BEATS: BEAmline for synchrotron X-ray microTomography at SESAME.

The ID10 beamline of the SESAME (Synchrotron-light for Experimental Science and Applications in the Middle East) synchrotron light source in Jordan was inaugurated in June 2023 and is now open to scientific users. The beamline, which was designed and installed within the European Horizon 2020 project BEAmline for Tomography at SESAME (BEATS), provides full-field X-ray radiography and microtomography imaging with monochromatic or polychromatic X-rays up to photon energies of 100 keV. The photon source generated by a 2.9 T wavelength shifter with variable gap, and a double-multilayer monochromator system allow versatile application for experiments requiring either an X-ray beam with high intensity and flux, and/or a partially spatial coherent beam for phase-contrast applications. Sample manipulation and X-ray detection systems are designed to allow scanning samples with different size, weight and material, providing image voxel sizes from 13 µm down to 0.33 µm. A state-of-the-art computing infrastructure for data collection, three-dimensional (3D) image reconstruction and data analysis allows the visualization and exploration of results online within a few seconds from the completion of a scan. Insights from 3D X-ray imaging are key to the investigation of specimens from archaeology and cultural heritage, biology and health sciences, materials science and engineering, earth, environmental sciences and more. Microtomography scans and preliminary results obtained at the beamline demonstrate that the new beamline ID10-BEATS expands significantly the range of scientific applications that can be targeted at SESAME.

Functional hydrogels for hepatocellular carcinoma: therapy, imaging, and in vitro model.

Hepatocellular carcinoma (HCC) is among the most common malignancies worldwide and is characterized by high rates of morbidity and mortality, posing a serious threat to human health. Interventional embolization therapy is the main treatment against middle- and late-stage liver cancer, but its efficacy is limited by the performance of embolism, hence the new embolic materials have provided hope to the inoperable patients. Especially, hydrogel materials with high embolization strength, appropriate viscosity, reliable security and multifunctionality are widely used as embolic materials, and can improve the efficacy of interventional therapy. In this review, we have described the status of research on hydrogels and challenges in the field of HCC therapy. First, various preparation methods of hydrogels through different cross-linking methods are introduced, then the functions of hydrogels related to HCC are summarized, including different HCC therapies, various imaging techniques, in vitro 3D models, and the shortcomings and prospects of the proposed applications are discussed in relation to HCC. We hope that this review is informative for readers interested in multifunctional hydrogels and will help researchers develop more novel embolic materials for interventional therapy of HCC.

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review.

Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.

Miniature Two-Photon Microscopic Imaging Using Dielectric Metalens.

Miniature two-photon microscopy has emerged as a powerful technique for investigating brain activity in freely moving animals. Ongoing research objectives include reducing probe weight and minimizing animal behavior constraints caused by probe attachment. Employing dielectric metalenses, which enable the use of sizable optical components in flat device structures while maintaining imaging resolution, is a promising solution for addressing these challenges. In this study, we designed and fabricated a titanium dioxide metalens with a wavelength of 920 nm and a high aspect ratio. Furthermore, a meta-optic two-photon microscope weighing 1.36 g was developed. This meta-optic probe has a lateral resolution of 0.92 µm and an axial resolution of 18.08 µm. Experimentally, two-photon imaging of mouse brain structures in vivo was also demonstrated. The flat dielectric metalens technique holds promising opportunities for high-performance integrated miniature nonlinear microscopy and endomicroscopy platforms in the biomedical field.

Nanoengineered Silica-Based Biomaterials for Regenerative Medicine.

The paradigm of regenerative medicine is undergoing a transformative shift with the emergence of nanoengineered silica-based biomaterials. Their unique confluence of biocompatibility, precisely tunable porosity, and the ability to modulate cellular behavior at the molecular level makes them highly desirable for diverse tissue repair and regeneration applications. Advancements in nanoengineered silica synthesis and functionalization techniques have yielded a new generation of versatile biomaterials with tailored functionalities for targeted drug delivery, biomimetic scaffolds, and integration with stem cell therapy. These functionalities hold the potential to optimize therapeutic efficacy, promote enhanced regeneration, and modulate stem cell behavior for improved regenerative outcomes. Furthermore, the unique properties of silica facilitate non-invasive diagnostics and treatment monitoring through advanced biomedical imaging techniques, enabling a more holistic approach to regenerative medicine. This review comprehensively examines the utilization of nanoengineered silica biomaterials for diverse applications in regenerative medicine. By critically appraising the fabrication and design strategies that govern engineered silica biomaterials, this review underscores their groundbreaking potential to bridge the gap between the vision of regenerative medicine and clinical reality.

