Imaging Of Biological Tissues Research Articles

Optimization on artifacts in photoacoustic images based on spectrum analyses and signal extraction.

Photoacoustic (PA) imaging is a promising technology for functional imaging of biological tissues, offering optical contrast and acoustic penetration depth. However, the presence of signal aliasing from multiple PA sources within the same imaging object can introduce artifacts and significantly impact the quality of the PA tomographic images. In this study, an optimized method is proposed to suppress these artifacts and enhance image quality effectively. By leveraging signal time-frequency spectrum, signals from each PA source can be extracted. Subsequently, the images are reconstructed using these extracted signals and fused together to obtain an optimized image. To verify this proposed method, PA imaging experiments were conducted on two phantoms and two in vitro samples and the distribution relative error and root mean square error of the images obtained through conventional and optimized methods were calculated. The results demonstrate that the proposed method successfully suppresses the artifacts and substantially improves the image quality.

Physics-Based Practical Speckle Noise Modeling for Optical Coherence Tomography Image Denoising

Optical coherence tomography (OCT) has been extensively utilized in the field of biomedical imaging due to its non-invasive nature and its ability to provide high-resolution, in-depth imaging of biological tissues. However, the use of low-coherence light can lead to unintended interference phenomena within the sample, which inevitably introduces speckle noise into the imaging results. This type of noise often obscures key features in the image, thereby reducing the accuracy of medical diagnoses. Existing denoising algorithms, while removing noise, tend to also damage the structural details of the image, affecting the quality of diagnosis. To overcome this challenge, we have proposed a speckle noise (PSN) framework. The core of this framework is an innovative dual-module noise generator that can decompose the noise in OCT images into speckle noise and equipment noise, addressing each type independently. By integrating the physical properties of noise into the design of the noise generator and training it with unpaired data, we are able to synthesize realistic noise images that match clear images. These synthesized paired images are then used to train a denoiser to effectively denoise real OCT images. Our method has demonstrated its superiority in both private and public datasets, particularly in maintaining the integrity of the image structure. This study emphasizes the importance of considering the physical information of noise in denoising tasks, providing a new perspective and solution for enhancing OCT image denoising technology.

Line-field dynamic optical coherence tomography platform for volumetric assessment of biological tissues.

Dynamic optical coherence tomography (dOCT) utilizes time-dependent signal intensity fluctuations to enhance contrast in OCT images and indirectly probe physiological processes in cells. Majority of the dOCT studies published so far are based on acquisition of 2D images (B-scans or C-scans) by utilizing point-scanning Fourier domain (spectral or swept-source) OCT or full-field OCT respectively, primarily due to limitations in the image acquisition rate. Here we introduce a novel, high-speed spectral domain line-field dOCT (SD-LF-dOCT) system and image acquisition protocols designed for fast, volumetric dOCT imaging of biological tissues. The imaging probe is based on an exchangeable afocal lens pair that enables selection of combinations of transverse resolution (from 1.1 µm to 6.4 µm) and FOV (from 250 × 250 µm2 to 1.4 × 1.4 mm2), suitable for different biomedical applications. The system offers axial resolution of ∼ 1.9 µm in biological tissue, assuming an average refractive index of 1.38. Maximum sensitivity of 90.5 dB is achieved for 3.5 mW optical imaging power at the tissue surface and maximum camera acquisition rate of 2,000 fps. Volumetric dOCT images acquired with the SD-LF-dOCT system from plant tissue (cucumber), animal tissue (mouse liver) and human prostate carcinoma spheroids allow for volumetric visualization of the tissues' cellular and sub-cellular structures and assessment of cellular motility.

