High-resolution Image Sequences Research Articles

Time series cube data construction and land use attribute change study based on high-resolution imagery

Abstract In order to better understand the changes in land use attributes, comprehend the drivers of land use changes, assess their environmental and economic impacts, and provide a scientific basis and data support for land use planning, environmental protection, and agricultural production, this study is based on Qiu County’s high-resolution image data from 2020-2022 and makes use of the latest research results of hyperspectral remote sensing technology to analyze its changes in land use attributes by establishing the time of the high-resolution image sequence cube data and solving the characteristic variables through band operations to quickly and accurately calculate a variety of remote sensing indicators and analyze the changes in their land use attributes. The results of the study show that vegetation in Qiu County will change significantly between 2020 and 2022, mainly due to seasonal and land-use changes; Water bodies will change less but more consistently; And buildings will change less, mainly focusing on villages and industrial land.

An integrated push-to-pull micromechanical device: Design, fabrication, and in-situ experiment

The rapid advancement of micro-nano machining technology has led to a decrease in the dimensions of microdevices and microchips, following the principles of Moore’s law. In addition to conventional semiconductor materials like silicon, emerging nanoscale materials such as nanowires, nanotubes, and two-dimensional materials are being considered as promising alternative constituent materials. The mechanical properties of these materials have a significant impact on the performance and service life of these microdevices and microchips. However, conventional mechanical testing methods have difficulty in accurately measuring the properties of these materials at the nanoscale due to limitations in displacement control and microforce sensing. Consequently, there is an urgent need to develop a micromechanical device capable of testing nanoscale solid materials. In this study, we propose a concept based on high-resolution image sequences for the design of an integrated micromechanical device capable of synchronously measuring the force and deformation of tested specimens. The device has been fabricated using ultrafast femtosecond laser etching technology, which offers an efficient and cost-effective approach for manufacturing microstructures and is suitable for processing various materials such as metals and nonmetals. The stiffness of the device plays a crucial role in the design of the micromechanical device, and a stiffness-matching criterion is introduced to ensure appropriate design parameters. The fabricated device is employed to conduct in-situ tension experiments on SiC nanowires and multilayer molybdenum disulfide nanosheet within a scanning electronic microscope, enabling accurate measurement of their strength, modulus, and fracture strain.

The many ages of Triton: New crater counts on the Voyager high-resolution image sequence and implications for impactor provenance

Triton is sparsely cratered overall, but because of the high concentration of its impact craters on the leading hemisphere of motion, a dominantly prograde planetocentric origin for the impactors has been hypothesized. With only a modest heliocentric contribution, the average age of Triton's datable terrains could be no more than ∼10 Ma, which would make Triton the most globally active icy satellite of all. This is obviously important, but is it true? There are clear stratigraphic age differences between terrains on Triton's leading hemisphere, especially on the 10-frame high-resolution Voyager 2 mosaic, which complicates simple or facile interpretations of the crater spatial distribution. Moreover, crater counts on the high-resolution mosaic, which we present here, indicate a steep slope for the impactor size-frequency distribution (SFD) at small (sub-km) sizes, completely at odds with crater counts on Charon, nominally the best estimate of the time-integrated Kuiper belt flux (Charon's corresponding SFD slope being quite shallow in comparison). Most likely, this implies that the smaller craters we measure on Triton are indeed predominantly planetocentric, but the origin of such a geologically recent prograde impactor swarm is puzzling. Here we consider several possible sources or explanations in detail, and find debris from a collision or collisions between neptunian irregular satellites to be the least objectionable, albeit unproven. Secondaries or sesquinaries from a large but unknown crater elsewhere on Triton are a distinct alternative.

Learning to Super-resolve Dynamic Scenes for Neuromorphic Spike Camera

Spike camera is a kind of neuromorphic sensor that uses a novel ``integrate-and-fire'' mechanism to generate a continuous spike stream to record the dynamic light intensity at extremely high temporal resolution. However, as a trade-off for high temporal resolution, its spatial resolution is limited, resulting in inferior reconstruction details. To address this issue, this paper develops a network (SpikeSR-Net) to super-resolve a high-resolution image sequence from the low-resolution binary spike streams. SpikeSR-Net is designed based on the observation model of spike camera and exploits both the merits of model-based and learning-based methods. To deal with the limited representation capacity of binary data, a pixel-adaptive spike encoder is proposed to convert spikes to latent representation to infer clues on intensity and motion. Then, a motion-aligned super resolver is employed to exploit long-term correlation, so that the dense sampling in temporal domain can be exploited to enhance the spatial resolution without introducing motion blur. Experimental results show that SpikeSR-Net is promising in super-resolving higher-quality images for spike camera.

