Imaging Systems Research Articles

W-band frequency selective digital metasurface using active learning-based binary optimization

Abstract The W-band is essential for applications like high-resolution imaging and advanced monitoring systems, but high-frequency signal attenuation leads to poor signal-to-noise ratios, posing challenges for compact and multi-channel systems. This necessitates distinct frequency selective surfaces (FSS) on a single substrate, a complex task due to inherent substrate resonance modes. In this study, we use a digital metasurface platform to design W-band FSS on a glass substrate, optimized through binary optimization assisted by active learning. The digital metasurface is composed of a periodic array of sub-wavelength unit cells, each containing hundreds of metal or dielectric pixels that act as binary states. By utilizing a machine learning model, we apply active learning-aided binary optimization to determine the optimal binary state configurations for a given target FSS profile. Specifically, we identify optimal designs for distinct FSS on a conventional glass substrate, with transmittance peaks at 79.3 GHz and Q-factors of 32.7.

Study of the TOFPET2c ASIC in time-of-flight detection of x-rays for scatter rejection in medical imaging applications

Objective.Integrating time-of-flight (ToF) measurements in radiography and computed tomography (CT) enables an approach for scatter rejection in imaging systems that eliminates the need for anti-scatter grids, potentially increasing system sensitivity and image quality. However, present hardware dedicated to the time-correlated measurement of x-rays is limited to a single pixel physically too large for the desired spatial resolution. A switch to highly integrated electronics and detectors is needed to progress towards detector arrays capable of acquiring images, while offering a timing resolution below 300 ps FWHM to achieve scatter rejection comparable to current anti-scatter grids.Approach.Using off-the-shelf scintillators, photodetectors and readouts designed for ToF positron emission tomography (PET) provides a preliminary evaluation of available highly integrated readout systems supporting detector arrays for ToF scatter rejection. The TOFPET2c ASIC from PETSys offers an established development platform necessary for fast and reliable results, with no known limitation regarding time-correlated detection of medical imaging x-rays (20-140 keV).Main results.Reliable photon detection down to 31 keV was achieved, reaching energy resolutions from 23% to 92% FWHM throughout the desired energy range. Optimal detector timing resolution (DTR) from 250 ps FWHM at 130 keV to 678 ps FWHM at 30 keV was reached. Strong time walk effects were observed, showing a time shift of 642 ps up to 1740 ps between events spanning the energies used in x-ray medical imaging.Significance.The TOFPET2c ASIC has shown its potential for ToF scatter rejection, but meets the time resolution requirement of 300 ps FWHM only for limited energies (110-140 keV). This significant timing degradation observed at lower energies limits the use of the TOFPET2c ASIC for ToF scatter rejection, but offers significant advancements regarding the understanding of the phenomenon arising from the time-correlated detection of medical imaging x-rays.

Commissioning and operation of 11.74T human MRI magnet for Gachon university gil medical center, South Korea

Abstract Magnetic resonance imaging (MRI) systems with magnetic field strengths higher than 7 Tesla (T), offer exceptional image resolution and signal-to-noise ratio, enabling more detailed visualization of fine anatomical structures, especially for brain, musculoskeletal, and cardiovascular studies. Only a few of them are operating in the whole world. An 11.74 T magnet for human MRI has been commissioned at Gachon University Gil Medical Center (GUGMC) in South Korea in 2022 and has now exceeded two-year operational life at full field. An overview of its features, the commissioning test campaign, and the experience gained after operations is presented.

Navigating challenges and maximizing potential: Handling complications and constraints in minimally invasive surgery

Abstract Minimally invasive surgery (MIS) has revolutionized the field of surgery by offering reduced trauma, faster recovery times, and improved patient outcomes compared to traditional open procedures. However, the widespread adoption of MIS is not without challenges, and managing complications and limitations is essential for optimizing patient safety and surgical efficacy. This study provides a comprehensive overview of the strategies employed in the management of complications and limitations associated with MIS. Complications in MIS can arise from various factors, including anatomical variations, unexpected bleeding, or difficulties in instrument manipulation. Recognizing the potential for complications is crucial, and surgeons must be equipped to make real-time decisions, including the possibility of converting to open procedures when necessary. Patient selection plays a pivotal role, with thorough preoperative assessments helping identify individuals who may be at higher risk for complications. Limitations in MIS are diverse and can stem from anatomical challenges, such as restricted access in confined spaces or complex structures. While technological advancements, such as robotic-assisted surgery and advanced imaging, have mitigated some limitations, ongoing efforts are directed toward overcoming challenges related to limited range of motion and maneuverability in intricate anatomical locations. Strategies for the management of complications and limitations involve a multidisciplinary approach. Surgeon training and proficiency in MIS techniques are paramount, emphasizing the importance of ongoing education and simulation-based training. Thorough preoperative planning, including a comprehensive assessment of patient history and anatomical considerations, helps anticipate potential challenges. Additionally, advancements in technology, such as enhanced imaging and robotic-assisted systems, contribute to improved visualization and precision during MIS. Regular maintenance and calibration of equipment, along with an emphasis on team communication and collaboration, further enhance the safety and effectiveness of MIS. The management of complications and limitations in MIS requires a holistic approach, encompassing surgical expertise, technological innovations, and meticulous preoperative planning. By addressing these challenges, the field of MIS continues to evolve, offering patients minimally invasive options with enhanced safety and efficacy.

