A three-wavelength multi-channel brain functional imager based on digital lock-in photon-counting technique

Digital lock-in detection provides spectroscopic and imaging instruments a means of measuring physical quantities with improved signal to noise ratios compared to analogue detection schemes. We introduce a digital lock-in detection algorithm for measuring the amplitude and phase of multiple amplitude modulated signals simultaneously by using particular modulation and sampling constraints and averaging filters. The technique exhibits exceptional reduction in both noise and inter-source distortion. It is shown that the digital lock-in technique can be performed as a simple matrix multiplication in order to reduce computation time. The digital lock-in algorithm is described and analyzed under certain sampling and modulation conditions. Results are shown for experimental data.

The fast data acquisition system (FAS) has been developed for the motional Stark effect (MSE) diagnostic in many machines. The FAS digitizes the signals coming directly out of the photomultiplier tube. The software solution of digital lock-in technique to derive the MSE signals from the data of FAS is presented in this article. The calculated frequency and phase of the photoelastic modulators (PEMs) with this digital lock-in technique can reach so high precision that the first, second, and fourth harmonic frequency signals of PEMs can be perfectly recovered.

Cerebrospinal fluid (CSF) circulation is tightly coupled to cerebral blood flow under the fixed-volume constraint of the cranial vault, with cardiac pulsations and respiration acting as dominant physiological drivers. Disruption of these flow dynamics has been implicated in various neurological disorders, motivating the need for imaging methods that capture CSF and vascular flow simultaneously and in real time. Conventional phase contrast MRI (PC-MRI) provides quantitative CSF velocity measurements but relies on cardiac gating and velocity encoding, which limit temporal resolution, dynamic range, and sensitivity to non-cardiac fluctuations. Here, we introduce self-gated ultrafast real-time flow-weighted echo-planar imaging (SURF-EPI), an inflow-weighted approach in which signal intensity reflects the replacement of saturated spins by freshly inflowing, unsaturated water molecules, yielding higher signal with higher flow. This allows simultaneous assessment of arterial inflow, venous outflow, and CSF motion at high temporal resolution. SURF-EPI was acquired at the C2–C3 spinal level, enabling real-time imaging of CSF flow dynamics in the cervical spinal canal alongside arterial and venous blood flow in major cervical vessels (frame-rate: 21.7 Hz). In addition to frequency-domain analyses, we leveraged intrinsic arterial signal fluctuations as a timing reference to reconstruct cardiac-resolved CSF dynamics for individual cardiac cycles without external physiological recordings. Frequency-domain analysis revealed distinct spectral signatures in CSF flow compared with neurovascular flow, including broadened cardiac peaks and enhanced respiratory modulation, particularly within the ventral spinal canal. In contrast, dorsal CSF showed increased power within the cardiac frequency band, higher coherence with cervical vasculature at the cardiac frequency, and reduced non-cardiac contributions. Time-domain analysis showed strong correlation between CSF flow waveforms derived from SURF-EPI and PC-MRI. Beyond ensemble-averaged waveforms, SURF-EPI enabled beat-to-beat analysis, revealing substantial cycle-to-cycle variability in CSF flow that is not captured by time-averaged gated approaches. Together, these findings establish SURF-EPI as a rapid, complementary framework to PC-MRI, enabling real-time neurofluid imaging with integrated time- and frequency-domain characterization of CSF and neurovascular flow dynamics.

Due to the limitations of hardware technology, satellite sensors cannot obtain images with high temporal, spatial, and spectral resolutions at the same time. Current spatiotemporal fusion methods try to solve the contradiction between temporal resolution and spatial resolution, which cannot achieve good reconstruction accuracy partly because the data sources are from heterogeneous platforms with long chains difficult to be modeled. Different from the crossing-platform fusion, this work proposes to improve the spatial and temporal resolutions on a single platform. For the 2-m panchromatic images, 8-m multispectral images, and 16-m wide-field-view images captured by the Gaofen-1 satellite, our goal is to produce 2-m multispectral images with high temporal resolutions. Two convolutional neural networks are built to solve this spatiotemporal-spectral fusion issue with pansharpening and spatiotemporal fusion in serial. In the validation stage, the 2-m multispectral images are built and evaluated with the panchromatic images and 8-m multispectral images. The digital and visual evaluations show that our method can produce visually acceptable fusion quality, which may enhance the feasibility of the Gaofen-1 data.

