Reflection-mode diffraction tomography of multiple-scattering samples on a reflective substrate from intensity images

Although rigorous numerical methods, such as the finite-difference time-domain (FDTD) method, can solve time-domain Maxwell's equations with high accuracy, they still face significant challenges regarding stability and computational efficiency. In this paper, a novel stable and generalizable neural network for spatiotemporal electromagnetic modeling (SGNN-STEM) is proposed to enable inference stability with larger stepping time interval than that of the conventional FDTD, and accurate prediction of spatiotemporal electromagnetic fields comparable to FDTD. The results show that the proposed SGNN-STEM exhibits strong generalization capabilities for both varying initial conditions and geometric configurations. Furthermore, its effectiveness across different boundary conditions is validated through various numerical results. Benefiting from the inference stability property, the SGNN-STEM approach showcases superiority in computational efficiency when simulation times reach 400 or larger. Regarding accuracy, the normalized mean squared error (NMSE) of the proposed method is below 0.1 percent in scenarios involving a polygonal scatterer. In the modeling of a scattering array, the NMSEs are 0.5 percent and 9.9 percent under absorbing boundary conditions and perfect electric conductor boundary conditions, respectively. Finally, two practical cases are provided to exhibit the possible utility of the proposed method.

PurposeThis paper aims to improve the computational efficiency of higher-order accurate Noye–Hayman [NH (9,9)] implicit finite difference scheme for the solution of electromagnetic scattering problems in tunnel environments.Design/methodology/approachThe proposed method consists of two major steps: First, the higher-order NH (9,9) scheme is numerically discretized using the finite-difference method. The second step is to use an algorithm based on hierarchical interpolative factorization (HIF) to accelerate the solution of this scheme.FindingsIt is observed that the simulation results obtained from the numerical tests illustrate very high accuracy of the NH (9,9) method in typical tunnel environments. HIF algorithm makes the NH (9,9) method computationally efficient for two-dimensional (2D) or three-dimensional (3D) problems. The proposed method could help in reducing the computational cost of the NH (9,9) method very close to O(n) usual O(n3) for a full matrix.Research limitations/implicationsFor simplicity, in this study, perfect electric conductor boundary conditions are considered. Future research may also include the utilization of meteorological techniques, including the effects of backward traveling waves, and make comparisons with the experimental data.Originality/valueThis study is directly applicable to typical problems in the field of tunnel propagation modeling for both national commercial and military applications.

A Fourier penalty method for solving the time-dependent Maxwell's equations in domains with curved boundaries

Two models for numerically simulating a surface impedance boundary condition in FDTD are presented. The resulting algorithm is easy to implement, requiring only trivial modification to a code written for a perfect electrical conductor boundary condition.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This special section on Methods and Application in Biomedical Imaging presents the ongoing and active research in the Biomedical Imaging area, with emphasis on machine learning and image processing state-of-the art techniques. The overall goal of this special section is to summarize in a handbook manner, recent discoveries and groundbreaking studies that will account for new challenge research in the broad and relevant area of Biomedical Imaging.

High-end biomedical applications, such as Optical Coherence Tomography (OCT) or Confocal Microscopy (CM) require both precision and speed. The latter is essential in OCT by example to achieve in vivo, real time imaging – with video rate imaging capability. An essential element of this effort to achieve such speeds in OCT by example is the optomechatronic system used for lateral scanning. It usually consists of a dual axis double galvanometer-based scanner (GS). However, GSs are used in a larger variety of applications in biomedical imaging – not only in lateral scanning. Due to the importance of the topic, we have approached different aspects of GSs technology, including scanning and control functions, duty cycle optimization, and minimization of artifacts. The paper proposes a Model-based Predictive Control (MPC) structure for driving the GSs in order to achieve either an improved precision or a higher speed. The predictive control solution was tested for different types of input signals. Reasons for choosing the objective function and the predictive horizons are discussed. The GS was characterized by a second order mathematical model (MM), with the values of the parameters identified experimentally. Simulations were carried out using Matlab Simulink. The control results achieved are compared with the Proportional Integrative Derivative controller with Lags (PID-L1). The conclusions support the proposed control solution and its implementation in applications.

