Real-time Image Data Research Articles

Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.

Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs.

Model-Based Analysis of Lactuca sativa Root Growth under the Action of Herbicides in Milli-Channel Arrays with In Situ Imaging.

Extracting practical information from the large amounts of data gathered during the live imaging analysis of plant organs is a challenging issue. The present work investigates the use of the logistic growth model to analyze experimental data from root elongation assays performed in milli-fluidic devices with in situ imaging. Lactuca sativa was used as a bioindicator and was subjected to wide concentration ranges of four different herbicides: 2,4-D, atrazine, glyphosate, and paraquat. The model parameters were directly connected to standard indicators of toxicity and plant development, such as the LD50 and the absolute growth rate, respectively. In addition, it was found that realistic predictions of the maximum root length can be achieved about 60 h before the bioassay end point, which could significantly shorten the turnaround time. The combination of milli-fluidic devices, real-time imaging, and model-based data analysis becomes a powerful tool for environmental studies and ecotoxicity testing.

(Invited) liquid Biopsy Tool: Integrated Aiot Digital Bead-Based Sensor for Molecular-Fingerprint Profiling of Extracellular Vesicles

Development of liquid biopsies for non-invasive tumor characterization techniques shows great promise for real-time monitoring of tumor recurrence, progression, and treatment response. To better assess the various tumor evolution, cellular heterogeneity, consequent drug-resistance mechanisms, it is critical to screen proteins and nucleic acids of extracellular vesicles (EVs) in biofluids. We are developing an integrated sensitive digital bead-based sensor enabling evaluation of molecular profiling of extracellular vesicles in glioblastoma. The tumor relevant genetic information in EVs is enriched by antibody-specific magnetic beads and amplified by reverse transcription recombinase polymerase amplification with CRISPR/Cas13a system in twenty-five thousand droplets, and results are analyzed by a miniaturized imaging device. The niches of our developing technique include a) the streamlined sample preparation process and miniaturized detection system enable the ultrasensitive detection of mRNA in extracellular vesicles within one and half hour, b) the fluorescent signal-to-noise ratio in each 40-µm sized droplet is significantly enhanced via using a simple Cas13a assay and triggering greater than 104 turnovers of fluorescent reporters on a chip, c) real-time image data are transferred to a cloud-based server to be classified with trained YOLOv5 models and RNAomic results are displayed on the Raspberry Pi touchscreen. This system allows use by lower skill health care workers. This approach can provide inexpensive, rapid, and treatment-informative diagnostics for underserved regions, and underfunded health care systems. It could be further developed for other cancers, leading up the speed of diagnosis and treatment decision-making in using liquid biopsy, thus find widespread use in global health. Figure 1

Improved Detection of Ice Accretion Status on Transmission Lines Using YOLOv5s

Ice accretion on transmission lines is one of the major hidden dangers to the safe operation of power systems. The current ice image monitoring system of power companies urgently needs to quickly and accurately detect the ice accretion status from massive real-time image data to assist in the smooth implementation of de-icing work. Based on the original dataset constructed with the help of Shanxi Power Company, this paper establishes a transmission line ice accretion image dataset through image screening, image enhancement, image sliding window segmentation, and image annotation. On the basis of the original YOLOv5s model, this paper proposes three improvements: 1. Designing a feature enhancement module based on atrous convolution to increase the receptive field and obtain more contextual information while preserving the texture details of the feature map. 2. This paper simplifies cross-scale connections and contextual information weighting operations in the feature pyramid to enhance the network's feature extraction capabilities. 3. Introducing Swin-Transformer network structure in the process of feature fusion, further enhancing the semantic information and global perception of small targets. It can be seen from the ablation experiment analysis that the mAP0.5:0.5 of the proposed algorithm has increased by 7.6% compared with the original YOLOv5s. At the same time, the performance comparison experiment validated that the detection accuracy of the proposed algorithm achieved better performance with little sacrifice in detection speed.

