Biomedical Imaging Research Articles

AI in Biomedical Imaging and Diagnostics

Advances in artificial intelligence (AI) and synthetic biology have profoundly influenced biomedical research, creating transformative opportunities in imaging, diagnostics, and therapeutic engineering. In biomedical imaging, AI-driven algorithms enhance precision and accuracy, enabling automated analysis of complex datasets, real-time imaging insights, and identification of disease biomarkers. Meanwhile, synthetic biology redefines cellular engineering, particularly in T-cell research, by enabling customized functionalities, such as precision-targeted antigen recognition and tunable immune responses. The integration of AI into T-cell engineering amplifies these capabilities, facilitating the design and optimization of synthetic circuits, predictive modeling of cellular behaviors, and dynamic monitoring of therapeutic outcomes. This interdisciplinary approach is revolutionizing diagnostics and immunotherapy by streamlining the identification of disease-specific markers, improving diagnostic accuracy, and enabling real-time modulation of T-cell functionality within the tumor microenvironment. By combining AI-powered insights with synthetic biology's ability to engineer living systems, this research aims to address critical challenges in disease treatment, including tumor heterogeneity and immune evasion. This work explores the synergistic application of AI and synthetic biology in biomedical imaging and T-cell engineering, highlighting state-of-the-art technologies, their therapeutic potential, and the future landscape of personalized medicine.

Spontaneous breaking of symmetry in overlapping cell instance segmentation using diffusion models

Abstract Instance segmentation is the task of assigning unique identifiers to individual objects in images. Solving this task requires breaking the inherent symmetry that semantically similar objects must result in distinct outputs. Deep learning algorithms bypass this break-of-symmetry by training specialized predictors or by utilizing intermediate label representations. However, many of these approaches break down when faced with overlapping labels that are ubiquitous in biomedical imaging, for instance for segmenting cell layers. Here, we discuss the reason for this failure and offer a novel approach for instance segmentation based on diffusion models that breaks this symmetry spontaneously. Our method outputs pixel-level instance segmentations matching the performance of models such as cellpose on the cellpose fluorescent cell dataset while also permitting overlapping labels.

Ultra‐Sensitive, Self‐powered, CMOS‐Compatible Near‐Infrared Photodetectors for Wide‐Ranging Applications

AbstractSelf‐powered near‐infrared (NIR) photodetectors are essential for surveillance systems, sensing in IoT electronics, facial recognition, health monitoring, optical communication networks, night vision, and biomedical imaging. However, silicon commercial detectors need external power to operate and cooling to suppress large dark currents. This work demonstrates a new class of CMOS‐compatible self‐powered NIR photodetector based on ferroelectric 5‐nm thick ZrO2 films which do not require cooling and therefore have two key advantages over Si, and at the same time have comparable performance metrics. At room‐temperature, under 940 nm wavelength illumination (1.4 mW cm−2 power density, 10 Hz repetition rate), and without any power applied, fast rise and fall times of ≈2 and 4 µs, respectively, are achieved in Al/Si/SiOx/ZrO2/ITO devices, along with responsivity, detectivity and sensitivity values of up to ≈3.4 A W−1, 1.2 × 1010 Jones and 4.2 × 103, respectively, far exceeding all other emerging self‐powered systems. Furthermore, dual‐band NIR detection is shown for different NIR wavelengths, proof‐of‐concept feasibility being demonstrated for the smart identification of NIR targets. Therefore, it is demonstrated, for the first time, that coupling together the pyroelectric effect, the photovoltaic effect, and the ferroelectric effect is a novel method to significantly enhance the performance of CMOS‐compatible ZrO2‐based self‐powered photodetectors in the NIR region.

Advances in Lanthanide‐Based NIR‐IIb Probes for In Vivo Biomedical Imaging

AbstractThe past decades have witnessed the significant development and practical interest of in vivo biomedical imaging technologies and optical materials in the second‐near infrared (NIR‐II, 1000–1700 nm) window. Imaging with the extended emission wavelength toward the long‐wavelength end (NIR‐IIb, 1500–1700 nm) further offers micrometer imaging resolution and centimeter tissue penetration depth by taking advantage of the much‐reduced photon scattering and near‐zero tissue autofluorescence background, which have become a very hot research area. This review focuses on the recent advances in the development of lanthanide‐based NIR‐IIb probes for in vivo biomedical applications. The progress including ratiometric imaging, multiplexed imaging for wide‐field and microscopy, lifetime multiplexing and sensing, persistent luminescence, and multimodal imaging is summarized. Challenges and future directions concerning the investigation of the photophysical and photochemical properties of NIR‐IIb probes, the selection of near‐infrared cameras as well as the potential extension of the NIR‐IIb imaging sub‐window are pointed out. This review will inspire readers who have a strong interest in developing optical imaging technology and long‐wavelength fluorescence probes for high‐contrast in vivo biomedical applications.

Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT). We begin with an extensive review of current machine-learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from three kidneys imaged using HiP-CT as part of the Human Organ Atlas Project. HiP-CT pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering a resolution of around 20 μm/voxel and enabling highly detailed localised zooms up to 1–2 μm/voxel without physical sectioning. We leverage the nnU-Net framework to evaluate model performance on this high-resolution dataset, using both known and novel samples, and implementing metrics tailored for vascular structures. Our comprehensive review and empirical analysis on HiP-CT data sets a new standard for evaluating machine learning models in high-resolution organ imaging. Our three experiments yielded Dice similarity coefficient (DSC) scores of 0.9523, 0.9410, and 0.8585, respectively. Nevertheless, DSC primarily assesses voxel-to-voxel concordance, overlooking several crucial characteristics of the vessels and should not be the sole metric for deciding the performance of vascular segmentation. Our results show that while segmentations yielded reasonably high scores-such as centerline DSC ranging from 0.82 to 0.88, certain errors persisted. Specifically, large vessels that collapsed due to the lack of hydrostatic pressure (HiP-CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors, particularly in significant vessels, obstruct the understanding of the structures by interrupting vascular tree connectivity. Our study establishes the benchmark across various evaluation metrics, for vascular segmentation of HiP-CT imaging data, an imaging technology that has the potential to substantively shift our understanding of human vascular networks.

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

224-fs soliton pulses generation at 1μm from ytterbium-doped fiber laser with CoTe2 nanosheets as an ultrafast modulator

Transition metal ditellurides (TMDTs) have numerous attractive properties, making them suitable for a wide range of applications. In this study, cobalt ditelluride (CoTe2) nanosheets, a promising TMDT for photonic applications, were prepared using an ultrasound-enhanced liquid phase exfoliation (LPE) method. A novel saturable absorber (SA) employing CoTe2 nanosheets was then fabricated by optically depositing them on microfiber. The nonlinear optical modulation properties of the CoTe2 SA were investigated. A high-performance 1 μm ultrafast fiber laser was demonstrated by incorporating newly developed CoTe2 nanosheets-based SA in a ring cavity ytterbium-doped fiber laser (YDFL). The dynamical behaviour of the proposed passively mode-locked YDFL in response to variations in pump optical power was investigated. The findings reveal that the device achieved a modulation depth of 2.5 %, and saturation light intensity of 30.6 MW/cm2. Moreover, a stable and robust mode-locked soliton optical pulse sequence with a fundamental repetition frequency of 3.089 MHz, and a pulse duration of 224 fs was generated at 1032 nm. The proposed YDFL, being all-fiber, compact, and cost-effective, is set to find extensive applications in various domains, including optical fiber communication, sensing, and biomedical imaging.

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.