Spectral imaging, which includes hyperspectral and multispectral imaging, can provide images in numerous wavelength bands within and beyond the visible light spectrum. Emerging technologies that enable compact, portable spectral imaging cameras can facilitate new applications in biomedical imaging. With this review paper, researchers will (1)understand the technological trends of upcoming spectral cameras, (2)understand new specific applications that portable spectral imaging unlocked, and (3)evaluate proper spectral imaging systems for their specific applications. We performed a comprehensive literature review in three databases (Scopus, PubMed, and Web of Science). We included only fully realized systems with definable dimensions. To best accommodate many different definitions of "compact," we included a table of dimensions and weights for systems that met our definition. There is a wide variety of contributions from industry, academic, and hobbyist spaces. A variety of new engineering approaches, such as Fabry-Perot interferometers, spectrally resolved detector array (mosaic array), microelectro-mechanical systems, 3D printing, light-emitting diodes, and smartphones, were used in the construction of compact spectral imaging cameras. In bioimaging applications, these compact devices were used for in vivo and ex vivo diagnosis and surgical settings. Compact and ultracompact spectral imagers are the future of spectral imaging systems. Researchers in the bioimaging fields are building systems that are low-cost, fast in acquisition time, and mobile enough to be handheld.

Optical imaging enables visualization of biological structure and function but is fundamentally limited by several physical constraints. Spatial resolution is bounded by optical diffraction, depth penetration is curtailed by strong absorption and scattering in tissue, and image contrast-to-noise ratio is often restricted by photon shot noise in low-light conditions. This thesis advances two novel directions—photoacoustic imaging and quantum imaging—to address these limitations. In photoacoustic imaging, we design and optimize high-speed photoacoustic computed tomography systems that enable deep, volumetric visualization of vasculature. By incorporating time-gated reconstruction and image-enhancement algorithms, these systems support small-animal imaging of cardiac structure, liver morphology, and brain hemodynamics non-invasively. Building on these foundations, we explore non-invasive breast photoacoustic imaging with high spatiotemporal resolution. Through integration with learning-based feature extraction, classification, and segmentation pipelines, we demonstrate the feasibility of applying photoacoustic imaging in clinical workflows to aid the characterization of breast tissue. In quantum imaging, we develop two complementary architectures that extend the state of the art in opposite but synergistic directions. The scanning quantum microscope scales up existing quantum imaging approaches, achieving the largest resolvable pixel counts to date by combining entangled-photon illumination with efficient coincidence detection. This platform enables the first demonstration of whole-organism imaging and shows potential in remote sensing and sub-shot-noise imaging. In contrast, the widefield quantum microscope scales down quantum imaging to the microscopic regime, integrating single-photon–sensitive cameras with a covariance-based coincidence estimation algorithm. This approach enables cellular-level imaging and demonstrates quantum-enhanced resolution beyond the classical diffraction limit, establishing a practical pathway for quantum microscopy in biological imaging. Across both research directions, this thesis advances system design and engineering, quantitative characterization, calibration, reconstruction, and image-enhancement methodologies. Together, these developments establish pathways from physical principles to practical imaging systems, spanning laboratory prototypes through preclinical and clinical applications in biomedical imaging.

The resolution of an optical microscope is determined by the overall point spread function of the system. When examining structures significantly smaller than the wavelength of light, the contribution of the background or surrounding environment can profoundly affect the point spread function. This research delves into the impact of reflective planar substrate structures on the system’s resolution. We establish a comprehensive forward imaging model for a reflection-type confocal laser scanning optical microscope, incorporating vector field manipulation to image densely packed nanoparticle clusters. Both theoretical and experimental findings indicate that the substrate causes an interference effect between the background field and the scattered field from the nanoparticles, markedly enhancing the overall spatial resolution. The integration of vector field manipulation with an interferometric scattering approach results in superior spatial resolution for imaging isolated particles and densely distributed nanoscale particle clusters even with deep subwavelength gaps as small as 20 nm between them. However, the method still struggles to resolve nanoparticles positioned directly next to each other without any gap, necessitating further work to enhance the resolving ability. This may involve techniques like deconvolution or machine learning-based post-processing methods.

