Biomedical Imaging Technique Research Articles

Deep learning aided determination of the optimal number of detectors for photoacoustic tomography.

Photoacoustic tomography (PAT) is a non-destructive, non-ionizing, and rapidly expanding hybrid biomedical imaging technique, yet it faces challenges in obtaining clear images due to limited data from detectors or angles. As a result, the methodology suffers from significant streak artifacts and low-quality images. The integration of deep learning (DL), specifically convolutional neural networks (CNNs), has recently demonstrated powerful performance in various fields of PAT. This work introduces a post-processing-based CNN architecture named residual-dense UNet (RDUNet) to address the stride artifacts in reconstructed PA images. The framework adopts the benefits of residual and dense blocks to form high-resolution reconstructed images. The network is trained with two different types of datasets to learn the relationship between the reconstructed images and their corresponding ground truths (GTs). In the first protocol, RDUNet (identified as RDUNet I) underwent training on heterogeneous simulated images featuring three distinct phantom types. Subsequently, in the second protocol, RDUNet (referred to as RDUNet II) was trained on a heterogeneous composition of 81% simulated data and 19% experimental data. The motivation behind this is to allow the network to adapt to diverse experimental challenges. The RDUNet algorithm was validated by performing numerical and experimental studies involving single-disk, T-shape, and vasculature phantoms. The performance of this protocol was compared with the famous backprojection (BP) and the traditional UNet algorithms. This study shows that RDUNet can substantially reduce the number of detectors from 100 to 25 for simulated testing images and 30 for experimental scenarios.

Clipping spline: interactive, dynamic 4D volume clipping and analysis based on thin plate spline

Methods for seeing inside volumetric images are increasingly important with the rapid advancements in 3D and 4D (3D + time) biomedical imaging techniques. Here, we report a novel volume clipping method and its open-source implementation which enables unprecedented 4D visualization and analysis of embryonic mouse heart development with data from optical coherence tomography (OCT). Clipping a volume to extract information inside has long been a vital approach in biomedical image analysis; however, it is challenging to make a dynamic non-planar cutaway view that is simultaneously smooth, adjustable, efficient to compute, easy to control, and interactive in real time. We addressed this challenge by applying the thin plate spline (TPS) to create a new way of volume clipping, called the clipping spline. Specifically, the clipping spline produces a cutaway view by generating a binary mask based on the unique TPS surface that intersects with a set of 3D control points while having minimal curvature. We implemented this method in an open-source platform where the clipping spline can be interactively controlled for real-time, adjustable, and dynamic cutaway views into a volume. We also developed an algorithm that automatically connects and interpolates different sets of control points over time, providing 4D volume clipping. In addition to characterizing the clipping spline, we demonstrate its application by revealing a series of never-before-seen dynamics and processes of embryonic mouse heart development based on OCT data. We also show a TPS-based method for tracking the embryonic myocardium with control points over two timescales (heartbeat and development). Our results indicate that the clipping spline promises to be broadly used in volumetric biomedical image visualization and analysis, especially by the OCT community.

Photoacoustic Imaging of pH-Sensitive Optical Sensors in Biological Tissues

Photoacoustic imaging is an emerging biomedical imaging technique that enables non-invasive visualization of the optical absorption properties of biological tissues in vivo. Although numerous studies have used contrast agents to achieve high-contrast imaging in deep tissues, targeting specific areas remains a challenge when using agents that are continuously activated. Recent research has focused on developing triggered contrast agents that are selectively activated in target areas. This review delves into the use of pH-triggered contrast agents in photoacoustic imaging, which take advantage of the lower pH of the tumor microenvironment compared to normal tissues. The paper discusses the mechanisms of pH-triggered contrast agents that contribute to improving depth and contrast in photoacoustic tumor imaging. In addition, the integration of functionalities, such as photothermal therapy and drug delivery monitoring, into these agents demonstrates significant potential for biomedical applications.

