the 1.7m band utilizing cascade Raman effect,thereby generate dual-wavelength pulses.To enhance clarity,the two wavelengths are separated through the graded-index multimode fiber.Then we obtain the dual-pulse sequences with 1639.4 nm and 1683.7 nm wavelengths,920 fs pulse duration,and 23.75 MHz pulse repetition rate.The average power of the signal is 53.64mW,corresponding to a single pulse energy of 2.25 nJ.This illumination source can be further amplified and compressed for three-photon fluorescence imaging,especially dual-color three-photon fluorescence imaging,making it an ideal option for biomedical applications.

Hyperspectral Image Super-Resolution Meets Deep Learning:A Survey and Perspective

Hyperspectral image super-resolution,which refers to reconstructing the high-resolution hyperspectral image from the input low-resolution observation,aims to improve the spatial resolution of the hyperspectral image,which is beneficial for subsequent applications.The development of deep learning has promoted significant progress in hyperspectral image super-resolution,and the powerful expression capabilities of deep neural networks make the predicted results more reliable.Recently,several latest deep learning technologies have made the hyperspectral image super-resolution method explode.However,a comprehensive review and analysis of the latest deep learning methods from the hyperspectral image super-resolution perspective is absent.To this end,in this survey,we first introduce the concept of hyperspectral image super-resolution and classify the methods from the perspectives with or without auxiliary information.Then,we review the learning-based methods in three categories,including single hyperspectral image super-resolution,panchromatic-based hyperspectral image super-resolution,and multispectral-based hyperspectral image super-resolution.Subsequently,we summarize the commonly used hyperspectral dataset,and the evaluations for some representative methods in three categories are performed qualitatively and quantitatively.Moreover,we briefly introduce several typical applications of hyperspectral image super-resolution,including ground object classification,urban change detection,and ecosystem monitoring.Finally,we provide the conclusion and challenges in existing learning-based methods,looking forward to potential future research directions.

Fluorescence molecular imaging system and fusion algorithm based on 2CCD camera

Infrared and visible light images can be obtained simultaneously by building fluorescence imaging system,which includes fluorescence excitation,images acquisition,mechanical part,image transmission and processing section.This system studied the 2charge-coupled device(CCD)camera(AD-080CL)of the JAI company.Fusion algorithm of visible light and near infrared images was designed for the fluorescence imaging system with wavelet transform image fusion algorithm.In order to enhance the fluorescent moiety of the fusion image,the luminance value of the green component of the color image was changed.And using microsoft foundation classes(MFC)application architecture,the supporting software system was bulit in VS2010 environment.

Response of Ficus microcarpa L.Foliage to Water Stress Determined by Chlorophyll Fluorescence Imaging Technique

[Objective] This study was to determine the response of Ficus microcarpa L. foliage to polyethylene glycol （PEG） simulated water stress using chlorophyll fluo- rescence imaging technique. [Method] The responses of detached leaves from Ficus microcarpa, Ficus benjamina and Nerium oleander to PEG-6000 simulated water stress were detected, and the chlorophyll fluorescence imaging technique was used to detect and analyze the stress at different spots of a single leaf simultaneously. [Result] The responses of Ficus microcarpa, Ficus benjamina and Nerium oleander to dehydration showed that： ~1~） the maximal photochemical efficiency （Fv/Fm） and non- photo-chemical quenching （NPQ） values were small in the reaction center among different detected spots of leaves, and there were great differences between relative electron transport rate （ETR）, photochemical quenching （qP） and quantum efficiency of PSII photochemistry （（φPSII）; （2） the differences of these parameters were more ob- vious among different spots of water-stressed leaves; （3） the discrete degrees of the species with strong resitances decreased significantly. [Conclusion] This study lays the foundation for the further research on the response of plants to drought stress using chlorophyll fluorescence imaging technique.