Dual Infrared 2‐Photon Microscopy Achieves Minimal Background Deep Tissue Imaging in Brain and Plant Tissues

AbstractTraditional deep fluorescence imaging has primarily focused on red‐shifting imaging wavelengths into the near‐infrared (NIR) windows or implementation of multi‐photon excitation approaches. Here, the advantages of NIR and multiphoton imaging are combined by developing a dual‐infrared two‐photon microscope that enables high‐resolution deep imaging in biological tissues. This study first computationally identifies that photon absorption, as opposed to scattering, is the primary contributor to signal attenuation. A NIR two‐photon microscope is constructed next with a 1640 nm femtosecond pulsed laser and a NIR PMT detector to image biological tissues labeled with fluorescent single‐walled carbon nanotubes (SWNTs). Spatial imaging resolutions are achieved close to the Abbe resolution limit and eliminate blur and background autofluorescence of biomolecules, 300 µm deep into brain slices and through the full 120 µm thickness of a Nicotiana benthamiana leaf. NIR‐II two‐photon microscopy can also measure tissue heterogeneity by quantifying how much the fluorescence power law function varies across tissues, a feature this study exploits to distinguish Huntington's Disease afflicted mouse brain tissues from wildtype. These results suggest dual‐infrared two‐photon microscopy can accomplish in‐tissue structural imaging and biochemical sensing with a minimal background, and with high spatial resolution, in optically opaque or highly autofluorescent biological tissues.

Imaging of biological tissues with terahertz microscopy based on a cross-filament

Terahertz (THz) imaging technology has shown powerful potential for applications in biological sensing due to characteristics of the THz radiation. However, the measurement accuracy is always restricted by the low spatial resolution of THz imaging. In this work, a THz microscopy based on a cross-filament was utilized to inspect different biological tissues. A quasi sub-wavelength resolution was realized without approaching the sampe surface with any actual devices. By using the technique, fine tissue structures of two plant leaves and two animal bodies were precisely identified and analyzed from their temporal, spectral, and absorption images. Furthermore, possible optimization solutions were also discussed in detail for enhancing the imaging performance. This work demonstrated a novel and practical inspection method for pushing the development of THz biological applications.

Engineered microbubbles decorated with red emitting carbon nanoparticles for efficient delivery and imaging

Altering the route of uptake by the cells is an attractive strategy to overcome drug-receptor adaptation problems. Carbon nanoparticles (CNPs) with emission beyond tissue autofluorescence for imaging biological tissues were used to study the phenomenon of uptake by the cells. In this regard, red-emitting carbon nanoparticles (CNPs) were synthesized and incorporated onto lipid microbubbles (MBs). The CNPs showed red emissions in the range of 640 nm upon excitation with 480 nm wavelength of light. Atomic force microscopic and confocal microscopic images showed the successful loading of CNPs onto the MB. Carbon nanoparticle loaded microbubbles (CNP-MBs) were treated with NIH 3 T3 cells at different concentrations. Confocal microscopic imaging studies confirm the presence of CNPs inside the treated cells. Cytotoxicity studies revealed that the CNPs showed minimal toxicity towards cells after loading onto MBs. The CNPs are usually taken up by the cells through the clathrin-mediated (CME) pathway, but when loaded onto MBs, the mechanism of uptake of CNPs is altered, and the uptake by the cells was observed even in the presence of inhibitors for the CME pathway. Loading CNPs onto MBs resulted in the uptake of CNPs by the cell through micropinocytosis and sonophoresis in the presence of ultrasound. The in vivo uptake CNP-MBs were performed in Danio rerio (Zebrafish larvae). This study provides insights into altering the uptake pathway through reformulation by loading nanoparticles onto MBs.

A closer look at high-energy X-ray-induced bubble formation during soft tissue imaging.