Analysis of the Relationship Between Scale Invariant Feature Transform Keypoint Properties and Their Invariance to Geometrical Transformation Applied to Cone-Beam Computed Tomography Images.

Image registration is a crucial post-processing technique in biomedical imaging, enabling the alignment and integration of images from various sources to facilitate accurate diagnosis, treatment planning, and longitudinal studies. This paper explores the application of Scale Invariant Feature Transform (SIFT), a robust feature-based method for the alignment of biomedical images. SIFT is particularly advantageous due to its invariance to scale, rotation, and affine transformations, making it well-suited for handling the diverse and complex nature of biomedical images. However, SIFT was not initially developed specifically for medical imaging applications, so it is necessary to adapt the algorithm to those kinds of images. In particular, this work was focused on images obtained with Cone-Beam Computed Tomography (CBCT) technology. Besides fine-tuning SIFT parameters on a case-by-case basis, the novelty of this work consists of finding the optimal SIFT parameters on the basis of the keypoints stability. A statistical analysis throughout a dataset of images obtained with CBCT technology was performed to find the best SIFT parameters setting, in terms of computational cost and result quality, compared to default presets.

Resolution Enhancement Strategies in Photoacoustic Microscopy: A Comprehensive Review.

Photoacoustic imaging has emerged as a promising modality for medical imaging since its introduction. Photoacoustic microscopy (PAM), which is based on the photoacoustic effect, combines the advantages of both optical and acoustic imaging modalities. PAM facilitates high-sensitivity, high-resolution, non-contact, and non-invasive imaging by employing optical absorption as its primary contrast mechanism. The ability of PAM to specifically image parameters such as blood oxygenation and melanin content makes it a valuable addition to the suite of modern biomedical imaging techniques. This review aims to provide a comprehensive overview of the diverse technical approaches and methods employed by researchers to enhance the resolution of photoacoustic microscopy. Firstly, the fundamental principles of the photoacoustic effect and photoacoustic imaging will be presented. Subsequently, resolution enhancement methods for both acoustic-resolution photoacoustic microscopy (AR-PAM) and optical-resolution photoacoustic microscopy (OR-PAM) will be discussed independently. Finally, the aforementioned resolution enhancement methods for photoacoustic microscopy will be critically evaluated, and the current challenges and future prospects of this technology will be summarized.

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.

Advancements in Nanoporous Materials for Biomedical Imaging and Diagnostics.

This review explores the latest advancements in nanoporous materials and their applications in biomedical imaging and diagnostics. Nanoporous materials possess unique structural features, including high surface area, tunable pore size, and versatile surface chemistry, making them highly promising platforms for a range of biomedical applications. This review begins by providing an overview of the various types of nanoporous materials, including mesoporous silica nanoparticles, metal-organic frameworks, carbon-based materials, and nanoporous gold. The synthesis method for each material, their current research trends, and prospects are discussed in detail. Furthermore, this review delves into the functionalization and surface modification techniques employed to tailor nanoporous materials for specific biomedical imaging applications. This section covers chemical functionalization, bioconjugation strategies, and surface coating and encapsulation methods. Additionally, this review examines the diverse biomedical imaging techniques enabled by nanoporous materials, such as fluorescence imaging, magnetic resonance imaging (MRI), computed tomography (CT) imaging, ultrasound imaging, and multimodal imaging. The mechanisms underlying these imaging techniques, their diagnostic applications, and their efficacy in clinical settings are thoroughly explored. Through an extensive analysis of recent research findings and emerging trends, this review underscores the transformative potential of nanoporous materials in advancing biomedical imaging and diagnostics. The integration of interdisciplinary approaches, innovative synthesis techniques, and functionalization strategies offers promising avenues for the development of next-generation imaging agents and diagnostic tools with enhanced sensitivity, specificity, and biocompatibility.

