Improvement In Spatial Resolution Research Articles

Prompt gamma imaging system in particle therapy: a mini-review

Accurate in-vivo verification of beam range and dose distribution is crucial for the safety and effectiveness of particle therapy. Prompt gamma (PG) imaging, as a method for real-time verification, has gained prominence in this area. Currently, several PG imaging systems are under development, including gamma electron vertex imaging (GEVI), the Compton camera, the slit camera, and the multi-array type collimator camera. However, challenges persist in dose prediction accuracy, largely due to patient positioning uncertainty and anatomical changes. Although each system demonstrates potential in verifying PG range, further improvements in detection efficiency, spatial resolution, background reduction, and integration into clinical workflows are essential.

Sub-pixel target fine spatial feature extraction method based on aperture coding and micro-scanning imaging mechanism

The small imaging size of targets over long distances results in the loss of geometry and spatial features. Current methods are subject to sampling limitations and cannot accurately capture the spatial features of sub-pixel targets. This paper proposes a method to accurately locate and extract the fine spatial features of sub-pixel targets through aperture coding and micro-scanning imaging. First, the formation mechanism of imaging features for sub-pixel targets is analyzed. Second, the optical aperture is anisotropically coded in different directions to modulate the spreading spots of the target. The primary spreading direction and the center of the anisotropic spreading spots are extracted. The contour and the location of the target are determined from the spreading length and the intersections of the primary spreading directions. Then, the target is sampled by different detector units through various micro-scanning offsets. The pixel units containing different sub-pixel components of the target after offset are determined based on the location results. The fine spatial distribution of the sub-pixel target is reconstructed based on the intensity variations in the pixel units containing the target. Finally, the accuracy of the sub-pixel target fine spatial feature extraction method is validated. The results show a sub-pixel localization error of less than 0.02 and an effective improvement of the sub-pixel target spatial resolution. This paper provides significant potential for improving the ability to capture spatial features of targets over long distances.

Learned high-resolution cardiac CT imaging from ultra-high-resolution PCD-CT.

Coronary computed tomography angiography (cCTA) is a widely used non-invasive diagnostic exam for patients with coronary artery disease (CAD). However, most clinical CT scanners are limited in spatial resolution from use of energy-integrating detectors (EIDs). Radiological evaluation of CAD is challenging, as coronary arteries are small (3-4 mm diameter) and calcifications within them are highly attenuating, leading to blooming artifacts. As such, this is a task well suited for high spatial resolution. Recently, photon-counting-detector (PCD) CT became commercially available, allowing for ultra-high resolution (UHR) data acquisition. However, PCD-CTs are costly, restricting widespread accessibility. To address this problem, we propose a super resolution convolutional neural network (CNN): ILUMENATE (Improved LUMEN visualization through Artificial super-resoluTion imagEs), creating a high resolution (HR) image simulating UHR PCD-CT. The network was trained and validated using patches extracted from 8 patients with a modified U-Net architecture. Training input and labels consisted of UHR PCD-CT images reconstructed with a smooth kernel degrading resolution (LR input) and sharp kernel (HR label). The network learned the resolution difference and was tested on 5 unseen LR patients. We evaluated network performance quantitatively and qualitatively through visual inspection, line profiles to assess spatial resolution improvements, ROIs for CT number stability and noise assessment, structural similarity index (SSIM), and percent diameter luminal stenosis. Overall, ILUMENATE improved images quantitatively and qualitatively, creating sharper edges more closely resembling reconstructed HR reference images, maintained stable CT numbers with less than 4% difference, reduced noise by 28%, maintained structural similarity (average SSIM = 0.70), and reduced percent diameter stenosis with respect to input images. ILUMENATE demonstrates potential impact for CAD patient management, improving the quality of LR CT images bringing them closer to UHR PCD-CT images.