Improving the scalability of tissue imaging throughput with bright, coherent X-rays requires identifying and mitigating artifacts resulting from the interactions between X-rays and matter. At synchrotron sources, long-term imaging of soft tissues in solution can result in gas bubble formation or cavitation, which dramatically compromises image quality and integrity of the samples. By combining in-line phase-contrast imaging with gas chromatography in real time, we were able to track the onset and evolution of high-energy X-ray-induced gas bubbles in ethanol-embedded soft tissue samples for tens of minutes (two to three times the typical scan times). We demonstrate quantitatively that vacuum degassing of the sample during preparation can significantly delay bubble formation, offering up to a twofold improvement in dose tolerance, depending on the tissue type. However, once nucleated, bubble growth is faster in degassed than undegassed samples, indicating their distinct metastable states at bubble onset. Gas chromatography analysis shows increased solvent vaporization concurrent with bubble formation, yet the quantities of dissolved gasses remain unchanged. By coupling features extracted from the radiographs with computational analysis of bubble characteristics, we uncover dose-controlled kinetics and nucleation site-specific growth. These hallmark signatures provide quantitative constraints on the driving mechanisms of bubble formation and growth. Overall, the observations highlight bubble formation as a critical yet often overlooked hurdle in upscaling X-ray imaging for biological tissues and soft materials and we offer an empirical foundation for their understanding and imaging protocol optimization. More importantly, our approaches establish a top-down scheme to decipher the complex, multiscale radiation-matter interactions in these applications.

Classification of Microscopic Hyperspectral Images of Blood Cells Based on Lightweight Convolutional Neural Network

Hyperspectral imaging has emerged as a novel imaging modality in the medical field, offering the ability to acquire images of biological tissues while simultaneously providing biochemical insights for in-depth tissue analysis. This approach facilitates early disease diagnosis, presenting advantages over traditional medical imaging techniques. Addressing challenges such as the computational burden of existing convolutional neural networks (CNNs) and imbalances in sample data, this paper introduces a lightweight GhostMRNet for the classification of microscopic hyperspectral images of human blood cells. The proposed model employs Ghost Modules to replace conventional convolutional layers and a cascading approach with small convolutional kernels for multiscale feature extraction, aiming to enhance feature extraction capabilities while reducing computational complexity. Additionally, an SE (Squeeze-and-Excitation) module is introduced to selectively allocate weights to features in each channel, emphasizing informative features and efficiently achieving spatial–spectral feature extraction in microscopic hyperspectral imaging. We evaluated the performance of the proposed GhostMRNet and compared it with other state-of-the-art models using two real medical hyperspectral image datasets. The experimental results demonstrate that GhostMRNet exhibits a superior performance, with an overall accuracy (OA), average accuracy (AA), and Kappa coefficient reaching 99.965%, 99.565%, and 0.9925, respectively. In conclusion, the proposed GhostMRNet achieves a superior classification performance at a smaller computational cost, thereby providing a novel approach for blood cell detection.

Imaging with mass spectrometry: Which ionization technique is best?

The use of mass spectrometry (MS) to acquire molecular images of biological tissues and other substrates has developed into an indispensable analytical tool over the past 25 years. Imaging mass spectrometry technologies are widely used today to study the in situ spatial distributions for a variety of analytes. Early MS images were acquired using secondary ion mass spectrometry and matrix-assisted laser desorption/ionization. Researchers have also designed and developed other ionization techniques in recent years to probe surfaces and generate MS images, including desorption electrospray ionization (DESI), nanoDESI, laser ablation electrospray ionization, and infrared matrix-assisted laser desorption electrospray ionization. Investigators now have a plethora of ionization techniques to select from when performing imaging mass spectrometry experiments. This brief perspective will highlight the utility and relative figures of merit of these techniques within the context of their use in imaging mass spectrometry.

A Nickel-doped, lanthanum gallium germanate-based phosphor as an ultra-broadband short-wavelength infrared region emitter for optical imaging

Short-wavelength infrared region light source (SWIR, λ = 1000–2000 nm) is useful for optical imaging of biological tissues for lack of auto-fluorescence, low intrinsic absorption, and low scattering. Here, we report the synthesis, characterization, and mechanistic studies of a Ni2+-doped, lanthanum gallium germanate-based phosphor, La3Ga5GeO14:Ni2+, that emits SWIR with an ultra-broad bandwidth emission of 1000–2000 nm, a full-width at half maximum (FWHM) of 340 nm, which covers near infrared-II and III regions. The analysis of crystal structure suggests that Ga3+ ions in [GaO6] octahedron lattices are replaced by Ni2+ ions, possibly due to the similar radius shared by both ions. The observed ultra-broadband SWIR luminescence with large Stokes shift is most likely originated from the influence of a strong crystal field on the extent of the cleavage of energy levels of Ni2+ ions, which is supported by the charge density data of [GaO6] octahedra in La3Ga5GeO14 derived from state density functional calculation. A SWIR pc-LED was constructed by packaging the La3Ga5GeO14:Ni2+ phosphor with a near-ultraviolet LED chip, and its potential application in optical imaging of human bodies was explored.