HiViPore: a highly viable in-flow compression for a one-step cell mechanoporation in microfluidics to induce a free delivery of nano- macro-cargoes

BackgroundAmong mechanoporation techniques for intracellular delivery, microfluidic approaches succeed in high delivery efficiency and throughput. However, especially the entry of large cargoes (e.g. DNA origami, mRNAs, organic/inorganic nanoparticles) is currently impaired since it requires large cell membrane pores with the need to apply multi-step processes and high forces, dramatically reducing cell viability.ResultsHere, HiViPore presents as a microfluidic viscoelastic contactless compression for one-step cell mechanoporation to produce large pores while preserving high cell viability. Inducing an increase of curvature at the equatorial region of cells, formation of a pore with a size of ~ 1 μm is obtained. The poration is coupled to an increase of membrane tension, measured as a raised fluorescence lifetime of 12% of a planarizable push-pull fluorescent probe (Flipper-TR) labelling the cell plasma membrane. Importantly, the local disruptions of cell membrane are transient and non-invasive, with a complete recovery of cell integrity and functions in ~ 10 min. As result, HiViPore guarantees cell viability as high as ~ 90%. In such conditions, an endocytic-free diffusion of large nanoparticles is obtained with typical size up to 500 nm and with a delivery efficiency up to 12 times higher than not-treated cells.ConclusionsThe proposed one-step contactless mechanoporation results in an efficient and safe approach for advancing intracellular delivery strategies. In detail, HiViPore solves the issues of low cell viability when multiple steps of poration are required to obtain large pores across the cell plasma membrane. Moreover, the compression uses a versatile, low-cost, biocompatible viscoelastic fluid, thus also optimizing the operational costs. With HiViPore, we aim to propose an easy-to-use microfluidic device to a wide range of users, involved in biomedical research, imaging techniques and nanotechnology for intracellular delivery applications in cell engineering.

Sparse sampling photoacoustic reconstruction with a graph regularization group sparse dictionary

Photoacoustic tomography (PAT) has emerged as a promising biomedical imaging technique. The combination of optical contrast and ultrasound spatial resolution in photoacoustic tomography overcomes the limitations of optical scattering, enabling clear imaging of tissue structures. However, achieving high-resolution photoacoustic images typically requires a large number of sensor detection elements for sufficient angular coverage. This demand for extensive data acquisition and processing raises concerns about efficiency and system complexity. While sparse sampling strategies can improve efficiency, preserving detailed structural information becomes challenging with a minimal number of detectors. To address the challenges of sparse sampling, compressed sensing (CS) techniques have been successfully applied for image reconstructions in 2D and 3D photoacoustic embodiments. In this context, we propose a joint graph regularization group sparse dictionary and total variational regularization (GRGS-TV) algorithm based on our previous work of a group sparse dictionary. It preserves structured information and geometric relationships among dictionary atoms. Moreover, TV regularization effectively preserves edge structures while exhibiting a certain degree of robustness and flexibility. Numerical simulations and in vivo experiments on mice validate the effectiveness of this method in improving photoacoustic image quality and suppressing artifacts. Comparative evaluations against other algorithms show enhanced performance in terms of image reconstruction evaluation indices. This innovative approach holds promise for advancing photoacoustic imaging in biomedical research and clinical diagnostics.

Challenges and advances in two-dimensional photoacoustic computed tomography: a review.

Photoacoustic computed tomography (PACT), a hybrid imaging modality combining optical excitation with acoustic detection, has rapidly emerged as a prominent biomedical imaging technique. We review the challenges and advances of PACT, including (1)limited view, (2)anisotropy resolution, (3)spatial aliasing, (4)acoustic heterogeneity (speed of sound mismatch), and (5)fluence correction of spectral unmixing. We performed a comprehensive literature review to summarize the key challenges in PACT toward practical applications and discuss various solutions. There is a wide range of contributions from both industry and academic spaces. Various approaches, including emerging deep learning methods, are proposed to improve the performance of PACT further. We outline contemporary technologies aimed at tackling the challenges in PACT applications.

