Biomedical Imaging Research Articles

Blazing Carbon Dots: Unfolding its Luminescence Mechanism to Photoinduced Biomedical Applications.

Carbon dots (CDs) are carbon-based nanomaterials that have garnered immense interest due to their exceptional photophysical and optoelectronic properties. They have been employed extensively for biomedical imaging and phototherapy due to their superb water dispersibility, low toxicity, outstanding biocompatibility, and exceptional tissue permeability. This review summarizes the luminescence mechanism of CDs. The modification in CDs via various doping routes is comprehensively reviewed, and the effect of such alterations on their photophysical properties, such as photoluminescence (PL), absorbance, and reactive oxygen species generation ability, is also highlighted. This review also aims to summarize the role of CDs in cellular imaging and fluorescence lifetime imaging for cellular metabolism. Subsequently, recent advancements and the future prospects of CDs as nanotheranostic agents have been discussed. Herein, we have discussed the role of CDs in photothermal, photodynamic, and synergistic therapy of anticancer, antiviral, and antibacterial applications. The overall summary of the review highlights the future prospects of CD-based research in bioimaging and biomedicine.

Ultrafast Polarization‐Maintaining Fiber Lasers: Design, Fabrication, Performance, and Applications

AbstractUltrafast polarization‐maintaining fiber lasers (UPMFLs), with superior optical performance and high immunity to environmental disturbances, are highly preferable in a variety of industrial and scientific applications such as high‐precision micromachining and biomedical imaging. Especially, the utilization of PM fibers endows the laser intrinsic stability, thereby enabling the construction of robust and low‐noise optical frequency comb systems. To meet more demanding application challenges, continuous efforts have been invested in the design and fabrication of UPMFLs, aiming to reach unprecedented levels of various pulse parameters, that is, to achieve shorter pulse duration, higher or lower repetition rate, and higher pulse energy. This review presents a detailed overview of different passive mode‐locking techniques for pulsed operation and the most significant achievements in UPMFLs. Representative advances at 1.0, 1.55, and 2.0 µm spectral regions are presented and summarized. The state‐of‐the‐art lasing performance is application‐oriented, and conversely, optical improvements in all‐PM pulsed lasers promote emerging applications, which are also discussed and analyzed. How to overcome the bottlenecks of UPMFLs in terms of pulse duration, repetition rate, emission wavelength, and pulse energy to make them powerful tools for physical, medical, and biological applications remains challenging in the future.

Cutting-Edge Machine Learning in Biomedical Image Analysis: Editorial for Bioengineering Special Issue: “Recent Advance of Machine Learning in Biomedical Image Analysis”

Biomedical image analysis plays a critical role in the healthcare system [...]

Acoustic field visualization and source localization via physics-informed learning of sparse data with adaptive sampling

Traditional methods for acoustic field visualization require considerable effort for capturing large amounts of acoustic data to achieve a high resolution field map, highly limiting their widespread use. In this study, we propose an approach for acoustic field visualization based on physics-informed neural networks (PINNs) by using a small amount of data, subsequently realizing accurate acoustic source localization. First, we present a PINN model integrated with an acoustic Helmholtz equation and adaptive sampling, the performance of which is testified via numerical simulations. The “no mesh” character of PINN enables achieving high resolution acoustic field visualization without requiring the capture of numerous data in advance. Furthermore, we experimentally validate the performance of the proposed method, which demonstrates that the acoustic sources can be precisely localized with sparse field data acquisition within a small area. This work would find potential applications in various acoustics, such as acoustic communication, biomedical imaging, and virtual reality.

Design of eye phantom for optical coherence tomography angiography study.

Tissue phantoms play an important role in validating biomedical imaging techniques. For optical imaging systems such as optical coherence tomography, creating layers of phantoms with different refractive indices is important. This paper explored various phantom materials to create eye-mimicking phantoms. The phantom also included microfluidic channels to mimic vasculature. To verify the optical properties of the created phantom, the transmission and reflection spectroscopy was performed using a photoluminescent spectrometer studying the complete visible and near infrared spectrum. We created a 3D model to house multiple layers that mimicked the retina at one end and the contact lens at the other, representing the anterior segment of the eye. A realistic retinal layer-mimicking, which, among other things, may pave the way for new combined imaging modalities.