Birefringence mapping of biological tissues based on polarization sensitive non-interferometric quantitative phase imaging technique

ObjectiveOral cancer is a leading cause of mortality globally, particularly affecting developing regions where oral hygiene is often overlooked. The optical properties of tissues are vital for diagnostics, with polarization imaging emerging as a label-free, contrast-enhancing technique widely employed in medical and scientific research over past few decades. Materials and methodsWe present a novel polarization sensitive quantitative phase imaging of biological tissues by incorporating the conventional polarization microscope and transport of intensity equation-based phase retrieval algorithm. This integration provides access to the birefringence mapping of biological tissues. The inherent optical anisotropy in biological tissues induces the polarization dependent refractive index variations which can provide the detailed insights into the birefringence characteristics of their extracellular constituents. Experimental investigations were conducted on both normal and cancerous oral tissue samples by recording a set of three polarization intensity images for each case with a step size of 2 μm. ResultsA noteworthy increment in birefringence quantification was observed in cancerous as compared to the normal tissues, attributed to the proliferation of abnormal cells during cancer progression.The mean birefringence values were calculated for both normal and cancerous tissues, revealing a significant increase in birefringence of cancerous tissues (2.1 ± 0.2) × 10−2 compared to normal tissues (0.8 ± 0.2) × 10−2. Data were collected from 8 patients in each group under identical experimental conditions. ConclusionThis polarization sensitive non-interferometric optical approach demonstrated effective discrimination between cancerous and normal tissues, with various parameters indicating elevated values in cancerous tissues.

Brillouin microscopic imaging of ex-vivo porcine eye using VIPA-CMOS-based spectrometer

The research on non-invasive ophthalmic imaging has become a hot topic in diagnosing ophthalmic diseases, especially assessment of corneal biomechanics has been an unmet clinical need in ophthalmology for many years. The Brillouin microscopy technique based on spontaneous Brillouin scattering has emerged as a unique elastography method due to its advantages of non-contact, label-free, and high-resolution biomechanical imaging of biological tissues. Here, we developed a three-dimensional (3D) optical microscope system for ex-vivo porcine cornea Brillouin imaging. To do this, we designed and experimentally demonstrated a setup with double-stage VIPA etalons and CMOS camera that integrate to a fully parallel dispersive Brillouin spectrometer. The designed spectrometer exhibits a smaller dispersion rate to enhance the spectral sampling rate and downsizes the size of the double-stage-based system. Using the optical microscope system, we measured and mapped the Brillouin frequency shifts of the cornea and crystalline lens in ex-vivo porcine eyes at micron-scale spatial resolution, and a spectral accuracy of < 50 MHz can be achieved without interpolating the spectra. This work is essential for future applications of Brillouin microscopy in clinical diagnosis and treatment of ophthalmic diseases.

Single-fiber probes for combined sensing and imaging in biological tissue: recent developments and prospects.

Single-fiber-based sensing and imaging probes enable the co-located and simultaneous observation and measurement (i.e., 'sense' and 'see') of intricate biological processes within deep anatomical structures. This innovation opens new opportunities for investigating complex physiological phenomena and potentially allows more accurate diagnosis and monitoring of disease. This prospective review starts with presenting recent studies of single-fiber-based probes for concurrent and co-located fluorescence-based sensing and imaging. Notwithstanding the successful initial demonstration of integrated sensing and imaging within single-fiber-based miniaturized devices, the realization of these devices with enhanced sensing sensitivity and imaging resolution poses notable challenges. These challenges, in turn, present opportunities for future research, including the design and fabrication of complex lens systems and fiber architectures, the integration of novel materials and other sensing and imaging techniques.