Enhanced biochemical sensing with high-Q transmission resonances in free-standing membrane metasurfaces

Optical metasurfaces provide solutions to label-free biochemical sensing by localizing light resonantly beyond the diffraction limit, thereby selectively enhancing light–matter interactions for improved analytical performance. However, high-Q resonances in metasurfaces are usually achieved in the reflection mode, which impedes metasurface integration into compact imaging systems. Here, we demonstrate a metasurface platform for advanced biochemical sensing based on the physics of the bound states in the continuum (BIC) and electromagnetically induced transparency (EIT) modes, which arise when two interfering resonances from a periodic pattern of tilted elliptic holes overlap both spectrally and spatially, creating a narrow transparency window in the mid-infrared spectrum. We experimentally measure these resonant peaks observed in transmission mode (Q∼734 at λ∼8.8µm) in free-standing silicon membranes and confirm their tunability through geometric scaling. We also demonstrate the strong coupling of the BIC-EIT modes with a thinly coated PMMA film on the metasurface, characterized by a large Rabi splitting (32cm−1) and biosensing of protein monolayers in transmission mode. Our new photonic platform can facilitate the integration of metasurface biochemical sensors into compact and monolithic optical systems while being compatible with scalable manufacturing, thereby clearing the way for on-site biochemical sensing in everyday applications.

Fast InVivo Two-Photon Fluorescence Imaging via Lateral and Axial Resolution Restoration WithSelf-Supervised Learning.

Two-photon fluorescence (TPF) imaging opens a new avenue to achieve high resolution at extended penetration depths. However, it is difficult for conventional TPF imaging systems to simultaneously achieve high resolution and speed. In this work, we develop an innovative deep learning framework of Lateral and Axial Resolution Restoration (LARR) to break the contradiction between imaging resolution and speed. LARR employs a self-supervised training scheme to computationally restore the sparsely sampled TPF images to resolution isotropic images by 4-fold axial and 16-fold lateral resolution enhancement. The simulation studies and experimental results demonstrate the excellent performance of LARR to preserve fine structural features with improved signal-to-noise ratio and structure similarity index. Moreover, the TPF imaging system with the LARR is able to achieve 60-fold improved imaging speed and comparable resolution as compared with the conventional TPF system. The outstanding performance makes LARR a potential tool for fast TPF imaging with high resolution.

Resolution metric for imaging systems

Resolution metric for imaging systems

A new model for poetry translation

A poem is comprehended as a complex macro-image that combines autosemantic, synsemantic, and subsemantic image levels. These levels are mutually conditioned and interact with one another, requiring a comprehensive reconstruction in translation that considers their hierarchy and relationships within the macro-image and broader contexts. The main goal of this study is to introduce a comprehensive scheme for the integral multi-aspect analysis of poetry during the translation process aimed at achieving translation adequacy. The article identifies and depicts the macro-image structure of a poem, which is essential for its interpretation, pre-translating analysis, translation, and quality assessment. This study treats a poem as a macro-image method of structural formation composed of interconnected image systems. An adequate rendering of these systems reflects the unity of the content and form of the original poem in its translation. Furthermore, it also provides a new definition of poetic translation. The proposed macro-image structure-system scheme of poetic works would facilitate a thorough analysis of the original poem and its adequate reconstruction in another language. Applying this scheme to existing translations would contribute to a more comprehensive analysis of poetic works and enhance translation adequacy.