Driver vigilance detection based on deep learning with fused thermal image information for public transportation

Autonomous-rail Rapid Transit (ART) is a new type of road public transportation with an automatic driving function, and it is one of the main places for epidemic spread. Scientific wearing of masks has been proven to be an extremely effective way to prevent an epidemic. However, drivers wearing masks make it difficult for traditional driver alert detection methods based on facial information to operate effectively. This paper proposes an ART driver alert detection system based on multimodal information fusion and deep learning, which can be applied to drivers wearing masks. The detection system is mainly divided into three modules: data acquisition module, preprocessing module, and vigilance detection module. The data acquisition module collects real-time face thermal imaging data, and environment temperature information of 13 people in the vigilance and fatigue state of the ART simulated driving environment. The preprocessing module proposes a face thermal imaging key information extraction method based on the constraints between Yolov5 and thermal image interframe constraints and extracts the mean temperature information and the hot voxel information (HV) of the eyes, forehead, and mask areas. Then the extracted information and the environment temperature information are transmitted together to the vigilance detection module for learning and classification. The vigilance detection module proposes a lightweight hybrid-attention optimization network, and this paper extracts training sets and testing sets from the data of 13 subjects. And the training set data is enhanced. Each sample is 90 frames. The maximum accuracy rate of the testing set reaches 99.57%. The feasibility of the detection system proposed in this paper is proved, which makes the thermal imaging and attention model show great potential in the application of traffic safety operation and maintenance.

Micro-flow imaging for in-situ and real-time enumeration and identification of microplastics in water

Microplastics (MPs) are emerging contaminants that have recently gained global attention. Current identification and quantification methods are known to be time-consuming, labor-intensive, and lack consensus on protocol standardization. This study explored the potential of micro-flow imaging (MFI) technology for rapid and in-situ identification and enumeration of MPs in water using two (2) MFI-based particle counters. Advantages, limitations, and recommendations for using MFI for MPs analysis were discussed. MPs with diverse physical (i.e., microbeads, fragments, fibers, and films) and surface (i.e., reflectivity, microporosity, color) characteristics were analyzed to understand the detection capabilities and limitations of MFI technology. Results demonstrated that MFI effectively automates most manually obtained particle features, such as size, color, object intensity and shape descriptors. It imparts consistency and reduces the subjective nature of results, thus enabling reliable comparison of the generated data. The particles can be further categorized based on their circularity and aspect ratio providing further insight into the shape and potential erosion of MPs in the environment. Transparent particles, often missed with other techniques such as microscopy, were detected by the MFI technology. The ability to assign particle IDs to MPs was an important advantage of the MFI technology that enabled the further investigation of selected MPs of interest. The limitations of the MFI technology were apparent in samples with high particle concentrations, with reflective MPs, and in the presence of bubbles. The color of the background against which the image was captured also influenced the detection accuracy. Procedural modifications during sample analysis and improvements in image analysis can assist in overcoming these challenges. MFI requires minimal sample preparation and gives real-time imaging data, making it a prime candidate for field monitoring in surface water systems in addition to laboratory analysis. With the potential application of machine learning and similar developments in the future, MFI-based particle counters are well-positioned to meet an important need in in-flow and real-time identification and enumeration of MPs.

Adaptive Traffic Signal Timer for A Signal in Chennai Metropolitan City Using Python and OpenCV

Background: The current traffic control system in India’s metropolises is ineffective because of the randomization of traffic density patterns throughout the day. For a predetermined period of time, the traffic signal timers switch traffic’s direction. Vehicles must therefore wait for a long time even when there is little traffic. Objectives: To continuously adjust the traffic signal timer based on the varying real-time traffic density and to significantly lessen traffic congestion. Methods: An adaptive traffic light timer is offered by this research using image processing algorithms. The real-time image data will be analyzed by the Raspberry Pi and OpenCV, which will determine when to regulate traffic flow. Findings: The adaptive timer minimizes vehicle waiting time and increases the number of turns granted to cars in each lane to cross the signal, which results in an improvement in traffic flow. Novelty: The use of image processing algorithms, the Raspberry Pi, and OpenCV makes the proposed system highly efficient and effective in adapting to the real-time traffic conditions. The suggested methodology will be more affordable than the currently used methodologies. Keywords: Adaptive Traffic Signal Timer; Intelligent Traffic Control

Deep learning for real-time detection of breast cancer presenting pathological nipple discharge by ductoscopy.