Experimentally Closing the Balance of Progress of Reaction in Premixed Turbulent Combustion in the Thin Flame Regime

We investigate the possibility of determining the local turbulent flame speed by measuring the individual terms in the balance of a mean progress of reaction variable for the case of a low turbulence methane-air Bunsen flame in the thin flame regime. Velocity distributions and flame edge positions were measured by particle image velocimetry techniques at 3 kHz for a flame stabilized by a surrounding pilot of the same stoichiometry, for a turbulent Reynolds number around 66 and Karlovitz numbers of the order of 4. The conservation equation for mean progress variable was analyzed along different streamlines as a balance of terms expressed as velocities, including terms for convection, turbulent diffusion, mean reaction, and turbulent and molecular diffusion. Each term was estimated from local velocities and flame locations using a thin flame approximation, and their uncertainty was evaluated based on propagation of experimentally measured statistical correlations. The largest terms were the convective and reaction terms, as expected, with smaller roles for turbulent and molecular diffusion across the flame brush. Countergradient diffusion and transition to gradient diffusion were observed across the flame brush. Closure of the balance of terms in the conservation equations using independently measured terms was not consistently achieved across the flame brush within the reckoned uncertainties, arriving at a balance within 20–30% of the absolute value. Testable hypotheses are offered for the possible reasons for the mismatch, including the role of spatial filtering and 3D effects on the reaction rate term. Finally, the experiments identify the inaccuracies in measuring a true local turbulent flame speed, and suggest a consistent methodology to reduce errors in such estimations. This is the first time such a detailed experimental closure is attempted for any configuration. The results suggest that the significant improvements in spatial resolution are necessary for a full closure.

Application of Satellite-Based Remote Sensing for the Management of Pavement Infrastructure Assets

The long-term monitoring of transportation infrastructure assets at a lower cost and with short mobilization time is of significant interest to both state and federal transportation agencies in the U.S. Because of the significant improvement in spatial and temporal resolution of synthetic aperture radar (SAR) remote sensing systems and a notable reduction in the cost of data acquisition, SAR has now become a viable method to provide economic and rapid condition assessment of transportation assets. A research study was developed and performed to comprehensively perform the inspection and characterization of a pavement surface based on the amplitude of backscattering of an X-band radar. In situ characterization of the test site was first performed using traditional inertial profilers and aerial photogrammetry with unmanned aerial vehicle (UAV) surveys. The results from these in situ methods were compared with the corrected amplitude of the SAR data, which indicated that the distribution of surface roughness values computed from the inertial profiler, UAV, and SAR exhibited similar probability densities at various segmental lengths considered in this study. This suggested that the problematic areas that are evident during in situ characterization can be delineated and quantified based on the normalized radar cross section of the pavement surface. Overall, the outcome of this research exhibits the potential of SAR for future transportation asset management undertakings, and the systematic framework developed as a part of this research could be of significant interest to engineers and transportation practitioners.

Near-Infrared Perylenecarboximide Fluorophores for Live-Cell Super-Resolution Imaging.

Organic near-infrared (NIR) photoblinking fluorophores are highly desirable for live-cell super-resolution imaging based on single-molecule localization microscopy (SMLM). Herein we introduce a novel small chromophore, PMIP, through the fusion of perylenecarboximide with 2,2-dimetheylpyrimidine. PMIP exhibits an emission maximum at 732 nm with a high fluorescence quantum yield of 60% in the wavelength range of 700-1000 nm and excellent photoblinking without any additives. With resorcinol-functionalized PMIP (PMIP-OH), NIR SMLM imaging of lysosomes is demonstrated for the first time in living mammalian cells under physiological conditions. Moreover, metabolically labeled nascent DNA is site-specifically detected using azido-functionalized PMIP (PMIP-N3) via click chemistry, thereby enabling the super-resolution imaging of nascent DNA in phosphate-buffered saline with a 9-fold improvement in spatial resolution. These results indicate the potential of PMIP-based NIR blinking fluorophores for biological applications of SMLM.