Quantitative estimation of organ-scale phenotypic parameters of field crops through 3D modeling using extremely low altitude UAV images

Plant phenotypic parameters provide key information in modern crop breeding. However, the rapid and accurate estimation of organ-scale phenotypic parameters remains a challenge. In this context, the present study proposed a novel methodology for the automatic quantification of the organ-scale parameters of field crops using the unmanned aerial vehicle (UAV) platform. First, a lightweight UAV was employed to capture the multi-view and high-resolution image sequences of field crops at an extremely-low flight altitude. Subsequently, based on these image sequences, point cloud reconstruction of the canopy was conducted. Next, the geometrical model of individual leaves was reconstructed using the modified Crust algorithm and optimized by abnormal facet elimination and leaf surface repair. Finally, individual leaf phenotypic parameters were calculated based on the reconstructed geometrical models. The method was evaluated by comparing the calculated parameters with actual measurements. The calculated values for leaf length, maximum leaf width, and leaf area were in good agreements with the measured values (maize: R2 > 0.97 for all parameters, RMSE for length, width, and leaf area was 2.6 cm, 0.4 cm, and 33.2 cm2, respectively; soybean: R2 > 0.85 for all parameters, RMSE for the counterparts was 0.3 cm, 0.5 cm, and 2.3 cm2, respectively; tobacco: R2 > 0.89 for all parameters, RMSE for the counterparts was 4.1 cm, 1.4 cm, and 42.6 cm2, respectively). The methodology based on extremely-low altitude UAV images has promising prospects in the crop breeding program for the automatic acquisition of fine organ-scale parameters with high efficiency.

Dynamic Fourier ptychography with deep spatiotemporal priors

Fourier ptychography (FP) involves the acquisition of several low-resolution intensity images of a sample under varying illumination angles. They are then combined into a high-resolution complex-valued image by solving a phase-retrieval problem. The objective in dynamic FP is to obtain a sequence of high-resolution images of a moving sample. There, the application of standard frame-by-frame reconstruction methods limits the temporal resolution due to the large number of measurements that must be acquired for each frame. In this work instead, we propose a neural-network-based reconstruction framework for dynamic FP. Specifically, each reconstructed image in the sequence is the output of a shared deep convolutional network fed with an input vector that lies on a one-dimensional manifold that encodes time. We then optimize the parameters of the network to fit the acquired measurements. The architecture of the network and the constraints on the input vectors impose a spatiotemporal regularization on the sequence of images. This enables our method to achieve high temporal resolution without compromising the spatial resolution. The proposed framework does not require training data. It also recovers the pupil function of the microscope. Through numerical experiments, we show that our framework paves the way for high-quality ultrafast FP.

Histogram analysis of multi-model high-resolution diffusion-weighted MRI in breast cancer: correlations with molecular prognostic factors and subtypes.