As a common breast cancer-related complaint, pathological nipple discharge (PND) detected by ductoscopy is often missed diagnosed. Deep learning techniques have enabled great advances in clinical imaging but are rarely applied in breast cancer with PND. This study aimed to design and validate an Intelligent Ductoscopy for Breast Cancer Diagnostic System (IDBCS) for breast cancer diagnosis by analyzing real-time imaging data acquired by ductoscopy. The present multicenter, case-control trial was carried out in 6 hospitals in China. Images for consecutive patients, aged ≥18 years, with no previous ductoscopy, were obtained from the involved hospitals. All individuals with PND confirmed from breast lesions by ductoscopy were eligible. Images from Beijing Chao-Yang Hospital were randomly assigned (8:2) to the training (IDBCS development) and internal validation (performance evaluation of the IDBCS) datasets. Diagnostic performance was further assessed with internal and prospective validation datasets from Beijing Chao-Yang Hospital; further external validation was carried out with datasets from 5 primary care hospitals. Diagnostic accuracies, sensitivities, specificities, and positive and negative predictive values for IDBCS and endoscopists (expert, competent, or trainee) in the detection of malignant lesions were obtained by the Clopper-Pearson method. Totally 11305 ductoscopy images in 1072 patients were utilized for developing and testing the IDBCS. Area under the curves (AUCs) in breast cancer detection were 0·975 (95%CI 0·899-0·998) and 0·954 (95%CI 0·925-0·975) in the internal validation and prospective datasets, respectively, and ranged between 0·922 (95%CI 0·866-0·960) and 0·965 (95%CI 0·892-0·994) in the 5 external validation datasets. The IDBCS had superior diagnostic accuracy compared with expert (0.912 [95%CI 0.839-0.959] vs 0.726 [0.672-0.775]; p<0.001), competent (0.699 [95%CI 0.645-0.750], p<0.001), and trainee (0.703 [95%CI 0.648-0.753], p<0.001) endoscopists. IDBCS outperforms clinical oncologists, achieving high accuracy in diagnosing breast cancer with PND. The novel system could help endoscopists improve their diagnostic efficacy in breast cancer diagnosis.

Open-source software package for on-the-fly deskewing and live viewing of volumetric lightsheet microscopy data.

Oblique plane microscopy, OPM, is a form of lightsheet microscopy that permits volumetric imaging of biological samples at high temporal and spatial resolution. However, the imaging geometry of OPM, and related variants of light sheet microscopy, distorts the coordinate frame of the presented image sections with respect to the real space coordinate frame in which the sample is moved. This makes live viewing and practical operation of such microscopes difficult. We present an open-source software package that utilises GPU acceleration and multiprocessing to transform the display of OPM imaging data in real time to produce a live extended depth of field projection. Image stacks can be acquired, processed and plotted at rates of several Hz, making live operation of OPMs, and similar microscopes, more user friendly and intuitive.

IoT-Based Medical Image Monitoring System Using HL7 in a Hospital Database.

In recent years, the healthcare system, along with the technology that surrounds it, has become a sector in much need of development. It has already improved in a wide range of areas thanks to significant and continuous research into the practical implications of biomedical and telemedicine studies. To ensure the continuing technological improvement of hospitals, physicians now also must properly maintain and manage large volumes of patient data. Transferring large amounts of data such as images to IoT servers based on machine-to-machine communication is difficult and time consuming over MQTT and MLLP protocols, and since IoT brokers only handle a limited number of bytes of data, such protocols can only transfer patient information and other text data. It is more difficult to handle the monitoring of ultrasound, MRI, or CT image data via IoT. To address this problem, this study proposes a model in which the system displays images as well as patient data on an IoT dashboard. A Raspberry Pi processes HL7 messages received from medical devices like an ultrasound machine (ULSM) and extracts only the image data for transfer to an FTP server. The Raspberry Pi 3 (RSPI3) forwards the patient information along with a unique encrypted image data link from the FTP server to the IoT server. We have implemented an authentic and NS3-based simulation environment to monitor real-time ultrasound image data on the IoT server and have analyzed the system performance, which has been impressive. This method will enrich the telemedicine facilities both for patients and physicians by assisting with overall monitoring of data.

Rapid online plant leaf area change detection with high-throughput plant image data

High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis.