Enhancing the Security and Efficiency of Biomedical Image Encryption through a Novel Hyper-Chaotic Logistic Map

Enhancing the Security and Efficiency of Biomedical Image Encryption through a Novel Hyper-Chaotic Logistic Map

Optical Performance of Quantum Dots and Their Applications in Biomedical Imaging

Quantum dots are nanomaterials which have attracted significant focus over the past few years because of their singular optical features. They have shown great potential in the field of bioimaging. This study delves into the photonic properties of quantum dots which include good luminescence stability and broad excitation spectra. Besides, the emission wavelength can be precisely controlled. These properties make them an excellent choice for bio-imaging. The usage of quantum dots in live organism imaging, such as pinpointing cell boundaries, tracking intracellular molecular dynamics, and early disease diagnosis, indicate the immense potential of quantum dots in biomedical realm. Through modifications on the surface of quantum dots, targeted imaging can be realized, which greatly improves the specificity and sensitivity of imaging. Nonetheless, the biosafety and long-term in vivo behavior of quantum dots still need to be further investigated. Overall, the light-based attributes of quantum dots have brought breakthroughs in bio-imaging technology. In addiiton, it would foretell a bright future for quantum dots in the biomedical field.

Intramolecular/Intermolecular Sequential Cyclization Accompanied by Double C-F Bond Cleavage: Access to Tricyclic Fluorine-Containing Pyrano[3,2-c]chromenes.

Defluorinative cyclization of CF3-alkenes has emerged as a reliable strategy for crafting intricate polycyclic frameworks. In this study, a facile defluorinative bicyclization approach was developed for the construction of 4H,5H-pyrano[3,2-c]chromenes under mild conditions involving a sequence of intramolecular cyclization and intermolecular defluoroheterocyclization. A variety of polysubstituted 4H,5H-pyrano[3,2-c]chromenes featuring C2-fluorine could be synthesized in good yields with excellent tolerance toward various functional groups. Moreover, the introduction of a C-F bond provides additional possibilities for further modification of this skeleton. The product features aggregation-induced emission (AIE) characteristics after simple modification, which is promising for chemical and biomedical imaging.

VCU-Net: a vascular convolutional network with feature splicing for cerebrovascular image segmentation.

Cerebrovascular image segmentation is one of the crucial tasks in the field of biomedical image processing. Due to the variable morphology of cerebral blood vessels, the traditional convolutional kernel is weak in perceiving the structure of elongated blood vessels in the brain, and it is easy to lose the feature information of the elongated blood vessels during the network training process. In this paper, a vascular convolutional U-network (VCU-Net) is proposed to address these problems. This network utilizes a new convolution (vascular convolution) instead of the traditional convolution kernel, to extract features of elongated blood vessels in the brain with different morphologies and orientations by adaptive convolution. In the network encoding stage, a new feature splicing method is used to combine the feature tensor obtained through vascular convolution with the original tensor to provide richer feature information. Experiments show that the DSC and IOU of the proposed method are 53.57% and 69.74%, which are improved by 2.11% and 2.01% over the best performance of the GVC-Net among several typical models. In image visualization, the proposed network has better segmentation performance for complex cerebrovascular structures, especially in dealing with elongated blood vessels in the brain, which shows better integrity and continuity.

Enhanced Singlet Oxygen Generation in Aggregates of Naphthalene-Fused BODIPY and Its Application in Photodynamic Therapy.

Several reports are available on aggregation-induced emission and its applications in biomedical imaging and other material sciences. However, enhancement of singlet oxygen generation in nanoaggregates is rarely reported. Here, we report the synthesis of Naph-BODIPY Br2, which absorbs at 661 nm (monomer) with a high molar absorption coefficient. The presence of bromine promotes intersystem crossing, thereby enhancing the singlet oxygen quantum yield (ΦΔ ∼ 0.50 in methanol). In order to increase hydrophilicity, we developed Naph-BODIPY Br2 nanoaggregates (∼100 nm), which demonstrated aggregation-induced properties and exhibited a bathochromic shift with an absorption maximum at 757 nm. The bathochromic shift in the UV-vis spectra due to aggregation is corroborated by TD-DFT analysis. The computational data also confirm the presence of a low-lying triplet state, which enhances the generation of singlet oxygen, making it effective for photodynamic therapy. These aggregates showed excellent singlet oxygen generation in aqueous media, compared to their monomeric form and standard methylene blue. Their hydrophilic nature and high singlet oxygen generation enabled significant phototoxicity against human carcinoma cells with IC50 values of 4.06 ± 0.01 and 4.09 ± 0.1 μM, respectively, for MCF-7 and A549 cells upon 5 min exposure to light. Moreover, their phototoxicity further increases with an increasing exposure time of light for both cell lines. Notably, Naph-BODIPY Br2 nanoaggregates exhibited nearly zero dark cell toxicity and effectively induced apoptosis in cancer cells upon light activation, highlighting their potential as powerful photosensitizers for photodynamic cancer therapy.