Image registration for accurate electrode deformation analysis in operando microscopy of battery materials

Operando imaging techniques have become increasingly valuable in both battery research and manufacturing. However, the reliability of these methods can be compromised by instabilities in the imaging setup and operando cells, particularly when utilizing high-resolution imaging systems. The acquired imaging data often include features arising from both undesirable system vibrations and drift, as well as the scientifically relevant deformations occurring in the battery sample during cell operation. For meaningful analysis, it is crucial to distinguish and separately evaluate these two factors. To address these challenges, we employ a suite of advanced image-processing techniques. These include fast Fourier transform analysis in the frequency domain, power spectrum-based assessments for image quality, as well as rigid and non-rigid image-registration methods. These techniques allow us to identify and exclude blurred images, correct for displacements caused by motor vibrations and sample holder drift and, thus, prevent unwanted image artifacts from affecting subsequent analyses and interpretations. Additionally, we apply optical flow analysis to track the dynamic deformation of battery electrode materials during electrochemical cycling. This enables us to observe and quantify the evolving mechanical responses of the electrodes, offering deeper insights into battery degradation. Together, these methods ensure more accurate image analysis and enhance our understanding of the chemomechanical interplay in battery performance and longevity.

Differentiable pixel-super-resolution lensless imaging

Conventional lensless imaging systems require complex phase diversity measurements and sequential processing steps, limiting their practical application despite their compact design. We present a differentiable end-to-end pixel-super-resolution (dPSR) technique that unifies PSR hologram synthesis, autofocusing, and complex-field reconstruction within a single optimization framework. By jointly optimizing these traditionally separate processes, our method eliminates both phase diversity requirements and error accumulation from sequential processing. Our method achieves superior position estimation accuracy (mean error 0.0282 pixels versus 0.1172 pixels with conventional methods), delivering precise autofocusing with accuracy better than 0.3 µm, and enabling a twofold resolution enhancement beyond the sensor’s native pixel size. This robust performance is validated through both simulated and experimental results, including challenging phase objects and label-free cell imaging, establishing dPSR as a practical solution for high-resolution microscopy applications.

Multi-modal dataset creation for federated learning with DICOM-structured reports.

Purpose Federated training is often challenging on heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging multi-modal learning paradigms, where dataset harmonization including a uniform data representation and filtering options are of paramount importance.Methods DICOM-structured reports enable the standardized linkage of arbitrary information beyond the imaging domain and can be used within Python deep learning pipelines with highdicom. Building on this, we developed an open platform for data integration with interactive filtering capabilities, thereby simplifying the process of creation of patient cohorts over several sites with consistent multi-modal data.Results In this study, we extend our prior work by showing its applicability to more and divergent data types, as well as streamlining datasets for federated training within an established consortium of eight university hospitals in Germany. We prove its concurrent filtering ability by creating harmonized multi-modal datasets across all locations for predicting the outcome after minimally invasive heart valve replacement. The data include imaging and waveform data (i.e., computed tomography images, electrocardiography scans) as well as annotations (i.e., calcification segmentations, and pointsets), and metadata (i.e., prostheses and pacemaker dependency).Conclusion Structured reports bridge the traditional gap between imaging systems and information systems. Utilizing the inherent DICOM reference system arbitrary data types can be queried concurrently to create meaningful cohorts for multi-centric data analysis. The graphical interface as well as example structured report templates are available at https://github.com/Cardio-AI/fl-multi-modal-dataset-creation .

Methods for Preclinical Assessment of the Efficacy of Anticancer Medicines in vivo (Review)

INTRODUCTION. The main risk to the clinical translatability of preclinical results for anticancer medicinal products is posed by the difficulty of simulating clinical conditions in an experimental model. With only 5% of product candidates proving clinically effective, the search for new approaches to the preclinical development of anticancer medicinal products is currently an active area of research in medicine.AIM. This study aimed to provide methodological support for planning experiments with modelling of neoplastic processes through analysis and classification of the methods used in preclinical studies of the efficacy of anticancer medicinal products in vivo.DISCUSSION. This article reviews the development of animal tumour models and the selection of cell lines and their testing for tumourigenicity and viability on a step-by-step basis. According to the study results, imaging systems, vital staining, and fluorescence- and luminescence-based methods can be used to assess the efficacy of anticancer medicinal products in both solid tumour models and haematological malignancy models. The article presents a schematic representation of the main types of mouse cancer models. However, no single animal species is universally suitable for in vivo cancer modelling. Researchers selecting models and considering their advantages and disadvantages should pay special attention to the similarity of disease mechanisms in animal models and humans at the tissue and molecular level, keeping in mind the aims of their research.CONCLUSIONS. The results of this comparative analysis of methods for preclinical efficacy evaluation of anticancer medicinal products are essential for designing experimental studies and ensuring the reliability of the results obtained. Choosing the correct research method will increase the chances of obtaining experimental data that can be successfully translated into clinical practice.

Towards an integrated imaging for melanoma diagnosis: A review of multispectral, hyperspectral, and thermal technologies with preliminary system development.