A Multiplexed 32 × 32 2D Matrix Array Transducer for Flexible Sub-Aperture Volumetric Ultrasound Imaging.

A fully-sampled two-dimensional (2D) matrix array ultrasonic transducer is essential for fast and accurate three-dimensional (3D) volumetric ultrasound imaging. However, these arrays, usually consisting of thousands of elements, not only face challenges of poor performance and complex wiring due to high-density elements and small element sizes but also put high requirements for electronic systems. Current commercially available fully-sampled matrix arrays, dividing the aperture into four fixed sub-apertures to reduce system channels through multiplexing are widely used. However, the fixed sub-aperture configuration limits imaging flexibility and the gaps between sub-apertures lead to reduced imaging quality. In this study, we propose a high-performance multiplexed matrix array by the design of 1-3 piezocomposite and gapless sub-aperture configuration, as well as optimized matching layer materials. Furthermore, we introduce a sub-aperture volumetric imaging method based on the designed matrix array, enabling high-quality and flexible 3D ultrasound imaging with a low-cost 256-channel system. The influence of imaging parameters, including the number of sub-apertures and steering angle on imaging quality was investigated by simulation, in vitro and in vivo imaging experiments. The fabricated matrix array has a center frequency of 3.4 MHz and a -6 dB bandwidth of above 70%. The proposed sub-aperture volumetric imaging method demonstrated a 10% improvement in spatial resolution, a 19% increase in signal-to-noise ratio, and a 57.7% increase in contrast-to-noise ratio compared with the fixed sub-aperture array imaging method. This study provides a new strategy for high-quality volumetric ultrasound imaging with a low-cost system.

Photon-counting CT in Thoracic Imaging: Early Clinical Evidence and Incorporation Into Clinical Practice.

Photon-counting CT (PCCT) is an emerging advanced CT technology that differs from conventional CT in its ability to directly convert incident x-ray photon energies into electrical signals. The detector design also permits substantial improvements in spatial resolution and radiation dose efficiency and allows for concurrent high-pitch and high-temporal-resolution multienergy imaging. This review summarizes (a) key differences in PCCT image acquisition and image reconstruction compared with conventional CT; (b) early evidence for the clinical benefit of PCCT for high-spatial-resolution diagnostic tasks in thoracic imaging, such as assessment of airway and parenchymal diseases, as well as benefits of high-pitch and multienergy scanning; (c) anticipated radiation dose reduction, depending on the diagnostic task, and increased utility for routine low-dose thoracic CT imaging; (d) adaptations for thoracic imaging in children; (e) potential for further quantitation of thoracic diseases; and (f) limitations and trade-offs. Moreover, important points for conducting and interpreting clinical studies examining the benefit of PCCT relative to conventional CT and integration of PCCT systems into multivendor, multispecialty radiology practices are discussed.

Resolving inter-crystal scatter in a light-sharing depth-encoding PET detector

Objective. Inter-crystal scattering (ICS) in light-sharing positron emission tomography (PET) detectors leads to ambiguity in positioning the initial interaction, which significantly degrades the contrast, quantitative accuracy, and spatial resolution of the resulting image. Here, we attempt to resolve the positioning ambiguity of ICS in a light-sharing depth-encoding detector by exploiting the confined, deterministic light-sharing enabled by the segmented light guide unique to Prism-PET. Approach. We first considered a test case of ICS between two adjacent crystals using an analytical and a neural network approach. The analytical approach used a Bayesian estimation framework constructed from a scatter absorption model—the prior—and a detector response model—the likelihood. A simple neural network was generated for the same scenario, to provide mutual validation for the findings. Finally, we generalized the solution to three-dimensional event positioning that handles all events in the photopeak using a convolutional neural network with unique architecture that separately predicts the identity and depth-of-interaction (DOI) of the crystal containing the first interaction. Main results. The analytical Bayesian method generated an estimation error of 20.5 keV in energy and 3.1 mm in DOI. Further analysis showed that the detector response model was sufficiently robust to achieve adequate performance via maximum likelihood estimation (MLE), without prior information. We then found convergent results using a simple neural network. In the generalized solution using a convolutional neural network, we found crystal identification accuracy of 83% and DOI estimation error of 3.0 mm across all events. Applying this positioning algorithm to simulated data, we demonstrated significant improvements in image quality over the baseline, centroid-based positioning approach, attaining 38.9% improvement in intrinsic spatial resolution and enhanced clarity in hot spots of diameters 0.8 to 2.5 mm. Significance. The accuracy of our findings exceeds those of previous reports in the literature. The Prism-PET light guide, mediating confined and deterministic light-sharing, plays a key role in ICS recovery, as its mathematical embodiment—the detector response model—was the essential driver of accuracy in our results.