Runtime and memory requirements for typical formulations of energy system models increase non-linearly with resolution, computationally constraining large-scale models despite state-of-the-art solvers and hardware. This scaling paradigm requires omission of detail which can affect key outputs to an unknown degree. Recent algorithmic innovations employing decomposition have enabled linear increases in runtime and memory use as temporal resolution increases. Newly tractable, higher resolution systems can be compared with lower resolution configurations commonly employed today in academic research and industry practice, providing a better understanding of the potential biases or inaccuracies introduced by these abstractions. We employ a state-of-the art electricity system planning model and new high-resolution systems to quantify the impact of varying degrees of spatial, temporal, and operational resolution on results salient to policymakers and planners. We find models with high spatial and temporal resolution result in more realistic siting decisions and improved emissions, reliability, and price outcomes. Errors are generally larger in systems with low spatial resolution, which omit key transmission constraints. We demonstrate that high temporal resolution cannot overcome biases introduced by low spatial resolution, and vice versa. While we see asymptotic improvements to total system cost and reliability with increased resolution, other salient outcomes such as siting accuracy and emissions exhibit continued improvement across the range of model resolutions considered. We conclude that modelers should carefully balance resolution on spatial, temporal, and operational dimensions and that novel computational methods enabling higher resolution modeling are valuable and can further improve the decision support provided by this class of models.

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to “zoom out” by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.

While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial–temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R 2 of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail.

Diffuse optical tomography (DOT) is an advanced imaging method used to visualize the internal state of biological tissues as 3D images. However, current continuous-wave DOT requires high-density probe arrays for measurement (less than 15-mm interval) to gather enough information for 3D image reconstruction, which makes the experiment time-consuming. In this paper, we propose a novel DOT measurement system using multi-directional light sources and multi-directional photodetectors instead of high-density probe arrays. We evaluated this system's multi-directional DOT through computer simulation and a phantom experiment. From the results, we achieved DOT with less than 5-mm localization error up to a 15-mm depth with low-density probe arrays (30-mm interval), indicating that the multi-directional measurement approach allows DOT without requiring high-density measurement.

Real-time imaging of optic nerve head collagen microstructure and biomechanics using instant polarized light microscopy

Precise tracking of the rapid and complex 3D movement of eukaryotic flagella is important for understanding their roles in cellular motility, sensory functions, and resource acquisition. Yet, achieving accurate 3D kinematic reconstruction of flagellar beating patterns, particularly highly non-planar ones, remains challenging. Here we present holoV3C, a method based on Digital Holographic Microscopy (DHM) that allows precise, label-free 3D tracking of highly non-planar eukaryotic flagella with high temporal resolution. This algorithm leverages phase anomaly detection to provide a combination of high temporal and axial resolution, with 0.25µm for beating mouse sperm flagella and down to 53nm for polystyrene particles, across large sampling volumes in a computationally efficient manner. Algorithmic validation is performed by tracking mouse sperm flagella over time, capturing approximately 600 points along a single flagellum to achieve high axial resolution. Furthermore, we apply holoV3C to reconstruct the highly non-planar beating dynamics of the 200-nm-diameter flagellum of the protist Reclinomonas americana with a temporal resolution of 200 frames per second. By enabling 3D tracking of non-planar eukaryotic flagella, holoV3C can yield important insights to advance our understanding of flagellar dynamics, opening new avenues in the study of microorganism motility and its ecological roles.

The framed-based camera is commonly used for object tracking tasks. However, under poor light conditions, the frame camera cannot work properly and provide stable detection for the tracking system. Also, it has motion blur when the object is moving fast. Compared to the frame-based camera, the event camera has a higher temporal resolution and dynamic range. The event data is transmitted immediately when the brightness of each pixel changes. The higher temporal resolution helps the event camera avoid the motion blur and provide more precise temporal information. Moreover, the higher dynamic range allows the event camera to work in extreme light conditions, such as evening, raining, and strong sunshine environments. In all, those advantages make the event cameras an applicable complementary sensor to the frame cameras. In this paper, several optical flow algorithms for the event data are tested. Then, a clustering algorithm is proposed to generate the detection and hybrid it with the detection from frame images. At last, the PMBM filter is used to realize object tracking and test it on our self-recorded dataset with the DAVIS346 and several publicly available datasets.KeywordsSensor fusionEvent-based cameraOptical flowObject tracking