To investigate the correlations between quantitative diffusion parameters and prognostic factors and molecular subtypes of breast cancer, based on a single fast high-resolution diffusion-weighted imaging (DWI) sequence with mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) models. A total of 143 patients with histopathologically verified breast cancer were included in this retrospective study. The multi-model DWI-derived parameters were quantitatively measured, including Mono-ADC, IVIM-D, IVIM-D*, IVIM-f, DKI-Dapp, and DKI-Kapp. In addition, the morphologic characteristics of the lesions (shape, margin, and internal signal characteristics) were visually assessed on DWI images. Next, Kolmogorov-Smirnov test, Mann-Whitney U test, Spearman's rank correlation, logistic regression, receiver operating characteristic (ROC) curve, and Chi-squared test were utilized for statistical evaluations. The histogram metrics of Mono-ADC, IVIM-D, DKI-Dapp, and DKI-Kapp were significantly different between estrogen receptor (ER)-positive vs. ER-negative groups, progesterone receptor (PR)-positive vs. PR-negative groups, Luminal vs. non-Luminal subtypes, and human epidermal receptor factor-2 (HER2)-positive vs. non-HER2-positive subtypes. The histogram metrics of Mono-ADC, DKI-Dapp, and DKI-Kapp were also significantly different between triple-negative (TN) vs. non-TN subtypes. The ROC analysis revealed that the area under the curve considerably improved when the three diffusion models were combined compared with every single model, except for distinguishing lymph node metastasis (LNM) status. For the morphologic characteristics of the tumor, the margin showed substantial differences between ER-positive and ER-negative groups. Quantitative multi-model analysis of DWI showed improved diagnostic performance for determining the prognostic factors and molecular subtypes of breast lesions. The morphologic characteristics obtained from high-resolution DWI can be identifying ER statuses of breast cancer.

Spectroscopic MRI-Guided Proton Therapy in Non-Enhancing Pediatric High-Grade Glioma.

Radiation therapy (RT) is a critical part of definitive therapy for pediatric high-grade glioma (pHGG). RT is designed to treat residual tumor defined on conventional MRI (cMRI), though pHGG lesions may be ill-characterized on standard imaging. Spectroscopic MRI (sMRI) measures endogenous metabolite concentrations in the brain, and Choline (Cho)/N-acetylaspartate (NAA) ratio is a highly sensitive biomarker for metabolically active tumor. We provide a preliminary report of our study introducing a novel treatment approach of whole brain sMRI-guided proton therapy for pHGG. An observational cohort (c1 = 10 patients) receives standard of care RT; a therapeutic cohort (c2 = 15 patients) receives sMRI-guided proton RT. All patients undergo cMRI and sMRI, a high-resolution 3D whole-brain echo-planar spectroscopic imaging (EPSI) sequence (interpolated resolution of 12 µL) prior to RT and at several follow-up timepoints integrated into diagnostic scans. Treatment volumes are defined by cMRI for c1 and by cMRI and Cho/NAA ≥ 2x for c2. A longitudinal imaging database is used to quantify changes in lesion and metabolite volumes. Four subjects have been enrolled (c1 = 1/c2 = 3) with sMRI imaging follow-up of 4-18 months. Preliminary data suggest sMRI improves identification of pHGG infiltration based on abnormal metabolic activity, and using proton therapy to target sMRI-defined high-risk regions is safe and feasible.

Online fusion of multi-resolution multispectral images with weakly supervised temporal dynamics

Real-time satellite imaging has a central role in monitoring, detecting and estimating the intensity of key natural phenomena such as floods, earthquakes, etc. One important constraint of satellite imaging is the trade-off between spatial/spectral resolution and their revisiting time, a consequence of design and physical constraints imposed by satellite orbit among other technical limitations. In this paper, we focus on fusing multi-temporal, multi-spectral images where data acquired from different instruments with different spatial resolutions is used. We leverage the spatial relationship between images at multiple modalities to generate high-resolution image sequences at higher revisiting rates. To achieve this goal, we formulate the fusion method as a recursive state estimation problem and study its performance in filtering and smoothing contexts. Furthermore, a calibration strategy is proposed to estimate the time-varying temporal dynamics of the image sequence using only a small amount of historical image data. Differently from the training process in traditional machine learning algorithms, which usually require large datasets and computation times, the parameters of the temporal dynamical model are calibrated based on an analytical expression that uses only two of the images in the historical dataset. A distributed version of the Bayesian filtering and smoothing strategies is also proposed to reduce its computational complexity. To evaluate the proposed methodology we consider a water mapping task where real data acquired by the Landsat and MODIS instruments are fused generating high spatial–temporal resolution image estimates. Our experiments show that the proposed methodology outperforms the competing methods in both estimation accuracy and water mapping tasks.

Prostatic urinary tract visualization with super-resolution deep learning models.

