Current Imaging Systems Research Articles

Optimization study on the compact thermal neutron imaging system based on portable D-T neutron generator

The distribution of lithium in lithium-ion batteries is critical to their performance and lifespan. Studies have shown that thermal neutron imaging can effectively perform non-destructive testing of this distribution. However, most current thermal neutron imaging systems rely on expensive, bulky, and immobile nuclear reactors or spallation neutron sources, significantly limiting their application scenarios. Therefore, this study adopts a portable D-T neutron generator as the neutron source and uses Monte Carlo simulation methods to optimize the design of key components in the thermal neutron imaging system. Simulation results indicate that the optimized thermal neutron imaging system achieves a thermal neutron flux of 8.36 × 104 n·cm−2·s−1 and a thermal neutron content (TNC) of 14.4% at a collimation ratio of 50. Within an imaging range of Φ100 mm, the non-uniformity of the thermal neutron fluence is approximately 2.07%. Simulation imaging results of lithium and iron step wedges demonstrate that the compact thermal neutron imaging system designed in this study can effectively distinguish between iron and lithium of the same thickness. This finding provides an important reference for subsequent detection of lithium distribution within lithium-ion batteries.

An Integrated Approach using YOLOv8 and ResNet, SeResNet & Vision Transformer (ViT) Algorithms based on ROI Fracture Prediction in X-ray Images of the Elbow.

In this study, we harnessed three cutting-edge algorithms' capabilities to refine the elbow fracture prediction process through X-ray image analysis. Employing the YOLOv8 (You only look once) algorithm, we first identified Regions of Interest (ROI) within the X-ray images, significantly augmenting fracture prediction accuracy. Subsequently, we integrated and compared the ResNet, the SeResNet (Squeeze-and-Excitation Residual Network) ViT (Vision Transformer) algorithms to refine our predictive capabilities. Furthermore, to ensure optimal precision, we implemented a series of meticulous refinements. This included recalibrating ROI regions to enable finer-grained identification of diagnostically significant areas within the X-ray images. Additionally, advanced image enhancement techniques were applied to optimize the X-ray images' visual quality and structural clarity. These methodological enhancements synergistically contributed to a substantial improvement in the overall accuracy of our fracture predictions. The dataset utilized for training, testing & validation, and comprehensive evaluation exclusively comprised elbow X-ray images, where predicting the fracture with three algorithms: Resnet50; accuracy 0.97, precision 1, recall 0.95, SeResnet50; accuracy 0.97, precision 1, recall 0.95 & ViTB- 16 with high accuracy of 0.99, precision same as the other two algorithms, with a recall of 0.95. This approach has the potential to increase the precision of diagnoses, lessen the burden of radiologists, easily integrate into current medical imaging systems, and assist clinical decision-making, all of which could lead to better patient care and health outcomes overall.

A programmable microfluidic platform to monitor calcium dynamics in microglia during inflammation

Neuroinflammation is characterized by the elevation of cytokines and adenosine triphosphate (ATP), which in turn activates microglia. These immunoregulatory molecules typically form gradients in vivo, which significantly influence microglial behaviors such as increasing calcium signaling, migration, phagocytosis, and cytokine secretion. Quantifying microglial calcium signaling in the context of inflammation holds the potential for developing precise therapeutic strategies for neurological diseases. However, the current calcium imaging systems are technically challenging to operate, necessitate large volumes of expensive reagents and cells, and model immunoregulatory molecules as uniform concentrations, failing to accurately replicate the in vivo microenvironment. In this study, we introduce a novel calcium monitoring micro-total analysis system (CAM-μTAS) designed to quantify calcium dynamics in microglia (BV2 cells) within defined cytokine gradients. Leveraging programmable pneumatically actuated lifting gate microvalve arrays and a Quake valve, CAM-μTAS delivers cytokine gradients to microglia, mimicking neuroinflammation. Our device automates sample handling and cell culture, enabling rapid media changes in just 1.5 s, thus streamlining the experimental workflow. By analyzing BV2 calcium transient latency to peak, we demonstrate location-dependent microglial activation patterns based on cytokine and ATP gradients, offering insights contrasting those of non-gradient-based perfusion systems. By harnessing advancements in microsystem technology to quantify calcium dynamics, we can construct simplified human models of neurological disorders, unravel the intricate mechanisms of cell-cell signaling, and conduct robust evaluations of novel therapeutics.