Synthetic Fourier Domain Optical Coherence Tomography

A novel approach for image formation in optical coherence tomography (OCT) and microscopy is presented. The depth resolution of OCT, including recently developed nanosensitive OCT (nsOCT), is limited by the spectral bandwidth of the light source used for illumination. The proposed approach, synthetic OCT (synOCT), permits label‐free, depth‐resolved quantitative visualization of the subwavelength‐sized structures with nanosensitivity. Using synOCT it is possible to estimate the contribution of axial Fourier components of an object's structure in image formation at each small volume within the image. The size of such areas can be smaller than the resolution limit of the imaging system that provides potential for super‐resolution imaging. Visualization of the subwavelength periodic structures and quantitative visualization of the subwavelength internal structures of highly scattering biological samples, within voxels smaller than resolution limit of the imaging system, are demonstrated. In contrast to nsOCT, the trade‐off between spectral and spatial resolution is removed which results in dramatic improvement of both spectral and spatial resolution in synOCT relative to nsOCT.

Graphene Electrode for Studying CO2 Electroreduction Nanocatalysts under Realistic Conditions in Microcells.

The ability to resolve the dynamic evolution of electrocatalytically induced processes with electrochemical liquid-phase electron microscopy (EM)is limited by the microcell configuration. Herein, a free-standing tri-layer graphene is integrated as a membrane and electrode material into the electrochemical chip and its suitability as a substrate electrode at the high cathodic potentials required for CO2 electroreduction (CO2ER) is evaluated. The three-layer stacked graphene is transferred onto an in-house fabricated single-working electrode chip for use with bulk-like reference and counter electrodes to facilitate evaluation of its effectiveness. Electrochemical measurements show that the graphene working electrode exhibits a wider inert cathodic potential range than the conventional glassy carbon electrode while achieving good charge transfer properties for nanocatalytic redox reactions. Operando scanning electron microscopy studies clearly demonstrate the improvement in spatial resolution but reveal a synergistic effect of the electron beam and the applied potential that limits the stability time window of the graphene-based electrochemical chip. By optimizing the operating conditions, in situ monitoring of Cu nanocube degradation is achieved at the CO2ER potential of -1.1V versus RHE. Thus, this improved microcell configuration allows EM observation of catalytic processes at potentials relevant to real systems.

Tissue ultrasound imaging based on wavelet correlation analysis and pulse-inversion technique.

Pulse-inversion-based tissue harmonic imaging has been utilized for many years because it can effectively eliminate the harmonic leakage and produce low side-lobe. However, the pulse inversion method is sensitive to imaging object movements, which may result in motion artifacts. Spatial resolution and contrast were limited. To improve ultrasound image quality by a new pulse-inversion-based tissue harmonic imaging technique. Continuous wavelet transform is applied to investigate the correlation between mother wavelet and the received echoes from two opposite pulses. To get a better correlation, a novel mother wavelet named 'tissue wavelet' is designed based on the Khokhlov-Zabolotskaya- Kuznetsov (KZK) wave equation. Radio frequency data were obtained from open Ultrasonix SonixTouch imaging system. Experiments were carried on ultrasonic tissue phantom, human carotid artery and human liver. The average improvement of lateral spatial resolution is 49.52% compared to pulse-inversion-based tissue second-harmonic Imaging (PIHI). Contrast ratio (CR) and contrast-to-noise ratio (CNR) increased by 5.55 dB and 1.40 dB over PIHI. Tissue wavelet performs better than Mexh and Morl wavelet in lateral spatial resolution, CR, and CNR. The proposed technique effectively improves the imaging quality in lateral spatial resolution, CR, and CNR.