Multivariate Cluster Point Process to Quantify and Explore Multi-Entity Configurations: Application to Biofilm Image Data.

Clusters of similar or dissimilar objects are encountered in many fields. Frequently used approaches treat each cluster's central object as latent. Yet, often objects of one or more types cluster around objects of another type. Such arrangements are common in biomedical images of cells, in which nearby cell types likely interact. Quantifying spatial relationships may elucidate biological mechanisms. Parent-offspring statistical frameworks can be usefully applied even when central objects ("parents") differ from peripheral ones ("offspring"). We propose the novel multivariate cluster point process (MCPP) to quantify multi-object (e.g., multi-cellular) arrangements. Unlike commonly used approaches, the MCPP exploits locations of the central parent object in clusters. It accounts for possibly multilayered, multivariate clustering. The model formulation requires specification of which object types function as cluster centers and which reside peripherally. If such information is unknown, the relative roles of object types may be explored by comparing fit of different models via the deviance information criterion (DIC). In simulated data, we compared a series of models' DIC; the MCPP correctly identified simulated relationships. It also produced more accurate and precise parameter estimates than the classical univariate Neyman-Scott process model. We also used the MCPP to quantify proposed configurations and explore new ones in human dental plaque biofilm image data. MCPP models quantified simultaneous clustering of Streptococcus and Porphyromonas around Corynebacterium and of Pasteurellaceae around Streptococcus and successfully captured hypothesized structures for all taxa. Further exploration suggested the presence of clustering between Fusobacterium and Leptotrichia, a previously unreported relationship.

Enhancing biomedical imaging: the role of nanoparticle-based contrast agents.

Biomedical imaging plays a critical role in early detection, precise diagnosis, treatment planning, and monitoring responses, but traditional methods encounter challenges such as limited sensitivity, specificity, and inability to monitor therapeutic responses due to factors like short circulation half-life and potential toxicity. Nanoparticles are revolutionizing biomedical imaging as contrast agents across modalities like computed tomography (CT), optical, magnetic resonance imaging (MRI), and ultrasound, exploiting unique attributes such as those of metal-based, polymeric, and lipid nanoparticles. They shield imaging agents from immune clearance, extending circulation time, and enhancing bioavailability at tumor sites. This results in improved imaging sensitivity. The study highlights advancements in multifunctional nanoparticles for targeted imaging, tackling concerns regarding toxicity and biocompatibility. Critically evaluating conventional contrast agents, emphasizes the shortcomings that nanoparticles aim to overcome. This review provides insight into the current status of nanoparticle-based contrast agents, illuminating their potential to reshape therapeutic monitoring and precision diagnostics.

Cr3+-Doped LiAlO2 NIR-I Emitting Phosphors with Superior Resistance to Thermal Quenching for Night Vision Monitoring and Bioimaging.

Near-infrared phosphor-converted light-emitting diodes (NIR pc-LEDs) as a new NIR light source has outstanding application potential in the fields of NIR spectroscopy analysis, sensing, imaging, and pattern recognition. Therefore, the development of NIR phosphors with good NIR luminescence thermal stability has attracted much attention. To this end, we developed LiAlO2:Cr3+ garnet-type NIR phosphors by a high-temperature solid-state reaction method. Under 405 nm excitation, the Cr3+ ions located in tetrahedrons of LiAlO2 emit NIR emission in a broadband NIR emission that covers 650-900 nm mixed with several sharp narrowband R-line emissions, which showed excellent luminescence thermal stability with integrated intensity of emission measured at 573 K is ∼90.86% of that measured at 303 K caused by good structural rigidity and low thermal expansion coefficient of the matrix material. An NIR pc-LED device assembled with the optimized LiAlO2:Cr3+ and a commercially available purple LED chip emitted NIR output power of ∼98.172 mW at a driving current of 300 mA and demonstrated an electro-optical conversion efficiency of ∼9.09%, while demonstrated it has excellent application potential in the fields of night vision monitoring and biomedical imaging.