Spherical time-encoded radiation imaging simulations

Radiation source localization is important for nuclear nonproliferation and can be obtained using time-encoded imaging systems with unsegmented detectors. A scintillation crystal can be used with a moving coded-aperture mask to vary the detected count rate produced from radiation sources in the far field. The modulation of observed counts over time can be used to reconstruct an image with the known coded-aperture mask pattern. Current time-encoded imaging systems incorporate cylindrical coded-aperture masks and have limits to their fully coded imaging field-of-view. This work focuses on expanding the field-of-view to 4π by using a novel spherical coded-aperture mask. A regular icosahedron is used to approximate a spherical mask. This icosahedron consists of 20 equilateral triangles; the faces of which are each subdivided into four equilateral triangle-shaped voxels which are then projected onto a spherical surface, creating an 80-voxel coded-aperture mask. These polygonal voxels can be made from high-Z materials for gamma-ray modulation and/or low-Z materials for neutron modulation. In this work, we present Monte Carlo N-Particle (MCNP) simulations and simple models programmed in Mathematica to explore image reconstruction capabilities of this 80-voxel coded-aperture mask.

Abstract 5613: Study of long non-coding RNA TUG1 kinetics under replication stress conditions in cancer cell

Abstract Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides that are not translated into proteins. In contrast to protein-coding mRNAs, certain lncRNAs carry out their functions within the nucleus. A detailed elucidation of the kinetics of lncRNAs contributes to a comprehensive understanding of their bona fide biological characteristics. Consequently, advancements in the in vivo visualization of lncRNAs have continued. Currently, various techniques, including RNA imaging technologies, have been employed to facilitate real-time tracking and visualization of RNA molecules within living cells. Taurine upregulated gene 1 (TUG1) is an oncogenic lncRNA, playing a pivotal role in cancer cells by resolving R-loops formed at stalled replication forks, particularly under replication stress conditions such as Hydroxyurea (HU) treatment. Despite its significance, the dynamic regulation of TUG1 under replication stress conditions remains largely unknown. In this study, we developed a stem loop-based live imaging system enabling real-time visualization of TUG1, providing insights into its subcellular localization and dynamic roles in physiological contexts. Upon treatment with HU, a chemical that interferes with replication, TUG1 expression was rapidly upregulated in the nucleus within 20 minutes. Additionally, we observed TUG1 molecules relocating close to the nuclear membrane. Considering the reported relocation of collapsed forks to the nuclear pore complex, this suggests that TUG1 may be involved in this relocation structure. These data indicate that TUG1 is associated with the fork restore mechanism occurring at the relocated collapsed forks, and the current live-cell imaging system holds potential for further uncovering the critical roles of TUG1 in cancer cells. Our stem loop-based imaging may contribute to a more precise understanding of RNA dynamics in live cells. Citation Format: Tomoya Fujita, Miho Suzuki, Osamu Masui, Keiko Shinjo, Yutaka Kondo. Study of long non-coding RNA TUG1 kinetics under replication stress conditions in cancer cell [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5613.

Overcoming the diffraction limit by exploiting unmeasured scattering media

Scattering is not necessarily an obstacle to imaging. It can help enhance imaging performance beyond the reach of a lens system. However, current scattering-enhanced imaging systems require prior knowledge of the transmission matrix. There are also some techniques that do not require such prior knowledge to see through strongly scattering media, but the results are still limited by the optics used. Here we propose overcoming the diffraction limit through a visually opaque diffuser. By controlling the distance between the diffuser and lens system, light with higher spatial frequencies is scattered into the entrance pupil. With the deformed wavefront corrected, we experimentally achieved imaging with 3.39× enhancement of the Rayleigh limit. In addition, our method works well for objects that are 4× larger than the memory effect range and can maintain super-resolution performance for a depth of field 6.6× larger than a lens can achieve. Using our method, an obstructive scattering medium can enhance the throughput of the imaging system, even though the transmission matrix of the scattering medium has not been measured beforehand.