New insight into the application of fluorescence platforms in tumor diagnosis: From chemical basis to clinical application.

Early and rapid diagnosis of tumors is essential for clinical treatment or management. In contrast to conventional means, bioimaging has the potential to accurately locate and diagnose tumors at an early stage. Fluorescent probe has been developed as an ideal tool to visualize tumor sites and to detect biological molecules which provides a requirement for noninvasive, real-time, precise, and specific visualization of structures and complex biochemical processes in vivo. Rencently, the development of synthetic organic chemistry and new materials have facilitated the development of near-infrared small molecular sensing platforms and nanoimaging platforms. This provides a competitive tool for various fields of bioimaging such as biological structure and function imaging, disease diagnosis, in situ at the in vivo level, and real-time dynamic imaging. This review systematically focused on the recent progress of small molecular near-infrared fluorescent probes and nano-fluorescent probes as new biomedical imaging tools in the past 3-5 years, and it covers the application of tumor biomarker sensing, tumor microenvironment imaging, and tumor vascular imaging, intraoperative guidance and as an integrated platform for diagnosis, aiming to provide guidance for researchers to design and develop future biomedical diagnostic tools.

A novel Bayesian functional spatial partitioning method with application to prostate cancer lesion detection using MRI.

Spatial partitioning methods correct for nonstationarity in spatially related data by partitioning the space into regions of local stationarity. Existing spatial partitioning methods can only estimate linear partitioning boundaries. This is inadequate for detecting an arbitrarily shaped anomalous spatial region within a larger area. We propose a novel Bayesian functional spatial partitioning (BFSP) algorithm, which estimates closed curves that act as partitioning boundaries around anomalous regions of data with a distinct distribution or spatial process. Our method utilizes transitions between a fixed Cartesian and moving polar coordinate system to model the smooth boundary curves using functional estimation tools. Using adaptive Metropolis-Hastings, the BFSP algorithm simultaneously estimates the partitioning boundary and the parameters of the spatial distributions within each region. Through simulation we show that our method is robust to shape of the target zone and region-specific spatial processes. We illustrate our method through the detection of prostate cancer lesions using magnetic resonance imaging.

Imaging Techniques in Pharmacological Precision Medicine.

Biomedical imaging is a powerful tool for medical diagnostics and personalized medicines. Examples of commonly used imaging modalities include Positron Emission Tomography (PET), Ultrasound (US), Single Photon Emission Computed Tomography (SPECT), and hybrid imaging. By combining these modalities, scientists can gain a comprehensive view and better understand physiology and pathology at the preclinical, clinical, and multiscale levels. This can aid in the accuracy of medical diagnoses and treatment decisions. Moreover, biomedical imaging allows for evaluating the metabolic, functional, and structural details of living tissues. This can be particularly useful for the early diagnosis of diseases such as cancer and for the application of personalized medicines. In the case of hybrid imaging, two or more modalities are combined to produce a high-resolution image with enhanced sensitivity and specificity. This can significantly improve the accuracy of diagnosis and offer more detailed treatment plans. In this book chapter, we showcase how continued advancements in biomedical imaging technology can potentially revolutionize medical diagnostics and personalized medicine.

Comparisons of 3D printed materials for biomedical imaging applications.

In biomedical imaging, it is desirable that custom-made accessories for restraint, anesthesia, and monitoring can be easily cleaned and not interfere with the imaging quality or analyses. With the rise of 3D printing as a form of rapid prototyping or manufacturing for imaging tools and accessories, it is important to understand which printable materials are durable and not likely to interfere with imaging applications. Here, 15 3D printable materials were evaluated for radiodensity, optical properties, simulated wear, and capacity for repeated cleaning and disinfection. Materials that were durable, easily cleaned, and not expected to interfere with CT, PET, or optical imaging applications were identified.

A guide for selecting 3D printed materials for custom research tools through characterization of their merits and limitations in biomedical imaging.

NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation.