The diagnosis of melanoma traditionally relies on visual inspection or on the use of the dermoscope, which do not have capabilities for early and precise detection. In this review, we aimed to explore other imaging technologies that can provide non-invasive and detailed information on skin lesions, such as multispectral, hyperspectral and thermal imaging. In this regard, the systems were evaluated in terms of hardware, performance and clinical applications. Since there is currently a very big interest in developing artificial intelligence (AI) applications in dermatology, the review also focuses on analysing studies that integrated this technology with newer imaging systems. To obtain clinical validation for such systems, there is an extensive need for publicly available datasets, as the current ones are limited. Expanding and obtaining new datasets is crucial in advancing research for a more accurate melanoma diagnosis. Taking into consideration the benefits that these imaging modalities can provide if they are combined with AI, we propose a prototype that can distinguish between melanoma and its precursor, the nevus, for which the set-up, components, imaging processing pipeline and classification techniques are described. The final system benefits of the advantages provided by near infrared, thermal and visible cameras, that allow a more in-depth characterizations of melanoma for a better understanding of its behaviour, an early detection improvement and diagnostic precision.

Microbial Identification Through Multispectral Infrared Imaging of Colonies: A New Type of Morpho-Spectral Fingerprinting.

We describe a proof of concept for a new microbial identification technique using Direct Frequency Infrared (DFIR) multispectral imaging. This approach combines Quantum Cascade Laser (QCL) light sources with a microbolometer array in a lensless configuration to capture detailed multispectral images of microbial colonies. These optical fingerprints blend both morphological and spectral information, without the need for staining or colony picking. A proof-of-concept database was acquired, comprising 10 strains from 8 species across 4 distinct genera. In total, 2253 microbial colonies were imaged at 9 different mid-infrared wavelengths. Machine learning classification correctly identified up to 94.4% ± 1.6 of colonies fingerprints, efficiently discriminating even closely related strains. Reducing the number of wavelengths to 4 maintained high classification performance, demonstrating the method's robustness. The resulting system is faster and simpler than existing FTIR imaging systems, making it a promising tool for microbial identification.

Tests of X-ray Image Reconstruction: A New Index for Assessing Image Quality

Abstract Indirect X-ray modulation imaging has been adopted in a number of solar missions and provided reconstructed X-ray images of solar flares that are of great scientific importance. However, the assessment of the image quality of the reconstruction is still difficult which is particularly useful for scheme design of X-ray imaging systems, testing and improvement of imaging algorithms, and scientific research of X-ray sources. Currently, there is no specified method to quantitatively evaluate the quality of X-ray image reconstruction and the point spread function (PSF) of an X-ray imager. In this paper, we propose percentage proximity degree (PPD) by considering the imaging characteristics of X-ray image reconstruction and in particular, sidelobes and their effects on imaging quality. After testing a variety of imaging quality assessments in six aspects, we utilized the technique for order preference by similarity to ideal solution to the indices that meet the requirements. Then we develop the final quality index for X-ray image reconstruction, QuIX, which consists of the selected indices and the new PPD. QuIX performs well in a series of tests, including assessment of instrument PSF and simulation tests under different grid configurations, and imaging tests with RHESSI data. It is also a useful tool for testing of imaging algorithms, and determination of imaging parameters for both RHESSI and ASO-S/HXI, such as field of view (FOV), beam width factor, and detector selection.

Evaluating goji berry (Lycium barbarum L.) quality change during storage using color and fluorescence imaging systems

Evaluating goji berry (Lycium barbarum L.) quality change during storage using color and fluorescence imaging systems

Noninvasive photoacoustic synergistic measurement of vascular morphology and blood flow velocity

Photoacoustic imaging is a multimodal imaging technique combining optical and acoustic principles to observe and analyze structural and functional tissue information. This study presents a photoacoustic velocimetry and imaging experimental system for synergistic measurement of vascular morphology and blood flow velocity. The system, equipped with a 532 nm pulsed laser and optical-acoustic combiner, achieves a horizontal resolution of 10 μm. It measures flow velocities ranging from 25 to 200 mm/s with an average error of 8.2%. Through two-dimensional scanning and Gaussian filtering, the vascular morphology was reconstructed with a Dice Similarity Coefficient of 0.92. Compared to conventional imaging systems, the proposed system enhances small vessel resolution by up to 40% and achieves a signal-to-noise ratio improvement of 7 dB. These results highlight the system's potential for precise and noninvasive biomedical applications, including real-time diagnosis and monitoring. Vascular imaging plays a critical role in biomedical research, aiding in understanding vascular structure and function and exploring cardiovascular diseases, tumor angiogenesis, hemodynamics, and other related diseases. Noninvasive imaging techniques can evaluate disease progression and therapy efficacy. Accurate blood flow measurement is essential for diagnosing various diseases, including stroke, diabetes, and cancer. This work demonstrates a synergistic photoacoustic system capable of achieving high-resolution vascular imaging and flow velocity measurements.