Generic arrays of surface-positioned and shallow-buried gold multi-shapes as reference samples to benchmark near-field microscopes. Part 1: Applications in s-SNOM depth imaging

A wide palette of nanoscale imaging techniques operating in the near-field regime has been reported to date, enabling an important number of scientific breakthroughs. While the tuning and benchmarking of near-field microscopes represent a very important step for optimizing the outputs of the imaging sessions, no generally acknowledged standards exist yet in terms of calibration of near-field microscopes, which would play an important role in fully exploiting the potential of these instruments. With this work, we aim to contribute to filling in this gap, by introducing a prototypical sample, that holds potential for becoming a benchmark with respect to comparing the performances of diverse near-field measurement techniques, including traditional, aperture based, scanning near field microscopy (SNOM), or apertureless variants, such as scattering-type scanning nearfield optical microscopy (s-SNOM). The proposed samples have been thoroughly simulated, and an easy fabrication procedure is presented and demonstrated. In this latter context, Au-SiO2 samples sharing different configurations, in terms of geometry, number and depth of contrast yielding layers, enabling both surface and sub-surface nanoscopy measurements, were designed and fabricated. We argue that the proposed prototypical samples can be highly useful for benchmarking the outputs of various near-field microscopy techniques, as they facilitate a broad range of tests, relevant for comparing the performances and accuracy of many diverse investigation methods. We also introduce a methodology for numerically simulating the samples and their near-field after illuminating them with light of different wavelengths, as well as their simple process flow. This methodology can considerably augment their future use as a prototypical sample for the evaluation and calibration of current and next generation near-field nanoscopy techniques. Experimental evidence on the usefulness of these samples as s-SNOM testing and benchmarking tools is provided in the context of differentiation of surface and sub-surface structures, and influence of tip-sample distance on attainable amplitude and phase signals. We consider these efforts to represent an important, required step, in advancing the near-field imaging field, with important potential to augment the outputs of current near-field imaging systems, and to facilitate the development and benchmarking of next generation of near-field instrumentation.

Measuring three-dimensional bubble dynamics for hydrogen production via water electrolysis

The commercialization of mass hydrogen production via water electrolysis is presently limited by low operational current densities. The optimal performance of electrolysis cells is significantly influenced by the substantial formation and residence of bubbles at high current densities. Thus, it is crucial to design electrodes with the ability for rapid bubble discharge to ensure appropriate bubble management. However, the quantitative volumetric measurements required to determine the bubble discharge ability of an electrode are not yet sufficiently accurate. This paper describes a quantitative volumetric method that combines a stereoscopic shadowgraph imaging system with particle tracking velocimetry (PTV) to measure the three-dimensional position, size, and velocity of micrometer-sized bubbles. The proposed method successfully captures hydrogen bubbles larger than 30 μm bubbles in an alkaline water electrolyzer. Considering the different luminance patterns of small bubbles (r ≤ 4 pixels) and large bubbles (r > 4 pixels) in the current imaging system, a bubble-size adaptive detection algorithm is established based on the cascade correlation method to obtain the two-dimensional centroid coordinates and radius of the observed bubbles. The bubble size information is also introduced into a two-view PTV algorithm for retrieving the Lagrangian trajectory of each bubble. Both the bubble detection and PTV algorithms are validated using synthetic datasets. Once the bubble trajectories are resolved successfully, the three-dimensional bubble velocity is obtained, and the actual bubble sizes are further corrected using the depth information. Analysis of the trajectory and velocity components indicates the existence of lateral bubble motion, reflecting interactions among bubbles. The rise velocity is positively correlated with the bubble radius in two regions, respectively, and the deviation from the theoretical value reveals the influence of non-buoyancy factors. The proposed technique provides effective diagnostics of the three-dimensional dynamic characteristics of micrometer-sized bubbles and can be used to evaluate and design bubble management systems for various electrochemical energy conversion devices.

Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging.