Design and Implementation of Real-Time Image Acquisition Chip Based on Triple-Hybrid Encryption System

With the improved hardware storage capabilities and the rapid development of artificial intelligence image recognition technology, information is becoming image-oriented. Increasingly sensitive image data needs to be processed. When facing a large amount of real-time sensitive image data encryption and decryption, ensuring both the speed and the security is an urgent demand. This paper proposes an original triple-hybrid encryption system for a real-time sensitive image acquisition chip. This encryption system optimizes the symmetric encryption algorithm AES, asymmetric encryption algorithm ECC, and chip authentication algorithm PUF in pursuit of security, calculation speed, and to ensure that it is lightweight. The three optimized algorithms are further mixed and reused on the circuit level, to ensure mutual protection while making full use of their advantages. Apart from sensitive image protection at the algorithm level, the image chip itself is also protected by an innovative PUF chip authentication method that prevents it from being tampered with and copied. Triple-hybrid encryption system hardware implementation achieves a frequency of 132.5 MHz under the Virtex-5 FPGA with an area of 2834 Slices; with Virtex-7 FPGA, it reaches a frequency of 137.6 MHz with an area of 2716 Slices. The system is also implemented on SMIC 40 nm ASIC, and the clock frequency reaches 480 MHz and the area is 94,812.4 μm2. In terms of computing speed, the peak image encryption speed is 6.15 Gb/s, which meets the real-time image encryption requirement. In terms of hardware resource usage, AES reduced the hardware area by 60.1% compared with the results in other literature, ECC reduced the hardware area by 43.4%, and the PUF hardware area decreased exponentially with the increase in information entropy. The implementation of the three algorithms is reasonable and cost-effective, and the mixture of algorithms does not increase the required capacity of the hardware resource. The triple-hybrid encryption system cooperates with the image acquisition subsystem, storage subsystem, and asynchronous clock subsystem through software control to realize a complete triple-hybrid encryption SoC chip solution, and was successfully taped-out under the SMIC 40 nm process with all constraints passed and a total area of 10.59 mm2.

PocketQube development for earth exploration and vegetation monitoring

A PocketQube is being popular these days due to the enhancement in technologies for space research and earth observation. It is extremely vital to analyze the condition of vegetation on the earth surface because deforestation, forest fire and smuggling of precious plants have been increasing dramatically. The camera module in the payload captures the image from the space which helps in analyzing the vegetative condition of the forest on the earth’s surface. The provided image data is huge in order to maintain the quality of the images it captures. Hence, the received image data is further divided into chunks. Each chunks holds 64bits of hex values of each pixels of the image. The subsequent real time Image data is transmitted to the ground station and the received image is checked, analyzed and displayed on the screen. The received data is regenerated by combining the received chunks at a time at ground station. Based on the provided images further image processing activities are conducted in order to understand the condition of vegetation by calculating the vegetative index of each pixels in the received images.

Building Integrated Photovoltaics 4.0: Digitization of the Photovoltaic Integration in Buildings for a Resilient Infra at Large Scale

Building integrated photovoltaic (BIPV) systems have gained a lot of attention in recent years as they support the United Nations’ sustainable development goals of renewable energy generation and construction of resilient infrastructure. To make the BIPV system infra resilient, there is a need to adopt digital technologies such as the internet of things (IoT), artificial intelligence (AI), edge computing, unmanned aerial vehicles (UAV), and robotics. In this study, the current challenges in the BIPV system, such as the rise in the temperature of the PV modules, the occurrence of various faults, and the accumulation of dust particles over the module surface, have been identified and discussed based on the previous literature. To overcome the challenges, the significance and application of the integration of these digital technologies in the BIPV system are discussed along with the proposed architecture. Finally, the study discusses the vital recommendations for future directions, such as ML and DL for image enhancement and flaws detection in real-time image data; edge computing to implement DL for intelligent BIPV data analytics; fog computing for 6G assisted IoT network in BIPV; edge computing integration in UAV for intelligent automation and detection; augmented reality, virtual reality, and digital twins for virtual BIPV systems with research challenges of real-time implementation in the BIPV.

Application of remote sensing in the description of fluvial system in dryland: A case study of Golmud distributive fluvial system, Qaidam Basin, NW China