Ultra‐wideband (UWB) antennas play a crucial role in biomedical and microwave imaging applications because they provide accurate imaging and diagnostics with a wide frequency range, high resolution, and reliable signal integrity. This study presents a coplanar waveguide (CPW)‐fed Helm‐shaped printed antenna with UWB characteristics for biomedical and microwave imaging applications. The antenna is compact and has total dimensions of 30 × 25 mm 2 and demonstrates an impressive UWB with a fractional bandwidth (FBW) of 122.15%, covering the frequency range from 2.9 to 12 GHz. Additionally, it achieves a peak gain of 4.8 dBi @ 11 GHz and has stable radiation characteristics across the operating bandwidth. The proposed antenna is developed in the form of a prototype, and measured results are obtained using in‐house facilities. It is observed that the measured performance is agreed well with simulations. The time domain analysis of the proposed structure is also conducted, and it is observed that the group delay is &lt;1 ns for both side‐by‐side (SbS) and face‐to‐face configurations. Through the obtained results, it is worth mentioning that the proposed antenna design is suitable for biomedical, microwave imaging, and hyperthermia treatment applications.

Intense interest in applying convolutional neural networks (CNNs) in biomedical image analysis is wide spread, but its success is impeded by the lack of large annotated datasets in biomedical imaging. Annotating biomedical images is not only tedious and time consuming, but also demanding of costly, specialty-oriented knowledge and skills, which are not easily accessible. To dramatically reduce annotation cost, this paper presents a novel method called AIFT (active, incremental fine-tuning) to naturally integrate active learning and transfer learning into a single framework. AIFT starts directly with a pre-trained CNN to seek "worthy" samples from the unannotated for annotation, and the (fine-tuned) CNN is further fine-tuned continuously by incorporating newly annotated samples in each iteration to enhance the CNN's performance incrementally. We have evaluated our method in three different biomedical imaging applications, demonstrating that the cost of annotation can be cut by at least half. This performance is attributed to the several advantages derived from the advanced active and incremental capability of our AIFT method.

We have developed a new derivative-free algorithm based on Radial Basis Functions (RBFs). Derivative-free optimization is an active field of research and several algorithms have been proposed recently. Problems of this nature in the industrial setting are quite frequent. The reason is that in a number of applications the optimization process contains simulation packages which are treated as black boxes. The development of our own algorithm was originally motivated by an application in biomedical imaging: the medical image registration problem. The particular characteristics of this problem have incited us to develop a new optimization algorithm based on trust-region methods. However it has been designed to be generic and to be applied to a wide range of problems. The main originality of our approach is the use of RBFs to build the models. In particular we have adapted the existing theory based on quadratic models to our own models and developed new procedures especially designed for models based on RBFs. We have tested our algorithm called BOOSTERS against state-of-the-art methods (UOBYQA, NEWUOA, DFO). On the medical image registration problem, BOOSTERS appears to be the method of choice. The tests on problems from the CUTEr collection show that BOOSTERS is comparable to, but not better than other methods on small problems (size 2-20). It is performing very well for medium size problems (20-80). Moreover, it is able to solve problems of dimension 200, which is considered very large in derivative-free optimization. We have also developed a new class of algorithms combining the robustness of derivative-free algorithms with the faster rate of convergence characterizing Newtonlike-methods. In fact, they define a new class of algorithms lying between derivative-free optimization and quasi-Newton methods. These algorithms are built on the skeleton of our derivative-free algorithm but they can incorporate the gradient when it is available. They can be interpreted as a way of doping derivative-free algorithms with derivatives. If the derivatives are available at each iteration, then our method can be seen as an alternative to quasi-Newton methods. At the opposite, if the derivatives are never evaluated, then the algorithm is totally similar to BOOSTERS. It is a very interesting alternative to existing methods for problems whose objective function is expensive to evaluate and when the derivatives are not available. In this situation, the gradient can be approximated by finite differences and its costs corresponds to n additional function evaluations assuming that Rn is the domain of definition of the objective function. We have compared our method with CFSQP and BTRA, two gradient-based algorithms, and the results show that our doped method performs best. We have also a theoretical analysis of the medical image registration problem based on maximization of mutual information. Most of the current research in this field is concentrated on registration based on nonlinear image transformation. However, little attention has been paid to the theoretical properties of the optimization problem. In our analysis, we focus on the continuity and the differentiability of the objective function. We show in particular that performing a registration without extension of the reference image may lead to discontinuities in the objective function. But we demonstrate that, under some mild assumptions, the function is differentiable almost everywhere. Our analysis is important from an optimization point of view and conditions the choice of a solver. The usual practice is to use generic optimization packages without worrying about the differentiability of the objective function. But the use of gradient-based methods when the objective function is not differentiable may result in poor performance or even in absence of convergence. One of our objectives with this analysis is also that practitioners become aware of these problems and to propose them new algorithms having a potential interest for their applications.