Quantitative mass spectrometry imaging (qMSI): A tutorial.

Mass spectrometry imaging (MSI) is an analytical technique that enables the simultaneous detection of hundreds to thousands of chemical species while retaining their spatial information; usually, MSI is applied to biological tissues. Combining these elements can create ion images, which allows for the identification and localization of multiple chemical species within the sample. Being able to produce molecular images of biological tissues has already impacted the study of health and disease; however, the next logical step is being able to combine MSI with quantitative mass spectrometry methods to both quantify and determine the localization of disease progression or drug action. In this tutorial, we will detail the main factors to consider when designing a qMSI experiment and highlight the methods that have been developed to overcome these added complexities, specifically for those newer to the field of MSI.

Terahertz polarimetric imaging of biological tissue: Monte Carlo modeling of signal contrast mechanisms due to Mie scattering.

Many promising biomedical applications have been proposed for terahertz (THz) spectroscopy and diagnostic imaging techniques. Polarimetric imaging systems are generally useful for enhancing imaging contrasts, yet the interplay between THz polarization changes and the random discrete structures in biological samples is not well understood. In this work, we performed Monte Carlo simulations of the propagation of polarized THz waves in skin and adipose tissues based on the Mie scattering from intrinsic structures, such as hair follicles or sweat glands. We show that the polarimetric contrasts are distinctly affected by concentration, size and dielectric properties of the scatterers, as well as the frequency and polarization of the incident THz waves. We describe the experimental requirements for observing and extracting these polarimetric signals due to the low energy and small angular spread of the back-scattered THz radiation. We analyzed the spatially integrated Mueller matrices of samples in the normal-incidence back-scattering geometry. We show that the frequency-dependent degree of polarization (DOP) can be used to infer the concentrations and dielectric contents of the scattering structures. Our modeling approach can be used to inform the design of the imaging modalities and the interpretation of the spectroscopic data in future terahertz biomedical imaging applications.

Photoacoustic microscopy for real-time monitoring of near-infrared optical absorbers inside biological tissue.

We developed a high-speed optical-resolution photoacoustic microscopy (OR-PAM) system using a high-repetition-rate supercontinuum (SC) light source and a two-axes Galvano scanner. The OR-PAM system enabled real-time imaging of optical absorbers inside biological tissues with excellent excitation wavelength tunability. In the near-infrared (NIR) wavelength range, high-speed OR-PAM faces limitations due to the lack of wavelength-tunable light sources. Our study aimed to enable high-speed OR-PAM imaging of various optical absorbers, including NIR contrast agents, and validate the performance of high-speed OR-PAM in the detection of circulating tumor cells (CTCs). A high-repetition nanosecond pulsed SC light source was used for OR-PAM. The excitation wavelength was adjusted by bandpass filtering of broadband light pulses produced by an SC light source. Phantom and in vivo experiments were performed to detect tumor cells stained with an NIR contrast agent within flowing blood samples. The newly developed high-speed OR-PAM successfully detected stained cells both in the phantom and in vivo. The phantom experiment confirmed the correlation between the tumor cell detection rate and tumor cell concentration in the blood sample. The high-speed OR-PAM effectively detected stained tumor cells. Combining high-speed OR-PAM with molecular probes that stain tumor cells in vivo enables in vivo CTC detection.

Real-time line-field optical coherence tomography for cellular resolution imaging of biological tissue.

A real-time line-field optical coherence tomography (LF-OCT) system is demonstrated with image acquisition rates of up to 5000 B-frames or 2.5 million A-lines per second for 500 A-lines per B-frame. The system uses a high-speed low-cost camera to achieve continuous data transfer rates required for real-time imaging, allowing the evaluation of future applications in clinical or intraoperative environments. The light source is an 840 nm super-luminescent diode. Leveraging parallel computing with GPU and high speed CoaXPress data transfer interface, we were able to acquire, process, and display OCT data with low latency. The studied system uses anamorphic beam shaping in the detector arm, optimizing the field of view and sensitivity for imaging biological tissue at cellular resolution. The lateral and axial resolution measured in air were 1.7 µm and 6.3 µm, respectively. Experimental results demonstrate real-time inspection of the trabecular meshwork and Schlemm's canal on ex vivo corneoscleral wedges and real-time imaging of endothelial cells of human subjects in vivo.