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing indirect immunofluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is very different from the conventional neural network approaches but has an equivalent quantitative and qualitative performance, and it is also robust against adversative noise. The method is robust, based on formally correct functions, and does not suffer from having to be tuned on specific data sets. Results: This work demonstrates the robustness of the method against variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on three datasets (Neuroblastoma, NucleusSegData, and ISBI 2009 Dataset) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional and structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) in segmenting cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches.

Multi-Illumination Single-Holographic-Exposure Lensless Fresnel (MISHELF) Microscopy: Principles and Biomedical Applications.

Lensless holographic microscopy (LHM) comes out as a promising label-free technique since it supplies high-quality imaging and adaptive magnification in a lens-free, compact and cost-effective way. Compact sizes and reduced prices of LHMs make them a perfect instrument for point-of-care diagnosis and increase their usability in limited-resource laboratories, remote areas, and poor countries. LHM can provide excellent intensity and phase imaging when the twin image is removed. In that sense, multi-illumination single-holographic-exposure lensless Fresnel (MISHELF) microscopy appears as a single-shot and phase-retrieved imaging technique employing multiple illumination/detection channels and a fast-iterative phase-retrieval algorithm. In this contribution, we review MISHELF microscopy through the description of the principles, the analysis of the performance, the presentation of the microscope prototypes and the inclusion of the main biomedical applications reported so far.

Calibrating the Dice Loss to Handle Neural Network Overconfidence for Biomedical Image Segmentation.

The Dice similarity coefficient (DSC) is both a widely used metric and loss function for biomedical image segmentation due to its robustness to class imbalance. However, it is well known that the DSC loss is poorly calibrated, resulting in overconfident predictions that cannot be usefully interpreted in biomedical and clinical practice. Performance is often the only metric used to evaluate segmentations produced by deep neural networks, and calibration is often neglected. However, calibration is important for translation into biomedical and clinical practice, providing crucial contextual information to model predictions for interpretation by scientists and clinicians. In this study, we provide a simple yet effective extension of the DSC loss, named the DSC++ loss, that selectively modulates the penalty associated with overconfident, incorrect predictions. As a standalone loss function, the DSC++ loss achieves significantly improved calibration over the conventional DSC loss across six well-validated open-source biomedical imaging datasets, including both 2D binary and 3D multi-class segmentation tasks. Similarly, we observe significantly improved calibration when integrating the DSC++ loss into four DSC-based loss functions. Finally, we use softmax thresholding to illustrate that well calibrated outputs enable tailoring of recall-precision bias, which is an important post-processing technique to adapt the model predictions to suit the biomedical or clinical task. The DSC++ loss overcomes the major limitation of the DSC loss, providing a suitable loss function for training deep learning segmentation models for use in biomedical and clinical practice. Source code is available at https://github.com/mlyg/DicePlusPlus .

Lossy Image Compression in a Preclinical Multimodal Imaging Study.

The growing use of multimodal high-resolution volumetric data in pre-clinical studies leads to challenges related to the management and handling of the large amount of these datasets. Contrarily to the clinical context, currently there are no standard guidelines to regulate the use of image compression in pre-clinical contexts as a potential alleviation of this problem. In this work, the authors study the application of lossy image coding to compress high-resolution volumetric biomedical data. The impact of compression on the metrics and interpretation of volumetric data was quantified for a correlated multimodal imaging study to characterize murine tumor vasculature, using volumetric high-resolution episcopic microscopy (HREM), micro-computed tomography (µCT), and micro-magnetic resonance imaging (µMRI). The effects of compression were assessed by measuring task-specific performances of several biomedical experts who interpreted and labeled multiple data volumes compressed at different degrees. We defined trade-offs between data volume reduction and preservation of visual information, which ensured the preservation of relevant vasculature morphology at maximum compression efficiency across scales. Using the Jaccard Index (JI) and the average Hausdorff Distance (HD) after vasculature segmentation, we could demonstrate that, in this study, compression that yields to a 256-fold reduction of the data size allowed to keep the error induced by compression below the inter-observer variability, with minimal impact on the assessment of the tumor vasculature across scales.

Spleen Tissue Segmentation Algorithm for Cryo-Imaging Data.