Photoacoustic (PA) imaging provides optical contrast at relatively large depths within the human body, compared to other optical methods, at ultrasound (US) spatial resolution. By integrating real-time PA and US (PAUS) modalities, PAUS imaging has the potential to become a routine clinical modality bringing the molecular sensitivity of optics to medical US imaging. For applications where the full capabilities of clinical US scanners must be maintained in PAUS, conventional limited view and bandwidth transducers must be used. This approach, however, cannot provide high-quality maps of PA sources, especially vascular structures. Deep learning (DL) using data-driven modeling with minimal human design has been very effective in medical imaging, medical data analysis, and disease diagnosis, and has the potential to overcome many of the technical limitations of current PAUS imaging systems. The primary purpose of this article is to summarize the background and current status of DL applications in PAUS imaging. It also looks beyond current approaches to identify remaining challenges and opportunities for robust translation of PAUS technologies to the clinic.

Robust cardiac segmentation corrected with heuristics.

Cardiovascular diseases related to the right side of the heart, such as Pulmonary Hypertension, are some of the leading causes of death among the Mexican (and worldwide) population. To avoid invasive techniques such as catheterizing the heart, improving the segmenting performance of medical echocardiographic systems can be an option to early detect diseases related to the right-side of the heart. While current medical imaging systems perform well segmenting automatically the left side of the heart, they typically struggle segmenting the right-side cavities. This paper presents a robust cardiac segmentation algorithm based on the popular U-NET architecture capable of accurately segmenting the four cavities with a reduced training dataset. Moreover, we propose two additional steps to improve the quality of the results in our machine learning model, 1) a segmentation algorithm capable of accurately detecting cone shapes (as it has been trained and refined with multiple data sources) and 2) a post-processing step which refines the shape and contours of the segmentation based on heuristics provided by the clinicians. Our results demonstrate that the proposed techniques achieve segmentation accuracy comparable to state-of-the-art methods in datasets commonly used for this practice, as well as in datasets compiled by our medical team. Furthermore, we tested the validity of the post-processing correction step within the same sequence of images and demonstrated its consistency with manual segmentations performed by clinicians.

Hypoxia dose painting in SBRT – the virtual clinical trial approach

Background Treating hypoxic tumours remains a challenge in radiotherapy as hypoxia leads to enhanced tumour aggressiveness and resistance to radiation. As escalating the doses is rarely feasible within the healthy tissue constraints, dose-painting strategies have been explored. Consensus about the best of care for hypoxic tumours has however not been reached because, among other reasons, the limits of current functional in-vivo imaging systems in resolving the details and dynamics of oxygen transport in tissue. Computational modelling of the tumour microenvironment enables the design and conduction of virtual clinical trials by providing relationships between biological features and treatment outcomes. This study presents a framework for assessing the therapeutic influence of the individual characteristics of the vasculature and the resulting oxygenation of hypoxic tumours in a virtual clinical trial on dose painting in stereotactic body radiotherapy (SBRT) circumventing the limitations of the imaging systems. Material and methods The homogeneous doses required to overcome hypoxia in simulated SBRT treatments of 1, 3 or 5 fractions were calculated for tumours with heterogeneous oxygenation derived from virtual vascular networks. The tumour control probability (TCP) was calculated for different scenarios for oxygenation dynamics resulting on cellular reoxygenation. Results A three-fractions SBRT treatment delivering 41.9 Gy (SD 2.8) and 26.5 Gy (SD 0.1) achieved only 21% (SD 12) and 48% (SD 17) control in the hypoxic and normoxic subvolumes, respectively whereas fast reoxygenation improved the control by 30% to 50%. TCP values for the individual tumours with similar characteristics, however, might differ substantially, highlighting the crucial role of the magnitude and time evolution of hypoxia at the microscale. Conclusion The results show that local microvascular heterogeneities may affect the predicted outcome in the hypoxic core despite escalated doses, emphasizing the role of theoretical modelling in understanding of and accounting for the dominant factors of the tumour microenvironment.