A Review: Phase Measurement Techniques Based on Metasurfaces

Phase carries crucial information about the light propagation process, and the visualization and quantitative measurement of phase have important applications, ranging from ultra-precision metrology to biomedical imaging. Traditional phase measurement techniques typically require large and complex optical systems, limiting their applicability in various scenarios. Optical metasurfaces, as flat optical elements, offer a novel approach to phase measurement by manipulating light at the nanoscale through light-matter interactions. Metasurfaces are advantageous due to their lightweight, multifunctional, and easy-to-integrate nature, providing new possibilities for simplifying traditional phase measurement methods. This review categorizes phase measurement techniques into quantitative and non-quantitative methods and reviews the advancements in metasurface-based phase measurement technologies. Detailed discussions are provided on several methods, including vortex phase contrast, holographic interferometry, shearing interferometry, the Transport of Intensity Equation (TIE), and wavefront sensing. The advantages and limitations of metasurfaces in phase measurement are highlighted, and future research directions are explored.

Overcoming photon and spatiotemporal sparsity in fluorescence lifetime imaging with SparseFLIM.

Fluorescence lifetime imaging microscopy (FLIM) provides quantitative readouts of biochemical microenvironments, holding great promise for biomedical imaging. However, conventional FLIM relies on slow photon counting routines to accumulate sufficient photon statistics, restricting acquisition speeds. Here we demonstrate SparseFLIM, an intelligent paradigm for achieving high-fidelity FLIM reconstruction from sparse photon measurements. We develop a coupled bidirectional propagation network that enriches photon counts and recovers hidden spatial-temporal information. Quantitative analysis shows over tenfold photon enrichment, dramatically improving signal-to-noise ratio, lifetime accuracy, and correlation compared to the original sparse data. SparseFLIM enables reconstructing spatially and temporally undersampled FLIM at full resolution and channel count. The model exhibits strong generalization across experimental modalities including multispectral FLIM and in vivo endoscopic FLIM. This work establishes deep learning as a promising approach to enhance fluorescence lifetime imaging and transcend limitations imposed by the inherent codependence between measurement duration and information content.

Broadband and parallel multiple-order optical spatial differentiation enabled by Bessel vortex modulated metalens

Optical analog image processing technology is expected to provide an effective solution for high-throughput and real-time data processing with low power consumption. In various operations, optical spatial differential operations are essential in edge extraction, data compression, and feature classification. Unfortunately, existing methods can only perform low-order or selectively perform a particular high-order differential operation. Here, we propose and experimentally demonstrate a Bessel vortex modulated metalens composed of a single complex amplitude metasurface, which can perform multiple-order radial differential operations over a wide band by presetting the order of the corresponding Bessel vortex. This architecture further enables angle multiplexing to create multiple information channels that synchronously perform multi-order spatial differential operations, indicating the superiority of the proposed devices in parallel processing. Our approach may find various applications in artificial intelligence, machine vision, autonomous driving, and advanced biomedical imaging.

Multi-scale dual-channel feature embedding decoder for biomedical image segmentation

Background and Objective:Attaining global context along with local dependencies is of paramount importance for achieving highly accurate segmentation of objects from image frames and is challenging while developing deep learning-based biomedical image segmentation. Several transformer-based models have been proposed to handle this issue in biomedical image segmentation. Despite this, segmentation accuracy remains an ongoing challenge, as these models often fall short of the target range due to their limited capacity to capture critical local and global contexts. However, the quadratic computational complexity is the main limitation of these models. Moreover, a large dataset is required to train those models. Methods:In this paper, we propose a novel multi-scale dual-channel decoder to mitigate this issue. The complete segmentation model uses two parallel encoders and a dual-channel decoder. The encoders are based on convolutional networks, which capture the features of the input images at multiple levels and scales. The decoder comprises a hierarchy of Attention-gated Swin Transformers with a fine-tuning strategy. The hierarchical Attention-gated Swin Transformers implements a multi-scale, multi-level feature embedding strategy that captures short and long-range dependencies and leverages the necessary features without increasing computational load. At the final stage of the decoder, a fine-tuning strategy is implemented that refines the features to keep the rich features and reduce the possibility of over-segmentation. Results:The proposed model is evaluated on publicly available LiTS, 3DIRCADb, and spleen datasets obtained from Medical Segmentation Decathlon. The model is also evaluated on a private dataset from Medical College Kolkata, India. We observe that the proposed model outperforms the state-of-the-art models in liver tumor and spleen segmentation in terms of evaluation metrics at a comparative computational cost. Conclusion:The novel dual-channel decoder embeds multi-scale features and creates a representation of both short and long-range contexts efficiently. It also refines the features at the final stage to select only necessary features. As a result, we achieve better segmentation performance than the state-of-the-art models.