Distributive fluvial systems (DFS) are widespread in drylands in the northwestern China. Analyzing differences in fluvial morphology in drylands is beneficial for studying ancient rock records and the extra-terrestrial surface environment. The remote sensing image, characterized by real-time and possibility of repeated observations, is a vital tool for recording and comparing fluvial systems in drylands. Satellite remote sensing technology is a method of investigating fluvial morphologies. Due to the limited accuracy of satellite imagery, there are few reports on the detailed description of the fluvial system in drylands of NW China. We analyze the pattern of fluvial morphology changes in the Golmud distributive fluvial system (DFS) in the Qaidam Basin, northwestern China, using satellite remote sensing and unmanned aerial vehicles (UAV). Firstly, we use Google Earth real-time image data, historical image data, and radar digital elevation data to extract geomorphological information; then the UAV remote sensing image data were used to interpret fluvial network information; finally, we use the gray-scale differential vector method to describe the fluvial morphologies. Three zones have been identified in the Golmud DFS: the proximal, the medial, and the distal, by comparing the differences in topographic and geomorphic characteristics, fluvial morphologies, and sedimentary characteristics of the Golmud DFS. The proximal slope is higher than the other two zones, and the geomorphic features are mainly gravel gobi. The proximal fluvial morphologies are mainly large braided rivers, and sediments are more gravelly and less sandy. The medial slope is relatively small, and the geomorphic features are mostly oasis plains. The medial fluvial morphologies are mainly meandering rivers associating with braided rivers, and sediments are more sandy and less gravelly. The distal slope is the lowest, and the geomorphic features are mostly oasis plains, lakes, and marsh plains. The distal fluvial morphologies are mainly meandering rivers, and sediments are sandy and muddy. Comparison of the DFS from proximal to medial to distal in Golmud confirmed the potential of remote sensing image technology in identifying the fluvial morphologies and sedimentary facies distribution in dryland.

Association Between Internal Organ/Liver Tumor and External Surface Motion From Cine MR Images on an MRI-Linac.

Purposes/ObjectivesHistorically, motion correlation between internal tumor and external surrogates have been based on limited sets of X-ray or magnetic resonance (MR) images. With the recent clinical implementation of MR-guided linear accelerators, a vast quantity of continuous planar real-time MR imaging data is acquired. In this study, information was extracted from MR cine imaging during liver cancer treatments to establish associations between internal tumor/diaphragm and external surface/skin movement.Methods and MaterialsThis retrospective study used 305,644 MR image frames acquired over 118 treatment/imaging sessions of the first 23 liver cancer patients treated on an MRI-linac. 9 features were automatically determined on each MR image frame: Lung_Area, the posterior (Dia_Post), dome (Dia_Dome), and anterior (Dia_Ant) points of a diaphragmatic curve and the diaphragm curve point (Dia_Max), the chest (Chest) and the belly (Belly) skin points experiencing the maximum motion ranges; the superior-interior (SI) and posterior-anterior (PA) positions of a target. For every session, correlation analyses were performed twice among the 9 features: 1) over a breath-hold (BH) set and 2) on a pseudo free-breathing (PFB) generated by removing breath-holding frames.Results303,123 frames of images were successfully analyzed. For BH set analysis, correlation coefficients were as follows: 0.94 ± 0.07 between any two features among Dia_Post, Dia_Dome, Dia_Max, and Lung_Area; 0.95 ± 0.06 between SI and any feature among Dia_Post, Dia_Dome, Dia_Max, or Lung_Area; 0.76 ± 0.29 between SI and Belly (with 50% of correlations ≥ 0.87). The PFB set had 142,862 frames of images. For this set, correlation coefficients were 0.96 ± 0.06 between any two features among Dia_Post, Dia_Dome, Dia_Max, and Lung_Area; 0.95 ± 0.06 between SI and any feature among Dia_Post, Dia_Dome, Dia_Max, or Lung_Area; 0.80 ± 0.26 between SI and Belly (with 50% of correlations ≥ 0.91).ConclusionDiaphragmatic motion as assessed by cine MR imaging is highly correlated with liver tumor motion. Belly vertical motion is highly correlated with liver tumor longitudinal motion in approximately half of the cases. More detailed analyses of those cases displaying weak correlations are in progress.

Application Strategy of Panoramic Technology in Environmental Field Investigation

In order to improve the efficiency and quality of environmental field investigation, as well as realize real-time image data information sharing between the field environment and the investigation center, this paper first analyzes the application advantages of panoramic technology, followed by the types of panoramic technology, and finally takes the UAV panoramic technology as an example to demonstrate the application process of panoramic technology in environmental field investigation for the reference of relevant personnel.