Full-angle high-reflection ultrathin composite film realizing high spatial resolution of scintillation crystal array

The performance of current nuclear medicine imaging systems is largely limited by the performance of detectors, and high spatial resolution detectors require high optical yield scintillator arrays. In this work, we simulated and designed for the first time a distributed Bragg reflector (multilayer dielectric film) that covers the entire lutetium yttrium oxyorthosilicate emission spectral band and consists of three 1/4 wavelength (λ/4) primary film systems centered at 420, 500, and 575 nm. In order to achieve ultrahigh reflectivity at the full incidence angle of the scintillator emitting surface, we propose a master optical configuration combining the dielectric film with a metal film/diffuse reflection adhesive. To explain this mechanism, we also simulated the change in reflectivity of the actual inner surface light collection. Experimental results show that a combination of a highly reflective reflector can achieve full-angle high reflectance at the total angle of incidence. We find that the dielectric film does not change the total reflection structure inside the crystal, while the light-blocking layer changes and increases the angular reflection of the dielectric film about the angle. These findings provide important insights into surface treatment as well as the design of scintillation crystal arrays, with far-reaching implications for high spatial resolution optical imaging systems.

Real-Time Segmentation of Artificial Targets Using a Dual-Modal Efficient Attention Fusion Network

The fusion of spectral–polarimetric information can improve the autonomous reconnaissance capability of unmanned aerial vehicles (UAVs) in detecting artificial targets. However, the current spectral and polarization imaging systems typically suffer from low image sampling resolution, which can lead to the loss of target information. Most existing segmentation algorithms neglect the similarities and differences between multimodal features, resulting in reduced accuracy and robustness of the algorithms. To address these challenges, a real-time spectral–polarimetric segmentation algorithm for artificial targets based on an efficient attention fusion network, called ESPFNet (efficient spectral–polarimetric fusion network) is proposed. The network employs a coordination attention bimodal fusion (CABF) module and a complex atrous spatial pyramid pooling (CASPP) module to fuse and enhance low-level and high-level features at different scales from the spectral feature images and the polarization encoded images, effectively achieving the segmentation of artificial targets. Additionally, the introduction of the residual dense block (RDB) module refines feature extraction, further enhancing the network’s ability to classify pixels. In order to test the algorithm’s performance, a spectral–polarimetric image dataset of artificial targets, named SPIAO (spectral–polarimetric image of artificial objects) is constructed, which contains various camouflaged nets and camouflaged plates with different properties. The experimental results on the SPIAO dataset demonstrate that the proposed method accurately detects the artificial targets, achieving a mean intersection-over-union (MIoU) of 80.4%, a mean pixel accuracy (MPA) of 88.1%, and a detection rate of 27.5 frames per second, meeting the real-time requirement. The research has the potential to provide a new multimodal detection technique for enabling autonomous reconnaissance by UAVs in complex scenes.

Angiographic velocimetry analysis using contrast dilution gradient method with a 1000 frames per second photon-counting detector.

Contrast dilution gradient (CDG) analysis is a quantitative method allowing blood velocity estimation using angiographic acquisitions. Currently, CDG is restricted to peripheral vasculature due to the suboptimal temporal resolution of current imaging systems. We investigate extension of CDG methods to the flow conditions of proximal vasculature using 1000 frames per second (fps) high-speed angiographic (HSA) imaging. We performed in-vitro HSA acquisitions using the XC-Actaeon detector and 3D-printed patient-specific phantoms. The CDG approach was used for blood velocity estimation expressed as the ratio of temporal and spatial contrast gradients. The gradients were extracted from 2D contrast intensity maps synthesized by plotting intensity profiles along the arterial centerline at each frame. In-vitro results obtained at various frame rates via temporal binning of 1000 fps data were retrospectively compared to computational fluid dynamics (CFD) velocimetry. Full-vessel velocity distributions were estimated at 1000 fps via parallel line expansion of the arterial centerline analysis. Using HSA, the CDG method displayed agreement with CFD at or above 250 fps [mean-absolute error (MAE): , ]. Relative velocity distributions correlated well with CFD at 1000fps with universal underapproximation due to effects of pulsatile contrast injection (MAE: 4.3cm/s). Using 1000fps HSA, CDG-based extraction of velocities across large arteries is possible. The method is sensitive to noise; however, image processing techniques and a contrast injection, which adequately fills the vessel assist algorithm accuracy. The CDG method provides high resolution quantitative information for rapidly transient flow patterns observed in arterial circulation.