Recent Advances in Degradable Biomedical Polymers for Prevention, Diagnosis and Treatment of Diseases.

Biomedical polymers play a key role in preventing, diagnosing, and treating diseases, showcasing a wide range of applications. Their unique advantages, such as rich source, good biocompatibility, and excellent modifiability, make them ideal biomaterials for drug delivery, biomedical imaging, and tissue engineering. However, conventional biomedical polymers suffer from poor degradation in vivo, increasing the risks of bioaccumulation and potential toxicity. To address these issues, degradable biomedical polymers can serve as an alternative strategy in biomedicine. Degradable biomedical polymers can efficiently relieve bioaccumulation in vivo and effectively reduce patient burden in disease management. This review comprehensively introduces the classification and properties of biomedical polymers and the recent research progress of degradable biomedical polymers in various diseases. Through an in-depth analysis of their classification, properties, and applications, we aim to provide strong guidance for promoting basic research and clinical translation of degradable biomedical polymers.

Monolayer MoS2 and WS2 for Vertical Circular‐ Polarized‐Light‐Emitting Diode: from Fundamental Understanding to Device Architecture

AbstractLight‐emitting diodes (LEDs) have revolutionized lighting and displays due to their numerous advantages over conventional lighting mechanisms. Moreover, the directional nature of luminescent materials has spurred significant advancements in the development of circularly polarized LEDs, which hold transformative potential for applications in biomedical imaging, liquid crystal displays, spintronics, and valleytronics. The performance of circularly polarized LEDs mainly depends on the emitter material, which is this study's focus. In particular, semiconducting‐phase 2D monolayer MoS2 and WS2 are attractive emitter‐material candidates owing to their bandgap versatility, high carrier mobility, high exciton binding energy, polarized‐light‐emission properties, and unique spin–valley coupling. Several works have examined the fundamental light‐emission properties of monolayer MoS2 and WS2 from the perspectives of optoelectronic concepts, material fabrication, and device construction. This paper presents approaches to control, tune, and enhance these properties of monolayer MoS2 and WS2. Possible guidelines for monolayer‐material synthesis (top‐down and bottom‐up approaches) and device engineering of vertically stacked MoS2 and WS2 are presented. Finally, the review considers the material topological characteristics, outlines the challenges and potential of monolayer MoS2 and WS2 for developing high‐performance commercial circularly polarized LED devices, and proposes a technological roadmap for leveraging other monolayer transition metal dichalcogenide systems in optoelectronic devices.

A Review: Laser Interference Lithography for Diffraction Gratings and Their Applications in Encoders and Spectrometers

The unique diffractive properties of gratings have made them essential in a wide range of applications, including spectral analysis, precision measurement, optical data storage, laser technology, and biomedical imaging. With advancements in micro- and nanotechnologies, the demand for more precise and efficient grating fabrication has increased. This review discusses the latest advancements in grating manufacturing techniques, particularly highlighting laser interference lithography, which excels in sub-beam generation through wavefront and amplitude division. Techniques such as Lloyd’s mirror configurations produce stable interference fringe fields for grating patterning in a single exposure. Orthogonal and non-orthogonal, two-axis Lloyd’s mirror interferometers have advanced the fabrication of two-dimensional gratings and large-area gratings, respectively, while laser interference combined with concave lenses enables the creation of concave gratings. Grating interferometry, utilizing optical interference principles, allows for highly precise measurements of minute displacements at the nanometer to sub-nanometer scale. This review also examines the application of grating interferometry in high-precision, absolute, and multi-degree-of-freedom measurement systems. Progress in grating fabrication has significantly advanced spectrometer technology, with integrated structures such as concave gratings, Fresnel gratings, and grating–microlens arrays driving the miniaturization of spectrometers and expanding their use in compact analytical instruments.