192-OR: T-Type Calcium Channel Cav3.2 in GABAergic Neurons Regulates Rhythmic Glucose Metabolism

Systemic insulin sensitivity shows a diurnal rhythm with a peak upon waking. The GABAergic (γ-aminobutyric acid-producing) neurons in the suprachiasmatic nucleus (SCN) (SCNGABA neurons) were reported to control the diurnal rhythm of insulin-mediated suppression of hepatic glucose production in mice in earlier work1. SCNGABA neural firing activity displays a diurnal rhythm, with the intrinsic ionic mechanism underlying this temporal pattern not completely understood. Here we provided evidence supporting a role of the T type voltage-gated calcium ion channel subunit 3.2 (Cav3.2) in SCNGABA neurons in regulating rhythmic neural firing and glucose homeostasis. We found that Cav3.2 global knockout (Cav3.2KO) mice displayed disrupted diurnal rhythm of blood glucose levels. Selective deletion of Cav3.2 in the SCNGABA neurons abolished the oscillatory firing activity of SCNGABA neurons and led to glucose intolerance. Furthermore, our real-time calcium imaging data in freely moving mice showed that SCN GABA neuronal activity displayed a circadian rhythm pattern in WT control mice but not Cav3.2KO mice or mice with Cav3.2 selectively deleted in the SCNGABA neurons. These findings provide insights into how the calcium ion channel regulates the central circadian clock and glucose metabolism, with implications in the extended dawn phenomenon in type 2 diabetes. Keywords: Type 2 diabetes, calcium channel, Cav3.2, GABA neurons, circadian rhythm Reference: 1 Ding, G. et al. REV-ERB in GABAergic neurons controls diurnal hepatic insulin sensitivity. Nature 592, 763-767, doi:10.1038/s41586-021-03358-w (2021) . Disclosure P. Xu: None. W. Zhou: None. Z. Sun: None. Y. He: None. Funding NIH (P20 GM135002)

Patient-Specific Quantification of Normal and Bicuspid Aortic Valve Leaflet Deformations from Clinically Derived Images.

The clinical benefit of patient-specific modeling of heart valve disease remains an unrealized goal, often a result of our limited understanding of the in vivo milieu. This is particularly true in assessing bicuspid aortic valve (BAV) disease, the most common cardiac congenital defect in humans, which leads to premature and severe aortic stenosis or insufficiency (AS/AI). However, assessment of BAV risk for AS/AI on a patient-specific basis is hampered by the substantial degree of anatomic and functional variations that remain largely unknown. The present study was undertaken to utilize a noninvasive computational pipeline ( https://doi.org/10.1002/cnm.3142 ) that directly yields local heart valve leaflet deformation information using patient-specific real-time three-dimensional echocardiographic imaging (rt-3DE) data. Imaging data was collected for patients with normal tricuspid aortic valve (TAV, [Formula: see text]) and those with BAV ([Formula: see text] with fused left and right coronary leaflets and [Formula: see text] with fused right and non-coronary leaflets), from which the medial surface of each leaflet was extracted. The resulting deformation analysis resulted in, for the first time, quantified differences between the in vivo functional deformations of the TAV and BAV leaflets. Our approach was able to capture the complex, heterogeneous surface deformation fields in both TAV and BAV leaflets. We were able to identify and quantify differences in stretch patterns between leaflet types, and found in particular that stretches experienced by BAV leaflets during closure differ from those of TAV leaflets in terms of both heterogeneity as well as overall magnitude. Deformation is a key parameter in the clinical assessment of valvular function, and serves as a direct means to determine regional variations in structure and function. This study is an essential step toward patient-specific assessment of BAV based on correlating leaflet deformation and AS/AI progression, as it provides a means for assessing patient-specific stretch patterns.

Relative Pose Determination of Uncooperative Spacecraft Based on Circle Feature

This paper investigates the problem of spacecraft relative navigation with respect to an unknown target during the close-proximity operations in the on-orbit service system. The serving spacecraft is equipped with a Time-of-Flight (ToF) camera for object recognition and feature detection. A fast and robust relative navigation strategy for acquisition is presented without any extra information about the target by using the natural circle features. The architecture of the proposed relative navigation strategy consists of three ingredients. First, a point cloud segmentation method based on the auxiliary gray image is developed for fast extraction of the circle feature point cloud of the target. Secondly, a new parameter fitting method of circle features is proposed including circle feature calculation by two different geometric models and results’ fusion. Finally, a specific definition of the coordinate frame system is introduced to solve the relative pose with respect to the uncooperative target. In order to validate the efficiency of the segmentation, an experimental test is conducted based on real-time image data acquired by the ToF camera. The total time consumption is saved by 94%. In addition, numerical simulations are carried out to evaluate the proposed navigation algorithm. It shows good robustness under the different levels of noises.