In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been developed to visualize the prostatic urinary tract using post-urination magnetic resonance imaging (PU-MRI) without a urethral catheter. This study investigated whether the combination of PU-MRI and super-resolution (SR) deep learning models improves the visibility of the prostatic urinary tract. We enrolled 30 patients who had previously undergone real-time-image-gated spot scanning proton therapy by insertion of fiducial markers. PU-MRI was performed using a non-contrast high-resolution two-dimensional T2-weighted turbo spin-echo imaging sequence. Four different SR deep learning models were used: the enhanced deep SR network (EDSR), widely activated SR network (WDSR), SR generative adversarial network (SRGAN), and residual dense network (RDN). The complex wavelet structural similarity index measure (CW-SSIM) was used to quantitatively assess the performance of the proposed SR images compared to PU-MRI. Two radiation oncologists used a 1-to-5 scale to subjectively evaluate the visibility of the prostatic urinary tract. Cohen's weighted kappa (k) was used as a measure of agreement of inter-operator reliability. The mean CW-SSIM in EDSR, WDSR, SRGAN, and RDN was 99.86%, 99.89%, 99.30%, and 99.67%, respectively. The mean prostatic urinary tract visibility scores of the radiation oncologists were 3.70 and 3.53 for PU-MRI (k = 0.93), 3.67 and 2.70 for EDSR (k = 0.89), 3.70 and 2.73 for WDSR (k = 0.88), 3.67 and 2.73 for SRGAN (k = 0.88), and 4.37 and 3.73 for RDN (k = 0.93), respectively. The results suggest that SR images using RDN are similar to the original images, and the SR deep learning models subjectively improve the visibility of the prostatic urinary tract.

HR-PrGAN: High-resolution story visualization with progressive generative adversarial networks

Generating a series of images to describe a story of multiple sentences is a challenging task in computer vision as it needs to consider both the image-level precision and story-level consistency. Existing methods usually focus on the consistency between images at the cost of the fine-grained object details. We propose a progressive adversarial learning algorithm, termed HR-PrGAN, to achieve high-resolution image sequences with rich details by decomposing the problem of generating into multiple stages. Specifically, HR-PrGAN has two stages, where the Coarse-grain Stage generates a series of coherent coarse-grained images from both the story and context embeddings, and an additional unconditional loss is proposed to restrict their deformation and preserve the object contours and layouts. Subsequently, the Refinement Stage further refines the series of coherent coarse-grained images by injecting the story-level text embeddings and preserving the image-level details via a Coarse-grained feature Supplementary Module (CSM). Moreover, two commonly-used datasets, i.e. the CLEVR-SV and PororoSV datasets, are applied to evaluate the proposed method. Extensive experiments demonstrate that the proposed model significantly outperforms state-of-the-art methods in terms of image anti-deformation, fine-grained feature synthesis and human perception based image quality evaluation.

Identification of forest vegetation types in southern China based on spatio-temporal fusion of GF-1 WFV and MODIS data

It is difficult to obtain long time series of high spatial resolution remote sensing images in southern China because of the complex terrain and frequent cloudy and rainy weather. In contrast, the spatio-temporal fusion can sychonorously obtain remote sensing data with high spatial-temporal resolution, which is beneficial to extract forest vegetation type information. With Xingguo County of Jiangxi Province as the study area, we fused the Landsat8 OLI and GF-1 WFV images with high spatial resolution with high temporal resolution of MODIS09 A1 image on the basis of enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), reconstructed the time series data of ESTARFM_Landsat8 EVI and ESTARFM_GF-1 EVI with 8 d step of enhanced vegetation index (EVI), obtained the phenology (PH) characteristics, and identified the forest vegetation types by using random forest classification model. The results showed that the correlation coefficients between the fusion data of ESTARFM_Landsat8 EVI and ESTARFM_GF-1 EVI and the real images were all greater than 0.7, and had good consistency in spatial distribution, which could be used to supplement the missing data with high spatial resolution. The extraction accuracy of random forest classification with different combination modes was EVI+PH>EVI>PH and the classification accuracy of fusion data GF-1 was higher than that of Landsat8. A total of 43 variables were selected as the optimal feature variables for classification. The overall accuracy and Kappa coefficient were 95.6% and 94.9%, respectively, including 37 sequential EVI values and 6 phenological feature information. The sequential EVI data contributed more to the identification of forest vegetation types, while the phenological feature information was beneficial to improve the classification accuracy. The ESTARFM fusion algorithm was suitable for GF-1 and MODIS data, which could solve the problem of insufficient long-term sequence of high spatial resolution images. The GF-1 temporal fusion images had high accuracy in the identification of forest vegetation types in southern China under complex terrain and frequent cloudy and rainy weather.