Spleen tissue segmentation is an essential process for analyzing various immunological diseases as observed in the cryo-imaging data. Because manual labeling of the spleen tissue by human experts is not efficient, an automatic segmentation algorithm is needed. In this study, we developed a novel algorithm for automatically segmenting spleen substructures including white pulp and red pulp for the first time. The algorithm is designed for datasets created by a cryo-imaging system. This unique technology can effectively enable cellular tracking anywhere in the whole mouse with single-cell sensitivity. The proposed algorithm consists of four components: initial spleen mask creation, feature extraction, Supervised Patch-based Fuzzy c-Mean (spFCM) classification, and post-processing. The algorithm accurately and efficiently labeled spleen tissues in all experiment settings. The algorithm also improved the spleen segmentation throughput by 90 folds as compared to the manual segmentation. Moreover, we show that our novel spFCM algorithm outperformed traditional fast-learning classifiers as well as the U-Net deep-learning model in many aspects. Two major contributions of this paper are (1) an explainable algorithm for segmenting spleen tissues in cryo-images for the first time and (2) an spFCM algorithm as a new classifier. We also discussed that our work can be beneficial to researchers who work not only in the fields of graft-versus-host disease (GVHD) mouse models, but also in that of other immunological disease models where spleen analysis is essential. Future work building upon our research may lay the foundations for biomedical studies that utilize cryo-imaging technology.

Multispectral Imaging Method for Rapid Identification and Analysis of Paraffin-Embedded Pathological Tissues.

The study of the interaction between light and biological tissue is of great help in the identification of diseases as well as structural alterations in tissues. In the present study, we have developed a tissue diagnostic technique by using multispectral imaging in the visible spectrum combined with principal component analysis (PCA). We used information from the propagation of light through paraffin-embedded tissues to assess differences in the eye tissues of control mouse embryos compared to mouse embryos whose mothers were deprived of folic acid (FA), a crucial vitamin necessary for the growth and development of the fetus. After acquiring the endmembers from the multispectral images, spectral unmixing was used to identify the abundances of those endmembers in each pixel. For each acquired image, the final analysis was performed by performing a pixel-by-pixel and wavelength-by-wavelength absorbance calculation. Non-negative least squares (NNLS) were used in this research. The abundance maps obtained for the first endmember revealed vascular alterations (vitreous and choroid) in the embryos with maternal FA deficiency. However, the abundance maps obtained for the third endmember showed alterations in the texture of some tissues such as the lens and retina. Results indicated that multispectral imaging applied to paraffin-embedded tissues enhanced tissue visualization. Using this method, first, it can be seen tissue damage location and then decide what kind of biological techniques to apply.

Design, Synthesis, and Biomedical Application of Multifunctional Fluorescent Polymer Nanomaterials.

Luminescent polymer nanomaterials not only have the characteristics of various types of luminescent functional materials and a wide range of applications, but also have the characteristics of good biocompatibility and easy functionalization of polymer nanomaterials. They are widely used in biomedical fields such as bioimaging, biosensing, and drug delivery. Designing and constructing new controllable synthesis methods for multifunctional fluorescent polymer nanomaterials with good water solubility and excellent biocompatibility is of great significance. Exploring efficient functionalization methods for luminescent materials is still one of the core issues in the design and development of new fluorescent materials. With this in mind, this review first introduces the structures, properties, and synthetic methods regarding fluorescent polymeric nanomaterials. Then, the functionalization strategies of fluorescent polymer nanomaterials are summarized. In addition, the research progress of multifunctional fluorescent polymer nanomaterials for bioimaging is also discussed. Finally, the synthesis, development, and application fields of fluorescent polymeric nanomaterials, as well as the challenges and opportunities of structure-property correlations, are comprehensively summarized and the corresponding perspectives are well illustrated.

Volumetric macromolecule identification in cryo-electron tomograms using capsule networks.