For noninvasive and quantitative measurements of global two-dimensional (2D) heart wall motion, speckle tracking methods have been developed and applied. In these conventional methods, the frame rate is limited to about 200 Hz, corresponding to the sampling period of 5 ms. However, myocardial function during short periods, as obtained by these conventional speckle tracking methods, remains unclear owing to low temporal and spatial resolutions of these methods. Moreover, an important parameter, the optimal kernel size, has not been thoroughly investigated. In our previous study, the optimal kernel size was determined in a phantom experiment under a high signal-to-noise ratio (SNR), and the determined optimal kernel size was applied to the in vivo measurement of 2D displacements of the heart wall by block matching using normalized cross-correlation between RF echoes at a high frame rate of 860 Hz, corresponding to a temporal resolution of 1.1 ms. However, estimations under low SNRs and the effects of the difference in echo characteristics, i.e., specular reflection and speckle-like echoes, have not been considered, and the evaluation of accuracy in the estimation of the strain rate is still insufficient. In this study, the optimal kernel sizes were determined in a phantom experiment under several SNRs and, then, the myocardial strain rate was estimated such that the myocardial function can be measured at a high frame rate. In a basic experiment, the optimal kernel sizes at depths of 20, 40, 60, and 80 mm yielded similar results: in particular, SNR was more than 15 dB. Moreover, it was found that the kernel size at the boundary must be set larger than that at the inside. The optimal sizes of the correlation kernel were seven times and four times the size of the point spread function around the boundary and inside the silicone rubber, respectively. To compare the optimal kernel sizes, which was determined in a phantom experiment, with other sizes, the radial strain rates estimated using different kernel sizes were examined using the normalized mean-squared error of the estimated strain rate from the actual one obtained by the 1D phase-sensitive method. Compared with conventional kernel sizes, this result shows the possibility of the proposed correlation kernel to enable more accurate measurement of the strain rate. In in vivo measurement, the regional instantaneous velocities and strain rates in the radial direction of the heart wall were analyzed in detail at an extremely high temporal resolution (frame rate of 860 Hz). In this study, transition in contraction and relaxation was able to be detected by 2D tracking. These results indicate the potential of this method in the high-accuracy estimation of the strain rates and detailed analyses of the physiological function of the myocardium.

Rainfall erosivity is a critical parameter for soil degradation assessment. It is especially important in areas prone to soil erosion due to frequent or intense rainfall events. Rainfall erosivity is typically quantified through the EI30 index, which combines the rainfall's kinetic energy (E) with its maximum 30-minute intensity (I30). Rainfall data with high temporal resolution (e.g., 5-minute) are often limited in availability, particularly across large spatial scales. Gridded rainfall products usually provide half-hourly or hourly data and are frequently used despite the potential biases they introduce into erosivity calculations. In Austria, the introduction of INCA (Integrated Nowcasting through Comprehensive Analysis) has provided an opportunity to enhance rainfall erosivity studies. INCA offers rainfall data at a 15-minute temporal and 1-kilometer spatial resolution. In our study, INCA’s accuracy and limitations in capturing erosive rainfall events, especially extreme events, are carefully evaluated against high-resolution in-situ rainfall station data. Understanding the degree of underestimation or overestimation in EI30 calculations is crucial for applying INCA data in erosion simulations and according to soil conservation strategies. This study focuses on the Main Agricultural Production Zones (MAPZ) in Austria. The study evaluates the performance of INCA in calculating erosivity compared to a dense network of 5-minute resolution rainfall stations. It also investigates the occurrence probability of extreme erosive events using a probabilistic approach, providing insights into long-term erosion risks and regional differences in erosivity patterns. Eventually, the study examines the impact of temporal resolution on erosivity estimates, assessing biases introduced by coarser resolutions (15- and 30-minute) for both mean and extreme rainfall events. Results indicate that INCA overestimates 8.1% of the total event number but underestimates 3.1% of the total rainfall erosivity, with the largest under- and overestimation in east of Austria. On the other hand, the mean annual maximum EI30 was underestimated by 13.6%, and the south showed the most considerable underestimation. It was found that INCA can detect highly erosive events occurring in the in-situ datasets. The underestimation of EI30 sources from the temporally smoothened peak I30 rather than the E of the rainfall events. Long-term extreme EI30 were analyzed using the Generalized Extreme Value (GEV) distribution, revealing that EI30 values increase with longer return periods (e.g., 50 years) and that the southern region exhibits the largest EI30 values, indicating a greater risk of extreme erosive events. On the other hand,  INCA may emphasize more recent, potentially intense rainfall trends, leading to larger return levels. The impact assessment by coarser temporal resolutions on EI30 confirms that the underestimation substantially increases with lower temporal resolution, primarily due to I30 rather than E. Eventually, a 15-minute temporal resolution dataset may lead to acceptable underestimations across our investigated MAPZ; underestimations ranged from 2.8–8.5% in event numbers and 1.0–10.0% in total rainfall erosivity at 15-minute resolution and from 10.2–33.7% and 5.2–26.6%, respectively, at a 30-minute resolution. The results of this study highlight the potential value of INCA data as a practical source for rainfall erosivity assessments, particularly in regions with limited high-resolution rainfall measurements.