Video-Based Eye Blink Identification and Classification.

Blink detection and classification can provide a very useful clinical indicator, because of its relation with many neurological and ophthalmological conditions. In this work, we propose a system that automatically detects and classifies blinks as "complete" or "incomplete" in high resolution image sequences zoomed into the participants' face, acquired during clinical examination using near-Infrared illumination. This method utilizes state-of-the-art (DeepLabv3+) deep learning encoder-decoder neural architecture -DLED to segment iris and eyelid in both eyes in the acquired images. The sequence of the segmented frames is post-processed to calculate the distance between the eyelids of each eye (palpebral fissure height) and the corresponding iris diameter. These quantities are temporally filtered and their fraction is subject to adaptive thresholding to identify blinks and determine their type, independently for each eye. The proposed system was tested on 15 participants, each with one video of 4 to 10 minutes. Several metrics of blink detection and classification accuracy were calculated against the ground truth, which was generated by three (3) independent experts, whose conflicts were resolved by a senior expert. Results show that the proposed system achieved F1-score 95.3% and 80.9% for the classification of complete and incomplete blinks respectively, collectively for all 15 participants, outperforming all 3 experts. The proposed system was proven robust in handling unexpected participant movements and actions, as well as glare and reflections from the spectacles, or face obstruction by facemasks.

In situ real time observation of tribological behaviour of coatings

The functional performance of coatings can be transformed using coatings to form an engineered surface system where resistance to wear and friction are improved relative to the uncoated substrate. Understanding the failure mechanisms of coatings in tribological contacts is difficult to achieve. This paper shows results from real time in situ measurements of tribological behaviour of coated surfaces.Two different test systems have been used. These are a microtribometer fitted inside an SEM which provides sequences of high resolution images of the deformation processes occurring in coatings, and a ball on disc tribometer fitted with imaging and profilometric systems giving real-time information on the tribological response of coatings.DLC and TiN coatings on tool steel substrates were tested in the experiments reported here. Delamination failure of the DLC coatings was observed with both test system. A new rotating scratch test is also described that can provide real-time in situ information on the response of the coatings to repeated abrasion. The in situ results are supported by post-test surface examination.

An integrated high-resolution image log, sequence stratigraphy and palynofacies analysis to reconstruct the Albian – Cenomanian basin depositional setting and cyclicity: Insights from the southern Tethys

Borehole image logs are used to overcome the lack of core samples and other conventional open-hole logs in many subsurface reservoirs around the world, significantly reducing the likelihood of petroleum resource exploration. These image logs can provide high-resolution data to visualize sedimentary structures, facies types, lithofacies, geomechanical characteristics, and depositional trends. Sedimentologists recognize the Bahariya Formation of the North-Western Desert of Egypt as the typical coastal -shallow marine depositional model that emphasizes a typical heterogeneous sandstone reservoir with many obstacles in production profile. One of the most significant issues related to the heterogeneous Bahariya reservoir that confronts hydrocarbon producers is the loss of productive target zones due to spatial facies changes. This research aims to overcome the exploration and development challenges of the complex and heterogeneous sandstone reservoir of the Albian – Cenomanian Bahariya Formation that lies in the Abu Gharadig Basin, Southern Tyths, Egypt. In this work, we used integrated image logging in conjunction with palynofacies, core samples, and sequence stratigraphy to provide high resolution information that can aid in better resource exploitation. Furthermore, we utilized the integrated work in the definition of the high-resolution para-sequences, sequences boundaries, lithofacies characteristics and paleocurrent analysis of each facies association. The interpretation of coastal shallow marine facies from four BHI-logs was established and calibrated with two core sets in order to define the best hydrocarbon reservoir intervals and focus on improving the development plan of the study area. Our findings show that the Bahariya Formation is characterized by eleven facies associations which have been interpreted based on the integrating of the twenty-seven electrofacies extracted from the FMI images, wireline logs, and core photographs. The identified facies associations were classified into five depositional sequences and their boundaries were determined using dipmeter patterns and extracted lithofacies from BHI. The LST and MFS deposits in both the fourth and fifth depositional cycles, respectively, whereas the HST deposits are found only in the third depositional cycle. According to our findings, the development of the shoreface sandy facies of cycles three and five represents the maximum rate of sea-level rise, as well as the development of the offshore sand bar system, which represents high-quality sandstone reservoirs. This case study demonstrates how image logs can be used to better understand the spatial distribution and geometries of sandstone bodies in the Bahariya Formation. Furthermore, it provides a reference imaging fingerprint of high-resolution coastal shallow marine, high-frequency sequences analysis, sedimentary structures, and extracted electrofacies analogues that can be used in the basin and elsewhere.