Innovative methodology for noninvasive spatial mapping of gold nanoparticle distribution in tissues: potential applications in biomedical imaging and therapy.

Gold nanoparticles (AuNPs) have emerged as versatile agents in biomedical applications, particularly for enhancing contrast in tagged biological tissues for tumor imaging and diagnostics due to their strong absorption cross-section. In this study, we present a methodology for quantifying the spatial distribution of AuNPs within superficial tissue volumes. Utilizing silicone tissue phantoms as a background medium and spatial frequency domain imaging (SFDI) to measure the tissues' optical properties, we constructed a lookup table (LUT) to infer the optical properties of embedded AuNPs with varying spatial concentrations and depths across multiple spatial frequencies. An analytical solution derived from the LUT facilitated the determination of embedded NP concentration in-depth as a function of measured spatial frequency-dependent optical absorption. Notably, SFDI enabled the spatial localization of NPs in three dimensions. These findings lay the foundation for future in vivo studies on mapping NPs and hold significant promise for advancing biomedical imaging techniques.

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.

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.

Research on Human Lung Impedance Tomography Based on Soft Thresholding Image Segmentation and Reduced-Order Tikhonov Regularization

The lung is one of the most vital organs in the human body, and its condition is closely correlated with overall health. Electrical impedance tomography (EIT), as a biomedical imaging technique, often produces low-quality reconstructed images due to its inherent ill-posedness in solving the inverse problem. To address this issue, this paper proposes a soft-threshold region segmentation algorithm with a relaxation factor. This algorithm segments the reconstructed lung images into internal regions, edge regions, and background regions, resulting in clearer boundaries in the reconstructed images. This facilitates the intuitive identification of regions of interest by healthcare professionals. Additionally, this segmentation algorithm is suitably combined with a dimension-reduced Tikhonov regularization algorithm. By utilizing the joint capabilities of these algorithms, the partition points belonging to the background region can be excluded from the sought grayscale vector, thereby improving the ill-posedness of the image reconstruction process and enhancing the quality of image reconstruction. Finally, a 16-electrode human lung EIT simulation model is established for the thoracic region and verified through simulation. Experimental validation is conducted using a human lung tank simulation platform to further demonstrate the effectiveness of the proposed method.

Oxygenation heterogeneity facilitates spatiotemporal flow pattern visualization inside human blood vessels using photoacoustic computed tomography

Hemodynamics can be explored through various biomedical imaging techniques. However, observing transient spatiotemporal variations in the saturation of oxygen (sO2) within human blood vessels proves challenging with conventional methods. In this study, we employed photoacoustic computed tomography (PACT) to reconstruct the evolving spatiotemporal patterns in a human vein. Through analysis of the multi-wavelength photoacoustic (PA) spectrum, we illustrated the dynamic distribution within blood vessels. Additionally, we computationally rendered the dynamic process of venous blood flowing into the major vein and entering a branching vessel. Notably, we successfully recovered, in real time, the parabolic wavefront profile of laminar flow inside a deep vein in vivo—a first-time achievement. While the study is preliminary, the demonstrated capability of dynamic sO2 imaging holds promise for new applications in biology and medicine.

Enhanced contrast imaging with polyamide 6/Fe(OH)3 nanofibrous scaffolds: A focus on high T1 relaxivity