Full field-of-view hexagonal lattice structured illumination microscopy based on the phase shift of electro-optic modulators.

High throughput has become an important research direction in the field of super-resolution (SR) microscopy, especially in improving the capability of dynamic observations. In this study, we present a hexagonal lattice structured illumination microscopy (hexSIM) system characterized by a large field of view (FOV), rapid imaging speed, and high power efficiency. Our approach employs spatial light interference to generate a two-dimensional hexagonal SIM pattern, and utilizes electro-optical modulators for high-speed phase shifting. This design enables the achievement of a 210-µm diameter SIM illumination FOV when using a 100×/1.49 objective lens, capturing 2048 × 2048 pixel images at an impressive 98 frames per second (fps) single frame rate. Notably, this method attains a near 100% full field-of-view and power efficiency, with the speed limited only by the camera's capabilities. Our hexSIM demonstrates a substantial 1.73-fold improvement in spatial resolution and necessitates only seven phase-shift images, thus enhancing the imaging speed compared to conventional 2D-SIM.

Space-Specific Mixing Excitation for High-SNR Spatial Encoding in Magnetic Particle Imaging.

Magnetic Particle Imaging (MPI) is a radiation-free tracer-based imaging technology that visualizes the spatial distribution of superparamagnetic iron oxide nanoparticles. Conventional spatial encoding methods in MPI rely on a gradient magnetic field with a constant gradient strength to generate a field-free point or line for spatial scanning. However, increasing the gradient strength can enhance theoretical spatial resolution but also lead to a decrease in the Signal-to-Noise Ratio (SNR) and sensitivity of the imaging system. This poses a technical challenge in balancing spatial resolution and sensitivity, necessitating intricate hardware design. To address this, we present a Space-Specific Mixing Excitation (SSME) technique for achieving high-SNR spatial encoding in MPI. By utilizing a dual-frequency excitation magnetic field with a non-homogeneous field strength, magnetic particles at each position generate unique intermodulation responses. By performing multi-channel acquisitions across the entire field of view, high SNR MPI signals can be acquired. When combined with reconstruction techniques based on system matrix, multi-dimensional SSME-MPI can be achieved. The effectiveness of the proposed method was validated through phantom and in vivo imaging experiments. The results demonstrate significant improvements in sensitivity (3.6-fold improvement) and spatial resolution (better than 1 mm) without any hardware modifications. These findings demonstrate the capability of SSME to enhance both the spatial resolution and sensitivity of MPI. This method provides a solution to the ongoing challenge of balancing spatial resolution and sensitivity in MPI, potentially facilitating the implementation of MPI in a wider range of medical applications.

Spatial resolution improvement by a super-resolution technique depending on training process in the background-orientated schlieren analyses

The effectiveness of the enhanced deep super-resolution network (EDSR), which is a super-resolution technique based on the deep convolutional neural network, is investigated for the improvement of spatial resolution during a background-orientated schlieren (BOS) analysis, and its suitable training process in the EDSR method is clarified. Consequently, a training dataset consisting of the simple dot patterns leads to a better image quality due to super-resolution because the image captured in the BOS measurement shows the similar dot patterns. When the image is enlarged at a large magnification ratio, it is important to adjust an image size in the training dataset individually for each magnification ratio, thereby obtaining a good estimation accuracy of the pixel displacement in the BOS analysis. A measurement error is improved by 62% compared with that of the Bicubic method, which is a classical spatial resolution improvement technique at the magnification ratio of 8. The present result shows that the EDSR method with the best training conditions provides a reasonable pixel displacement vector field up to the magnification ratio of 8 for the BOS analysis; however, its effectiveness depends on flow structure.