Because of the unique properties of nanoparticles in the electromagnetic spectrum, its use as diagnostic agents for improved visualization is an emerging research area in biomedical imaging. However specific aspects of nanoparticles need to be explored to illustrate these applications. In this paper we present the dielectric and optical studies performed on propylene carbonate colloid of barium titanate nanoparticles at different volume fractions. The behavior of these nanoparticles at the near infrared optical window and at the industrial, scientific, and medical applications band suggests its potential application as contrast agents or tracers in optical and microwave medical imaging. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:11‐13, 2012; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26496

In vivo bioluminescence imaging (BLI) has played a more and more important role in biomedical research of small animals. Tomographic bioluminescence imaging (TBI) further translates the BLI optical information into three-dimensional bioluminescent source distribution, which could greatly facilitate applications in related studies. Although the diffusion approximation (DA) is one of the most widely-used forward models, higher-order approximations are still needed for in vivo small animal imaging. In this work, as a relatively accurate and higher-order approximation theory, a simplified spherical harmonics approximation (SP_N) is applied for heterogeneous tomographic bioluminescence imaging in vivo. Furthermore, coupled with the SP_N, a generalized graph cuts optimization approach is utilized, making BLT reconstructions fast and suit for the whole body of small animals. Heterogeneous in vivo experimental reconstructions via the higher-order approximation model demonstrate higher tomographic imaging quality, which is shown the capability for practical biomedical tomographic imaging applications.

Background: Carbon-based nanoparticle classes consisting of various subgroups based on morphology and crystallinity are called carbon dots (CQDs). The physical, chemical, and optical properties of CQDs can be modified using the simple pot synthesis technique. Additionally, its non-toxic nature, biocompatibility, physical and chemical responsiveness, resistance to chemical and photo bleaching, and low cost make it suitable for various purposes, such as biomedical imaging applications. Biomass waste, which has been widely discarded without economic utilization and potential, can surprisingly be used as a precursor for CQDs. Method: The literature was systematically collected from major databases. Studies from 2017–2025 were analyzed based on synthesis strategies, surface functionalization, and biological performance. Its potential in the medical field is highly advantageous. CQDs have fluorescence that is useful for biomedical imaging both in vivo and in vitro. The hydrothermal carbonization approach is also discussed in more detail, highlighting its green and sustainable synthesis, as well as the ease of the synthesis process. Finding: It was found that CQDs have compatibility and adjustable optical properties. Its fluorescence can clearly record tissues, body care, aging, and living cells. Utilizing renewable biomass precursors offers an environmentally friendly and cost-effective route for synthesizing fluorescent nanoprobes with excellent water solubility, tunable emission, and low cytotoxicity. Additionally, in vitro studies reinforce CQDs as multicolor fluorescent probes, and in vivo studies demonstrate that CQDs have low toxicity, rapid clearance, are safe, and biocompatible. Conclusion: This paper delves into the remarkable potential of CQDs to provide insights into how fluorescent inks are truly essential in biomedical imaging. Novelty/Originality of this article: This study provides a comprehensive and updated synthesis of CQD research spanning up to 2025, specifically focusing on the transition from "waste to nanoprobe."