Nanofibers serve as widely employed tissue engineering scaffolds in diverse biomedical applications. When implanted in vivo, it is crucial for tissue engineering scaffolds to be visualizable, enabling the monitoring of their shape, position, and performance. This capability facilitates the effective assessment of implant deformations, displacements, degradations, and functionalities. However, in many biomedical imaging techniques such as magnetic resonance imaging (MRI), the contrast of tissue engineering scaffolds is often inadequate. MRI is particularly notable for its effectiveness in imaging soft tissues. Previous endeavors to enhance the contrast of tissue engineering scaffolds in MRI have involved the use of negative contrast agents (CAs). Nonetheless, negative CAs can result in artifacts, thus favoring the preference for positive CAs due to their ability to generate clearer boundaries. In this study, we successfully prepared composite polyamide 6 nanofibrous scaffolds with ultrafine dispersion Fe(OH)3 nanoparticles using electrospinning and in-situ growth techniques. The relaxation properties of the magnetic nanofibrous scaffolds confirmed the successful production of scaffolds suitable for positive imaging. In vitro cell seeding experiments demonstrated the efficient proliferation and adhesion of endothelial cells and fibroblasts. In vivo studies further revealed the biocompatibility and functionality of the scaffolds. These findings indicate that the prepared PA6/Fe(OH)3 composite nanofibrous scaffolds can enable straightforward, safe, and efficient in vivo positive contrast MRI monitoring, thereby playing a pivotal role in the integration of diagnosis and treatment within tissue engineering scaffolds.

Decoding phenotypic screening: A comparative analysis of image representations

Biomedical imaging techniques such as high content screening (HCS) are valuable for drug discovery, but high costs limit their use to pharmaceutical companies. To address this issue, The JUMP-CP consortium released a massive open image dataset of chemical and genetic perturbations, providing a valuable resource for deep learning research. In this work, we aim to utilize the JUMP-CP dataset to develop a universal representation model for HCS data, mainly data generated using U2OS cells and CellPainting protocol, using supervised and self-supervised learning approaches. We propose an evaluation protocol that assesses their performance on mode of action and property prediction tasks using a popular phenotypic screening dataset. Results show that the self-supervised approach that uses data from multiple consortium partners provides representation that is more robust to batch effects whilst simultaneously achieving performance on par with standard approaches. Together with other conclusions, it provides recommendations on the training strategy of a representation model for HCS images.

An overview of nasal cartilage bioprinting: from bench to bedside.

Nasal cartilage diseases and injuries are known as significant challenges in reconstructive medicine, affecting a substantial number of individuals worldwide. In recent years, the advent of three-dimensional (3D) bioprinting has emerged as a promising approach for nasal cartilage reconstruction, offering potential breakthroughs in the field of regenerative medicine. This paper provides an overview of the methods and challenges associated with 3D bioprinting technologies in the procedure of reconstructing nasal cartilage tissue. The process of 3D bioprinting entails generating a digital 3D model using biomedical imaging techniques and computer-aided design to integrate both internal and external scaffold features. Then, bioinks which consist of biomaterials, cell types, and bioactive chemicals, are applied to facilitate the precise layer-by-layer bioprinting of tissue-engineered scaffolds. After undergoing in vitro and in vivo experiments, this process results in the development of the physiologically functional integrity of the tissue. The advantages of 3D bioprinting encompass the ability to customize scaffold design, enabling the precise incorporation of pore shape, size, and porosity, as well as the utilization of patient-specific cells to enhance compatibility. However, various challenges should be considered, including the optimization of biomaterials, ensuring adequate cell viability and differentiation, achieving seamless integration with the host tissue, and navigating regulatory attention. Although numerous studies have demonstrated the potential of 3D bioprinting in the rebuilding of such soft tissues, this paper covers various aspects of the bioprinted tissues to provide insights for the future development of repair techniques appropriate for clinical use.

Combining Nuclear Medicine With Other Modalities: Future Prospect for Multimodality Imaging.

This meeting report summarizes a consultants meeting that was held at International Atomic Energy Agency Headquarters, Vienna, in July 2022 to provide an update on the development of multimodality imaging by combining nuclear medicine imaging agents with other nonradioactive molecular probes and/or biomedical imaging techniques.

How can new biomedical imaging techniques help us understand strokes?

How can new biomedical imaging techniques help us understand strokes?