Large Eddy Simulation of the Flow around a Generic Submarine under Straight-Ahead and 10° Yaw Conditions

Aiming towards a better understanding of the flow field around a fully appended Joubert BB2 submarine model, and in order to complement the experimental investigations of the wake of the hydroplanes and sail, large eddy simulation (LES) with the dynamic Smagorinsky model was conducted. Three sets of grids with a maximum grid number of up to 228 million were designed to perform the LES simulation for the Joubert BB2 under 10° yaw conditions, with a freestream Reynolds number based on the local freestream velocity and a hull length of ReL = 2.2 × 107. Comparisons of the wake of the cruciform appendage were made with experiments to verify the computational accuracy and to examine the influence of the spatial resolution. A satisfactory result was more representative of the experiments with the improvement in grid spatial resolution. The evolution characteristics of three co-rotating vortices originating from the cruciform appendage under the most refined grid arrangement are further described in detail under straight-ahead and 10° yaw conditions. The comparison results show that, in the core-flow region, the resultant velocity, vorticity magnitude, and TKE were stronger and the wake was more complicated under 10° yaw conditions. Tip vortex tracking under 10° yaw conditions exhibited significant three-dimensional characteristics as the wake developed downstream.

Machine and Deep Learning Regression of Chlorophyll-a Concentrations in Lakes Using PRISMA Satellite Hyperspectral Imagery

The estimation of Chlorophyll-a concentration is crucial for monitoring freshwater ecosystem health, particularly in lakes, as it is closely linked to eutrophication processes. Satellite imagery enables synoptic and frequent evaluations of Chlorophyll-a in water bodies, providing essential insights into spatiotemporal eutrophication dynamics. Frontier applications in water remote sensing support the utilization of machine and deep learning models applied to hyperspectral satellite imagery. This paper presents a comparative analysis of conventional machine and deep learning models—namely, Random Forest Regressor, Support Vector Regressor, Long Short-Term Memory, and Gated Recurrent Unit networks—for estimating Chlorophyll-a concentrations. The analysis is based on data from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral mission, complemented by low-resolution Chlorophyll-a concentration maps. The analysis focuses on three sub-alpine lakes, spanning Northern Italy and Switzerland as testing areas. Through a series of modelling experiments, best-performing model configurations are pinpointed for both Chlorophyll-a concentration estimations and the improvement of spatial resolution in predictions. Support Vector Regressor demonstrated a superior performance in Chlorophyll-a concentration estimations, while Random Forest Regressor emerged as the most effective solution for refining the spatial resolution of predictions.

Spatial Proteomics toward Subcellular Resolution by Coupling Deep Ultraviolet Laser Ablation with Nanodroplet Sample Preparation.

Multiplexed molecular profiling of tissue microenvironments, or spatial omics, can provide critical insights into cellular functions and disease pathology. The coupling of laser microdissection with mass spectrometry-based proteomics has enabled deep and unbiased mapping of >1000 proteins. However, the throughput of laser microdissection is often limited due to tedious two-step procedures, sequential laser cutting, and sample collection. The two-step procedure also hinders the further improvement of spatial resolution to <10 μm as needed for subcellular proteomics. Herein, we developed a high-throughput and high-resolution spatial proteomics platform by seamlessly coupling deep ultraviolet (DUV) laser ablation (LA) with nanoPOTS (Nanodroplet Processing in One pot for Trace Samples)-based sample preparation. We demonstrated the DUV-LA system can quickly isolate and collect tissue samples at a throughput of ∼30 spots/min and a spatial resolution down to 2 μm from a 10 μm thick human pancreas tissue section. To improve sample recovery, we developed a proximity aerosol collection approach by placing DMSO droplets close to LA spots. We demonstrated the DUV-LA-nanoPOTS platform can detect an average of 1312, 1533, and 1966 proteins from ablation spots with diameters of 7, 13, and 19 μm, respectively. In a proof-of-concept study, we isolated and profiled two distinct subcellular regions of the pancreas tissue revealed by hematoxylin and eosin (H&E) staining. Quantitative proteomics revealed proteins specifically enriched to subcellular compartments.