Neuroimaging cerebrovascular biomarkers in Parkinson's disease.

The cardiovascular risk in Parkinson's disease (PD) remains uncertain and controversial. Some studies suggest PD patients present an increased risk of cerebrovascular disease. We aimed to study the prevalence of neuroimaging cerebrovascular biomarkers in PD patients compared to controls, using an accurate and complete magnetic resonance (MR) imaging evaluation. Neuroimaging sub-study within a larger cross-sectional case-control study. An enriched subgroup of PD patients (≤10 years since diagnosis) with at least a moderate cardiovascular mortality risk based on a Systematic COronary Risk Evaluation (SCORE) was compared to community-based controls regarding neuroimaging biomarkers. Patients underwent a high-resolution T1-weighted MR imaging sequence at 3.0T to visualize neuromelanin. A 3D SWI FFE, sagittal 3D T1-weighted, axial FLAIR and diffusion-weighted image sequences were obtained. The study included 47 patients, 24 with PD and 23 controls. PD patients presented a reduced area and signal intensity of the substantia nigra and locus coeruleus on neuromelanin-sensitive MR. The median SCORE was 5% in both groups. No significant differences regarding white matter hyperintensities (OR 4.84, 95% CI 0.50, 47.06), lacunes (OR 0.43, 95% CI 0.07, 2.63), microbleeds (OR 0.64, 95% CI 0.13, 3.26), or infarcts (0.95, 95% CI 0.12, 7.41) was found. The frequency of these neuroimaging biomarkers was very low in both groups. The present study does not support an increased prevalence of neuroimaging cerebrovascular biomarkers in PD patients.

Possible Signature of Sausage Waves in Photospheric Bright Points

Sausage waves have been frequently reported in solar magnetic structures such as sunspots, pores, and coronal loops. However, they have not been unambiguously identified in photospheric bright points (BPs). Using high-resolution TiO image sequences obtained with the Goode Solar Telescope at the Big Bear Solar Observatory, we analyzed four isolated BPs. It was found that their area and average intensity oscillate for several cycles in an in-phase fashion. The oscillation periods range from 100 to 200 seconds. We interpreted the phase relation as a signature of sausage waves, particularly slow waves, after discussing sausage-wave theory and the opacity effect.

Volumetric Reconstruction and Web‐Based Visualization of Giga‐resolution Anatomical Datasets Using 3D Point Clouds: From Full‐color Image Sequences to Spatial Anatomy