Indocyanine Green as a Marker for Tissue Ischemia in Spinal Tumor Resections and Extended Revisions: A Technical Note

Background/Objectives: The increasing complexity of spinal oncology procedures, particularly in en-bloc tumor resections, creates challenges in tissue perfusion assessment due to extended operative times and extensive surgical dissection. Real-time visualization of tissue perfusion can be achieved with ICG using commercially available handheld imaging systems, offering potential advantages in spinal oncology cases. This study assessed the utility of ICG in analyzing soft-tissue viability during complex spine procedures extending beyond 7.5 h, with a particular focus on oncologic resections. Methods: Three cases that required over 7.5 h of operative time were chosen for ICG utilization. These cases included an en-bloc malignant peripheral nerve sheath tumor resection, an en-bloc resection of a malignant epithelioid neoplasm, and a long-segment fusion revision for pseudoarthrosis. At the conclusion of the critical portion of the procedure, a handheld intraoperative fluorescence camera was utilized to visualize the tissue penetration of intravenous ICG. Results: Prior to injecting ICG, devascularized tissue was not clearly visible. Injecting ICG allowed clear separation of vascularized (fluorescing) and devascularized (non-fluorescing) tissues. One region of non-florescent tissue was later confirmed to be devascularized with MRI and experienced postoperative infection. Conclusions: As the complexity of spinal oncology procedures increases, ICG fluorescence imaging offers a novel method for real-time assessment of tissue perfusion. This technique may be particularly valuable in extensive tumor resections, post-radiation cases, and revision surgeries where tissue viability is at risk. Further investigation in the spinal oncology population could help establish whether early identification of poorly perfused tissues impacts wound healing outcomes.

Collision of high-resolution wide FOV metalens cameras and vision tasks

Abstract Metalenses, with their compact form factor and unique optical capabilities, hold tremendous potential for advancing computer vision applications. In this work, we propose a high-resolution, large field-of-view (FOV) metalens intelligent recognition system, combining the latest YOLO framework, aimed at supporting a range of vision tasks. Specifically, we demonstrate its effectiveness in scanning, pose recognition, and object classification. The metalens we designed to achieve a 100° FOV while operating near the diffraction limit, as confirmed by experimental results. Moreover, the metalenses weigh only 0.1 g and occupy a compact volume of 0.04 cm3, effectively addressing the bulkiness of conventional lenses and overcoming the limitations of traditional metalens in spatial frequency transmission. This work highlights the transformative potential of metalenses in the field of computer vision, The integration of metalenses with computer vision opens exciting possibilities for next-generation imaging systems, offering both enhanced functionality and unprecedented miniaturization.

Improving the reconstruction accuracy of tomographic absorption spectroscopy sensor by optimizing laser beam arrangements

Tomographic absorption spectroscopy (TAS) typically employs multiple projections to reconstruct the spatial distribution of temperature and species concentration, making it a promising method for combustion diagnostics. However, the geometric arrangement of the laser beams significantly affects its accuracy, especially under limited beam conditions. In this work, we propose a beam arrangement optimization method to maximize the utilization efficiency of limited beams, thereby improving reconstruction accuracy in a simpler and more intuitive manner. We designed a cost function that combines the beam number matrix (BNM) and the total weight matrix (TWM). This approach ensures that the maximum number of rays passes through each grid while also maximizing the optical path length in the regions covered by the beam distribution. The optimization is performed using a simulated annealing algorithm to obtain the optimal beam arrangement. For a TAS sensor comprising 12 laser beams, numerical simulations demonstrate that the BNM-TWM optimized beam arrangement achieves lower reconstruction errors across various synthetic temperature fields when compared to traditional orthogonal beam arrangements and those optimized for orthogonality (OD). This improvement is particularly significant when the performance of the orthogonal and OD-optimized arrangements is suboptimal. Finally, we demonstrate the application of the BNM-TWM optimized beam arrangement for reconstructing temperature distributions of asymmetric butane flames, where parallel beam distributions were ineffective. The results effectively pinpoint the location of the flame, showing a temperature difference of less than 3% compared to thermocouple measurements for the center of the flame. These findings indicate that the developed beam arrangement optimization method has the potential to enhance the accuracy of TAS reconstruction under limited beam conditions and could be extended to other tomographic imaging systems.