Improvement of LED-based photoacoustic imaging using lag-coherence factor (LCF) beamforming.

Owing to its portability, affordability, and energy-efficiency, LED-based photoacoustic (PA) imaging is increasingly becoming popular when compared to its laser-based alternative, mainly for superficial vascular imaging applications. However, this technique suffers from low SNR and thereby limited imaging depth. As a result, visual image quality of LED-based PA imaging is not optimal, especially in sub-surface vascular imaging applications. Combination of linear ultrasound (US) probes and LED arrays are the most common implementation in LED-based PA imaging, which is currently being explored for different clinical imaging applications. Traditional delay-and-sum (DAS) is the most common beamforming algorithm in linear array-based PA detection. Side-lobes and reconstruction-related artifacts make the DAS performance unsatisfactory and poor for a clinical-implementation. In this work, we explored a new weighting-based image processing technique for LED-based PAs to yield improved image quality when compared to the traditionalmethods. We are proposing a lag-coherence factor (LCF), which is fundamentally based on the combination of the spatial auto-correlation of the detected PA signals. In LCF, the numerator contains lag-delay-multiply-and-sum (DMAS) beamformer instead of a conventional DAS beamformer. A spatial auto-correlation operation is performed between the detected US array signals before using DMAS beamformer. We evaluated the new method on both tissue-mimicking phantom (2D) and human volunteer imaging (3D) data acquired using a commercial LED-based PA imagingsystem. Our novel correlation-based weighting technique showed LED-based PA image quality improvement when it is combined with conventional DAS beamformer. Both phantom and human volunteer imaging results gave a direct confirmation that by introducing LCF, image quality was improved and this method could reduce side-lobes and artifacts when compared to the DAS and coherence-factor (CF) approaches. Signal-to-noise ratio, generalized contrast-to-noise ratio, contrast ratio and spatial resolution were evaluated and compared with conventional beamformers to assess the reconstruction performance in a quantitative way. Results show that our approach offered image quality enhancement with an average signal-to-noise ratio and spatial resolution improvement of around 20% and 25% respectively, when compared with conventional CF based DASalgorithm. Our results demonstrate that the proposed LCF based algorithm performs better than the conventional DAS and CF algorithms by improving signal-to-noise ratio and spatial resolution. Therefore, our new weighting technique could be a promising tool to improve the performance of LED-based PA imaging and thus accelerate its clinicaltranslation.

Fluorescence radial fluctuation enables two-photon super-resolution microscopy.

Despite recent improvements in microscopy, it is still difficult to apply super-resolution microscopy for deep imaging due to the deterioration of light convergence properties in thick specimens. As a strategy to avoid such optical limitations for deep super-resolution imaging, we focused on super-resolution radial fluctuation (SRRF), a super-resolution technique based on image analysis. In this study, we applied SRRF to two-photon microscopy (2P-SRRF) and characterized its spatial resolution, suitability for deep observation, and morphological reproducibility in real brain tissue. By the comparison with structured illumination microscopy (SIM), it was confirmed that 2P-SRRF exhibited two-point resolution and morphological reproducibility comparable to that of SIM. The improvement in spatial resolution was also demonstrated at depths of more than several hundred micrometers in a brain-mimetic environment. After optimizing SRRF processing parameters, we successfully demonstrated in vivo high-resolution imaging of the fifth layer of the cerebral cortex using 2P-SRRF. This is the first report on the application of SRRF to in vivo two-photon imaging. This method can be easily applied to existing two-photon microscopes and can expand the visualization range of super-resolution imaging studies.