The ability to produce high-quality images of human brain function in any environment and during unconstrained movement of the subject has long been a goal of neuroimaging research. Diffuse optical tomography, which uses the intensity of back-scattered near-infrared light from multiple source-detector pairs to image changes in haemoglobin concentrations in the brain, is uniquely placed to achieve this goal. Here, we describe a new generation of modular, fibre-less, high-density diffuse optical tomography technology that provides exceptional sensitivity, a large dynamic range, a field-of-view sufficient to cover approximately one-third of the adult scalp, and also incorporates dedicated motion sensing into each module. Using in-vivo measures, we demonstrate a noise-equivalent power of 318 fW, and an effective dynamic range of 142 dB. We describe the application of this system to a novel somatomotor neuroimaging paradigm that involves subjects walking and texting on a smartphone. Our results demonstrate that wearable high-density diffuse optical tomography permits three-dimensional imaging of the human brain function during overt movement of the subject; images of somatomotor cortical activation can be obtained while subjects move in a relatively unconstrained manner, and these images are in good agreement with those obtained while the subjects remain stationary. The scalable nature of the technology we described here paves the way for the routine acquisition of high-quality, three-dimensional, whole-cortex diffuse optical tomography images of cerebral haemodynamics, both inside and outside of the laboratory environment, which has profound implications for neuroscience.

The impacts of global change, including extreme heat and water scarcity, are threatening an ever-growing urban world population. Evapotranspiration (ET) mitigates the urban heat island, reducing the effect of heat waves. It can also be used as a proxy for vegetation water use, making it a crucial tool to plan resilient green cities. To optimize the trade-off between urban greening and water security, reliable and up-to-date maps of ET for cities are urgently needed. Despite its importance, few studies have mapped urban ET accurately for an entire city in high spatial and temporal resolution. We mapped the ET of Berlin, Germany in high spatial (10-m) and temporal (hourly) resolution for the year of 2019. A novel machine learning (ML) approach combining Sentinel-2 time series, open geodata, and flux footprint modeling was applied. Two eddy flux towers with contrasting surrounding land cover provided the training and testing data. Flux footprint modeling allowed us to incorporate comprehensive land cover types in training the ML models. Open remote sensing and geodata used as model inputs included Normalized Difference Vegetation Index (NDVI) from Sentinel-2, building height, impervious surface fraction, vegetation fraction, and vegetation height. NDVI was used to indicate vegetation phenology and health, as plant transpiration contributes to the majority of terrestrial ET. Hourly reference ET (RET) was calculated and used as input to capture the temporal dynamics of the meteorological conditions. Predictions were carried out using Random Forest (RF) regression. Weighted averages extracted from hourly ET maps using flux footprints were compared to measured ET from the two flux towers. Validation showed that the approach is reliable for mapping urban ET, with a mean R2 of 0.76 and 0.56 and a mean RMSE of 0.0289 mm and 0.0171 mm at the more vegetated site and the city-center site, respectively. Lastly, the variation of ET between Local Climate Zones (LCZs) was analyzed to support urban planning. This study demonstrated the capacity to map urban ET at an unprecedented high spatial and temporal resolution with a novel methodology, which can be used to support the sustainable management of green infrastructure and water resources in an urbanizing world facing climate change.