Construction of a near infrared fluorescence system for imaging of biological tissues.

Surgical procedures often rely on unaided visual observation or endoscopic assistance, which may pose challenges in cases involving intricate anatomical relationships. Real-time imaging technologies capable of intraoperative visualization of target organs have the potential to enhance the precision of surgical procedures by facilitating accurate identification, separation, and protection of vital tissues or organs. Despite these advantages, the widespread adoption of such technologies has been hindered by factors such as the prohibitive cost of equipment. This study aims to optimize and develop a device based on Indocyanine Green (ICG) for fluorescence imaging. The objective is to monitor changes in the average fluorescence intensity of ICG in the bladder, offering valuable guidance for surgeries involving the bladder. 1. Male rabbits were administered 0.01mg/ml ICG via the renal pelvis and ear vein to obtain fluorescence images of the ureter, bladder, and small intestine. 2. After ligating the bilateral ureters of male rabbits, a retrograde bladder perfusion of 5ml 0.01mg/ml ICG was conducted to capture fluorescence images of the bladder over time. The average fluorescence intensity was computed using Image Pro Plus 6.0, and the corresponding curve was generated using Prism 8.0. Using a similar methodology, the average fluorescence intensity of male rabbits without ureteral ligation was measured and plotted over time. 1. The developed device facilitated imaging of the ureter, bladder, and small intestine. 2. The bladder's average fluorescence intensity exhibited changes over time in response to urine production and ureteral ligation, contrasting with observations without ureteral ligation. We have successfully constructed and optimized a modular fluorescence imaging system for organs and tissues. This system proves effective in imaging experiments involving hollow organs in animals and offers valuable insights for relevant surgical procedures.

Decomposition Technique for Bio-Transmittance Imaging Based on Attenuation Coefficient Matrix Inverse.

Human body tissue disease diagnosis will become more accurate if transmittance images, such as X-ray images, are separated according to each constituent tissue. This research proposes a new image decomposition technique based on the matrix inverse method for biological tissue images. The fundamental idea of this research is based on the fact that when k different monochromatic lights penetrate a biological tissue, they will experience different attenuation coefficients. Furthermore, the same happens when monochromatic light penetrates k different biological tissues, as they will also experience different attenuation coefficients. The various attenuation coefficients are arranged into a unique k×k-dimensional square matrix. k-many images taken by k-many different monochromatic lights are then merged into an image vector entity; further, a matrix inverse operation is performed on the merged image, producing N-many tissue thickness images of the constituent tissues. This research demonstrates that the proposed method effectively decomposes images of biological objects into separate images, each showing the thickness distributions of different constituent tissues. In the future, this proposed new technique is expected to contribute to supporting medical imaging analysis.

Image segmentation of mouse eye invivo with optical coherence tomography based on Bayesian classification.

Optical coherence tomography (OCT) is a new imaging technology that uses an optical analog of ultrasound imaging for biological tissues. Image segmentation plays an important role in dealing with quantitative analysis of medical images. We have proposed a novel framework to deal with the low intensity problem, based on the labeled patches and Bayesian classification (LPBC) model. The proposed method includes training and testing phases. During the training phase, firstly, we manually select the sub-images of background and Region of Interest (ROI) from the training image, and then extract features by patches. Finally, we train the Bayesian model with the features. The segmentation threshold of each patch is computed by the learned Bayesian model. In addition, we have collected a new dataset of mouse eyes invivo with OCT, named MEVOCT, which can be found at URL https://17861318579.github.io/LPBC. MEVOCT consists of 20 high-resolution images. The resolution of every image is 2048×2048 pixels. The experimental results demonstrate the effectiveness of the LPBC method on the new MEVOCT dataset. The ROI segmentation is of great importance for the distortion correction.