BACKGROUND: Despite recent advances in cellular cryo-electron tomography (CET), developing automated tools for macromolecule identification in submolecular resolution remains challenging due to the lack of annotated data and high structural complexities. To date, the extent of the deep learning methods constructed for this problem is limited to conventional Convolutional Neural Networks (CNNs). Identifying macromolecules of different types and sizes is a tedious and time-consuming task. In this paper, we employ a capsule-based architecture to automate the task of macromolecule identification, that we refer to as 3D-UCaps. In particular, the architecture is composed of three components: feature extractor, capsule encoder, and CNN decoder. The feature extractor converts voxel intensities of input sub-tomograms to activities of local features. The encoder is a 3D Capsule Network (CapsNet) that takes local features to generate a low-dimensional representation of the input. Then, a 3D CNN decoder reconstructs the sub-tomograms from the given representation by upsampling. RESULTS: We performed binary and multi-class localization and identification tasks on synthetic and experimental data. We observed that the 3D-UNet and the 3D-UCaps had an [Formula: see text]score mostly above 60% and 70%, respectively, on the test data. In both network architectures, we observed degradation of at least 40% in the [Formula: see text]-score when identifying very small particles (PDB entry 3GL1) compared to a large particle (PDB entry 4D8Q). In the multi-class identification task of experimental data, 3D-UCaps had an [Formula: see text]-score of 91% on the test data in contrast to 64% of the 3D-UNet. The better [Formula: see text]-score of 3D-UCaps compared to 3D-UNet is obtained by a higher precision score. We speculate this to be due to the capsule network employed in the encoder. To study the effect of the CapsNet-based encoder architecture further, we performed an ablation study and perceived that the [Formula: see text]-score is boosted as network depth is increased which is in contrast to the previously reported results for the 3D-UNet. To present a reproducible work, source code, trained models, data as well as visualization results are made publicly available. CONCLUSION: Quantitative and qualitative results show that 3D-UCaps successfully perform various downstream tasks including identification and localization of macromolecules and can at least compete with CNN architectures for this task. Given that the capsule layers extract both the existence probability and the orientation of the molecules, this architecture has the potential to lead to representations of the data that are better interpretable than those of 3D-UNet.

Recent advances in positron emission particle tracking: a comparative review.

Positron emission particle tracking (PEPT) is a technique which allows the high-resolution, three-dimensional imaging of particulate and multiphase systems, including systems which are large, dense, and/or optically opaque, and thus difficult to study using other methodologies. In this work, we bring together researchers from the world's foremost PEPT facilities not only to give a balanced and detailed overview and review of the technique but, for the first time, provide a rigorous, direct, quantitative assessment of the relative strengths and weaknesses of all contemporary PEPT methodologies. We provide detailed explanations of the methodologies explored, including also interactive code examples allowing the reader to actively explore, edit and apply the algorithms discussed. The suite of benchmarking tests performed and described within the document is made available in an open-source repository for future researchers.

Bioimaging guided pharmaceutical evaluations of nanomedicines for clinical translations.

Nanomedicines (NMs) have emerged as an efficient approach for developing novel treatment strategies against a variety of diseases. Over the past few decades, NM formulations have received great attention, and a large number of studies have been performed in this field. Despite this, only about 60 nano-formulations have received industrial acceptance and are currently available for clinical use. Their in vivo pharmaceutical behavior is considered one of the main challenges and hurdles for the effective clinical translation of NMs, because it is difficult to monitor the pharmaceutic fate of NMs in the biological environment using conventional pharmaceutical evaluations. In this context, non-invasive imaging modalities offer attractive solutions, providing the direct monitoring and quantification of the pharmacokinetic and pharmacodynamic behavior of labeled NMs in a real-time manner. Imaging evaluations have great potential for revealing the relationship between the physicochemical properties of NMs and their pharmaceutical profiles in living subjects. In this review, we introduced imaging techniques that can be used for in vivo NM evaluations. We also provided an overview of various studies on the influence of key parameters on the in vivo pharmaceutical behavior of NMs that had been visualized in a non-invasive and real-time manner.

Graphene-Based Nanomaterials for Biomedical Imaging.

Graphene is sp2-hybridized carbon structure-based two-dimensional (2D) sheet. Graphene-based nanomaterials possess several features such as unique mechanical, electronic, thermal, and optical properties, high specific surface area, versatile surface functionalization, and biocompatibility, which attracted researcher's interests in various fields including biomedicine. In this chapter, we particularly focused on the biomedical imaging applications of graphene-based nanomaterials like graphene oxide (GO), reduced graphene oxide (rGO), graphene quantum dots (GQDs), graphene oxide quantum dots (GOQDs), and other derivatives, which utilize their outstanding optical properties. There are some biomedical imaging modalities using Graphene-based Nanomaterials, among which we will highlight fluorescence imaging, Raman imaging, magnetic resonance imaging, and photoacoustic imaging. We also discussed the brief perspectives and future application related to them.