Introduction The Visible Human Project (VHP) data sets are comprehensive visual archives that provide a reliable submillimeter registration of human anatomy. The evolution of computer graphics and the use of standardized pipelines have offered a general improvement in the three-dimensional (3D) visualization of data primarily obtained from radiological and histological sections. Due to the massive amount of high-resolution image sequences found in the data sets, there is currently a lack of complete and accessible 3D versions of the VHP. Recently, digital representations based on a set of 3D points following a given coordinate system, called “point clouds” (3D-PC), have become an essential tool for the study of topographical models in the geospatial field, with a wide variety of applications. This study describes a 3D-PC-based workflow developed by our team with the aim of online volumetric visualization, interaction, and democratization of the VHP data sets. Materials & Methods Three VHP data sets (i.e., VHP male, VHP female, and VHP 2.0) containing full-color cryo-sections were acquired from the online repository of the National Library of Medicine. The workflow for each one of the data sets was divided into four steps: 1) Batch-processing of image sequences: the image files were converted into TIFF files, and the pixels contained in the files were processed into arrays with fixed scale size, 3D position, and color value using in-house software (tif2laz v1.0). 2) Images to 3D-PC: the previously assigned values were used for the creation of volumetric models formatted as 3D-PC, and the resulting file was saved as a compressed geospatial file (i.e., .laz) with a converter (tif2laz v1.0) to facilitate its online visualization and interaction. 3) 3D Filtering: the 3D-PC model was previewed, and the existing noise was removed using 3D software (CloudCompare v.2.11.2). 4) Deployment: the model was subsequently deployed on an octree-based 3D-PC renderer platform (Potree v.1.7.2), and an ordinary web server was created for the online use of the final models. Results Total and segmented visualization of the models, including interactions with 3D clipping boxes, and measurement tools, were possible using the online platform. The process data characteristics were as follows: 1) VHP male dataset (Fig. 1A & 2A): 4.3 billion 3D points obtained from 1871 images (4096 x 2700 resolution) at 1 mm interval. Data set size: 64 GB. 2) VHP female (Fig. 1B & 2B): 12 billion 3D points from 5189 images (4096 x 3061 resolution) at 0.33 mm interval. Data set size: 189 GB. 3) VHP 2.0 (Fig. 1C & 2C): 1.5 billion 3D points from 1481 images (1056 x 1528 resolution) at 0.5 mm interval. Data set size: 17 GB. Conclusion The use of 3D-PC demonstrated to be a cost-efficient method for the spatial representation of the anatomical geometry generated from the VHP data sets. The advent of novel computer graphic and geospatial technologies along with the optimization of online 3D rendering techniques, would likely offer new opportunities for the education and research of giga-resolution anatomical data sets. Further research needs to be done to elucidate the potential applications to the use of 3D-PC in anatomy and medical imaging.

A Multiscale Nested Sampling Method for Representative Albedo Observations at Various Pixel Scales

Validation of satellite products is necessary to enable end-users to know the fitness and exploit their full potential in supporting the decision-making process. Representative in situ observations need to be obtained to make an independent, unified, standard, and traceable validation due to the spatial scale mismatch between satellite and in situ -based observations. Under the demand of validating various pixel scales of satellite products ranging from a few meters to kilometers, this article proposed a multiscale nested sampling method (MNSM), which is able to provide representative ground observations at various pixel scales under a unified accuracy requirement. The basic idea is to extract the spatiotemporal variation information of surface variables using long time sequences of high-resolution images. And the sample locations were optimized with a random combination method using an index of spatiotemporal representativeness. The optimal sampling was carried out from a coarse pixel scale to a high pixel scale with the top–down nested approach. The final “point” in situ observations can represent surface variables at different pixel scales with a unified and predetermined accuracy. It was found that the representativeness error of a single in situ measurement and the minimum number of required filed plots under a unified sampling accuracy requirement are partly determined by spatial heterogeneity and partly determined by the spatial scale mismatch between the plot size and the pixel size to be matched. The sampling method is applicable to many other surface variables (e.g., soil moisture, biomass, albedo, GPP, and LAI) that exhibiting spatial heterogeneity.

Estimation of High Framerate Digital Subtraction Angiography Sequences at Low Radiation Dose.

Digital Subtraction Angiography (DSA) provides high resolution image sequences of blood flow through arteries and veins and is considered the gold standard for visualizing cerebrovascular anatomy for neurovascular interventions. However, acquisition frame rates are typically limited to 1-3 fps to reduce radiation exposure, and thus DSA sequences often suffer from stroboscopic effects. We present the first approach that permits generating high frame rate DSA sequences from low frame rate acquisitions eliminating these artifacts without increasing the patient's exposure to radiation. Our approach synthesizes new intermediate frames using a phase-aware Convolutional Neural Network. This network accounts for the non-linear blood flow progression due to vessel geometry and initial velocity of the contrast agent. Our approach out-performs existing methods and was tested on several low frame rate DSA sequences of the human brain resulting in sequences of up to 17 fps with smooth and continuous contrast flow, free of flickering artifacts.