Abstract 6205: Visualizing dynamics of long non-coding RNA TUG1 in live glioblastoma cells

Abstract Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides that are not translated into proteins. lncRNA TUG1 (taurine upregulated gene 1) is highly expressed in various cancers, including glioblastoma. Previously we found that TUG1 is an essential molecule for cancer cells to cope with excessive replication stress in the S phase. Albeit its importance to cell cycle-related function, how it is dynamically regulated during the cell cycle is entirely unknown. Here, we established a system visualizing endogenous TUG1 molecules in live cells. TUG1 was tagged with stem-loop motifs recognized by a specific binding protein fused with GFP. We tracked TUG1 RNA by the GFP signal for 22 hours in T98G glioblastoma cells and found that the expression started from the later G1 phase. When cells were treated with Hydroxy Urea, a chemical that interferes with replication and increases replication stress, the expression of TUG1 was further upregulated in the S phase within 20 mins. We also observed some TUG1 molecules accumulated in several nuclear locations. It may indicate that TUG1 assembles to protect damaged DNA loci efficiently. Our results suggest that the expression of TUG1 is regulated in a cell cycle-dependent manner and is strongly connected with its function of TUG1. The current live-cell imaging system could help further discover the critical roles of TUG1 in cancer cells. Citation Format: Ayaka Minematsu. Visualizing dynamics of long non-coding RNA TUG1 in live glioblastoma cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6205.

Improving Magnetic Field Response of Eddy Current Magneto-Optical Imaging for Defect Detection in Carbon Fiber Reinforced Polymers

A large number of carbon fiber reinforced polymers have been applied to aircraft and automobiles, and many nondestructive testing methods have been studied to detect their defects. Eddy current magneto-optical imaging nondestructive testing technology has been widely used in the detection of metal materials such as aircraft skin, but it usually requires a large excitation current and, at present, can only detect metal materials with high conductivity. In order to take full advantage of the innate benefits and efficiency of eddy current magneto-optic imaging and enable it to detect defects in carbon fiber reinforced polymers with weak conductivity, it is necessary to improve the magnetic field response of the eddy current magneto-optic imaging system and explore suitable excitation and detection methods. The scanning eddy current magneto-optical imaging nondestructive testing device built in this study has improved the magnetic field response of the system, and the eddy current magneto-optical phase imaging testing method has been proposed to detect the crack defects of carbon fiber reinforced polymers. The effectiveness of the method has been verified by simulation and experiment.

Gold cluster encapsulated liposomes: theranostic agent with stimulus triggered release capability

Cancer is a major cause of death worldwide. Cancer-resistant to chemo or radiotherapy treatment is a challenge that could be overcome by a nanotechnology approach. Providing a theranostic nano-platform for different cancer treatment strategies could be revolutionary. Here we introduce a multifunctional theranostic nanostructure which has the capacity for improving cancer diagnosis and treatment through better chemo and radiotherapy and current x-ray imaging systems through co-encapsulation of a small gold cluster and anticancer drug doxorubicin. 2nm gold clusters represent good heating under radio frequency electric field (RF-EF) exposure and have been used for in vitro hyperthermia treatment of cancerous cells. Liposomal doxorubicin (169 ± 19.8nm) with gold clusters encapsulation efficiency of 13.2 ± 3.0% and doxorubicin encapsulation efficiency of 64.7 ± 0.7% were prepared and studied as a theranostic agent with a high potential in different cancer treatment modalities. Exposure to a radiofrequency electric field on prepared formulation caused 20.2 ± 2.1% drug release and twice decreasing of IC50 on colorectal carcinoma cells. X-ray attenuation efficiency of the liposomal gold cluster was better than commercial iohexol and free gold clusters in different concentrations. Finally, treatment of gold clusters on cancerous cells results in a significant decrease in the viability of irradiated cells to cobalt-60 beam. Based on these experiments, we concluded that the conventional liposomal formulation of doxorubicin that has been co-encapsulated with small gold clusters could be a suitable theranostic nanostructure for cancer treatment and merits further investigation.

