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In this Letter a novel, to our knowledge, approach for near-infrared (NIR) fluorescence portable confocal microscopy is introduced, aiming to enhance fluorescence imaging of biological samples in the NIR-II window. By integrating a superconducting nanowire single-photon detector (SNSPD) into a confocal microscopy, we have significantly leveraged the detection efficiency of the NIR-II fluorescence signal from indocyanine green (ICG), an FDA-approved dye known for its NIR-II fluorescence capabilities. The SNSPD, characterized by its extremely low dark count rate and optimized NIR system detection efficiency, enables the excitation of ICG with 1 mW and the capture of low-light fluorescence signals from deep regions (up to 512 µm). Consequently, our technique was able to produce high-resolution images of bio samples with a superior signal-to-noise ratio, making a substantial advancement in the field of fluorescence microscopy and offering a promising opportunity for future clinical study. 

Imaging of surface-enhanced Raman scattering (SERS) nanoparticles (NPs) has been intensively studied for cancer detection due to its high sensitivity, unconstrained low signal-to-noise ratios, and multiplexing detection capability. Furthermore, conjugating SERS NPs with various biomarkers is straightforward, resulting in numerous successful studies on cancer detection and diagnosis. However, Raman spectroscopy only provides spectral data from an imaging area without co-registered anatomic context. This is not practical and suitable for clinical applications. Here, we propose a custom-made Raman spectrometer with computer-vision-based positional tracking and monocular depth estimation using deep learning (DL) for the visualization of 2D and 3D SERS NPs imaging, respectively. In addition, the SERS NPs used in this study (hyaluronic acid-conjugated SERS NPs) showed clear tumor targeting capabilities (target CD44 typically overexpressed in tumors) by anex vivoexperiment and immunohistochemistry. The combination of Raman spectroscopy, image processing, and SERS molecular imaging, therefore, offers a robust and feasible potential for clinical applications. 

Surface enhanced resonance Raman (SERS) is a powerful optical technique, which can help enhance the sensitivity of Raman spectroscopy aided by noble metal nanoparticles (NPs). However, current SERS‐NPs are often suboptimal, which can aggregate under physiological conditions with much reduced SERS enhancement. Herein, a robust one‐pot method has been developed to synthesize SERS‐NPs with more uniform core diameters of 50 nm, which is applicable to both non‐resonant and resonant Raman dyes. The resulting SERS‐NPs are colloidally stable and bright, enabling NP detection with low‐femtomolar sensitivity. An algorithm has been established, which can accurately unmix multiple types of SERS‐NPs enabling potential multiplex detection. Furthermore, a new liposome‐based approach has been developed to install a targeting carbohydrate ligand, i.e., hyaluronan, onto the SERS‐NPs bestowing significantly enhanced binding affinity to its biological receptor CD44 overexpressed on tumor cell surface. The liposomal hyaluronan (HA)‐SERS‐NPs enabled visualization of spontaneously developed breast cancer in mice in real time guiding complete surgical removal of the tumor, highlighting the translational potential of these new glyco‐SERS‐NPs. 

Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images’ size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model’s results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings. 

Bistable liquid crystal (LC) shutters have attracted much interest due to their low energy consumption and fast response time. In this paper, we demonstrate an electrically tunable/switchable biostable LC light shutter in biological optics through a three–step easy–assembly, inexpensive, multi–channel shutter. The liquid crystal exhibits tunable transparency (100% to 10% compared to the initial light intensity) under different voltages (0 V to 90 V), indicating its tunable potential. By using biomedical images, the response time, resolution, and light intensity changes of the LC under different voltages in three common fluorescence wavelengths are displayed intuitively. Particularly, the shutter’s performance in tumor images under the near–infrared band shows its application potential in biomedical imaging fields. 

Integrative neural interfaces combining neurophysiology and optogenetics with neural imaging provide numerous opportunities for neuroscientists to study the structure and function of neural circuits in the brain. Such a comprehensive interface demands miniature electrode arrays with high transparency, mechanical flexibility, electrical conductivity, and biocompatibility. Conventional transparent microelectrodes made of a single material, such as indium tin oxide (ITO), ultrathin metals, graphene and poly-(3,4-ethylenedioxythiophene)/poly(styrenesulfonate) (PEDOT:PSS), hardly possess the desired combination of those properties. Herein, ultra-flexible, highly conductive and fully transparent microscale electrocorticogram (μECoG) electrode arrays made of a PEDOT:PSS–ITO–Ag–ITO assembly are constructed on thin parylene C films. The PEDOT:PSS–ITO–Ag–ITO assembly achieves a maximum ∼14% enhancement in light transmission over a broad spectrum (350–650 nm), a significant reduction in electrochemical impedance by 91.25%, and an increase in charge storage capacitance by 1229.78 μC cm −2 . Peeling, bending, and Young's modulus tests verify the enhanced mechanical flexibility and robustness of the multilayer assembly. The μECoG electrodes enable electrical recordings with high signal-to-noise ratios (SNRs) (∼35–36 dB) under different color photostimulations, suggesting that the electrodes are resilient to photon-induced artifacts. In vivo animal experiments confirm that our array can successfully record light-evoked ECoG oscillations from the primary visual cortex (V1) of an anesthetized rat. 

The electrostatic MEMS scanner plays an important role in the miniaturization of the microscopic imaging system. We have developed a new two-dimensional (2D) parametrically-resonant MEMS scanner with patterned Au coating (>90% reflectivity at an NIR 785-nm wavelength), for a near-infrared (NIR) fluorescence intraoperative confocal microscopic imaging system with a compact form factor. A silicon-on-insulator (SOI)-wafer based dicing-free microfabrication process has been developed for mass-production with high yield. Based on an in-plane comb-drive configuration, the resonant MEMS scanner performs 2D Lissajous pattern scanning with a large mechanical scanning angle (MSA, ±4°) on each axis at low driving voltage (36 V). A large field-of-view (FOV) has been achieved by using a post-objective scanning architecture of the confocal microscope. We have integrated the new MEMS scanner into a custom-made NIR fluorescence intraoperative confocal microscope with an outer diameter of 5.5 mm at its distal-end. Axial scanning has been achieved by using a piezoelectric actuator-based driving mechanism. We have successfully demonstrated ex vivo 2D imaging on human tissue specimens with up to five frames/s. The 2D resonant MEMS scanner can potentially be utilized for many applications, including multiphoton microendoscopy and wide-field endoscopy. 

Magnetic particle imaging (MPI) is an emerging noninvasive molecular imaging modality with high sensitivity and specificity, exceptional linear quantitative ability, and potential for successful applications in clinical settings. Computed tomography (CT) is typically combined with the MPI image to obtain more anatomical information. Herein, a deep learning‐based approach for MPI‐CT image segmentation is presented. The dataset utilized in training the proposed deep learning model is obtained from a transgenic mouse model of breast cancer following administration of indocyanine green (ICG)‐conjugated superparamagnetic iron oxide nanoworms (NWs‐ICG) as the tracer. The NWs‐ICG particles progressively accumulate in tumors due to the enhanced permeability and retention (EPR) effect. The proposed deep learning model exploits the advantages of the multihead attention mechanism and the U‐Net model to perform segmentation on the MPI‐CT images, showing superb results. In addition, the model is characterized with a different number of attention heads to explore the optimal number for our custom MPI‐CT dataset.