Implementation of miniaturized modular-array fluorescence microscopy for long-term live-cell imaging.

Fluorescence microscopy imaging of live cells has provided consistent monitoring of dynamic cellular activities and interactions. However, because current live-cell imaging systems are limited in their adaptability, portable cell imaging systems have been adapted by a variety of strategies, including miniaturized fluorescence microscopy. Here, we provide a protocol for the construction and operational process of miniaturized modular-array fluorescence microscopy (MAM). The MAM system is built in a portable size (15c m×15c m×3c m) and provides in situ cell imaging inside an incubator with a subcellular lateral resolution (∼3µm). We demonstrated the improved stability of the MAM system with fluorescent targets and live HeLa cells, enabling long-term imaging for 12h without the need for external support or post-processing. We believe the protocol could guide scientists to construct a compact portable fluorescence imaging system and perform time-lapse in situ single-cell imaging and analysis.

Sentinel lymph node mapping in patients with breast cancer using a photoacoustic/ultrasound dual-modality imaging system with carbon nanoparticles as the contrast agent: a pilot study.

Assessing the metastatic status of axillary lymph nodes is a common clinical practice in the staging of early breast cancers. Yet sentinel lymph nodes (SLNs) are the regional lymph nodes believed to be the first stop along the lymphatic drainage path of the metastasizing cancer cells. Compared to axillary lymph node dissection, sentinel lymph node biopsy (SLNB) helps reduce morbidity and side effects. Current SLNB methods, however, still have suboptimum properties, such as restrictions due to nuclide accessibility and a relatively low therapeutic efficacy when only a single contrast agent is used. To overcome these limitations, researchers have been motivated to develop a non-radioactive SLN mapping method to replace or supplement radionuclide mapping. We proposed and demonstrated a clinical procedure using a dual-modality photoacoustic (PA)/ultrasound (US) imaging system to locate the SLNs to offer surgical guidance. In our work, the high contrast of PA imaging and its specificity to SLNs were based on the accumulation of carbon nanoparticles (CNPs) in the SLNs. A machine-learning model was also trained and validated to distinguish stained SLNs based on single-wavelength PA images. In the pilot study, we imaged 11 patients in vivo, and the specimens from 13 patients were studied ex vivo. PA/US imaging identified stained SLNs in vivo without a single false positive (23 SLNs), yielding 100% specificity and 52.6% sensitivity based on the current PA imaging system. Our machine-learning model can automatically detect SLNs in real time. In the new procedure, single-wavelength PA/US imaging uses CNPs as the contrast agent. The new system can, with that contrast agent, noninvasively image SLNs with high specificity in real time based on the unique features of the SLNs in the PA images. Ultimately, we aim to use our systems and approach to substitute or supplement nuclide tracers for a non-radioactive, less invasive SLN mapping method in SLNB for the axillary staging of breast cancer.

Automatic force-controlled 3D photoacoustic system for human peripheral vascular imaging.

Photoacoustic (PA) imaging provides unique advantages in peripheral vascular imaging due to its high sensitivity to hemoglobin. Nevertheless, limitations associated with handheld or mechanical scanning by stepping motor techniques have precluded photoacoustic vascular imaging from advancing to clinical applications. As clinical applications require flexibility, affordability, and portability of imaging equipment, current photoacoustic imaging systems developed for clinical applications usually use dry coupling. However, it inevitably induces uncontrolled contact force between the probe and the skin. Through 2D and 3D experiments, this study proved that contact forces during the scanning could significantly affect the vascular shape, size, and contrast in PA images, due to the morphology and perfusion alterations of the peripheral blood vessels. However, there is no available PA system that can control forces accurately. This study presented an automatic force-controlled 3D PA imaging system based on a six-degree-of-freedom collaborative robot and a six-dimensional force sensor. It is the first PA system that achieves real-time automatic force monitoring and control. This paper's results, for the first time, demonstrated the ability of an automatic force-controlled system to acquire reliable 3D PA images of peripheral blood vessels. This study provides a powerful tool that will advance PA peripheral vascular imaging to clinical applications in the future.