Conventional Mapping Methods Research Articles

Rotor Localization and Phase Mapping of Cardiac Excitation Waves Using Deep Neural Networks.

The analysis of electrical impulse phenomena in cardiac muscle tissue is important for the diagnosis of heart rhythm disorders and other cardiac pathophysiology. Cardiac mapping techniques acquire local temporal measurements and combine them to visualize the spread of electrophysiological wave phenomena across the heart surface. However, low spatial resolution, sparse measurement locations, noise and other artifacts make it challenging to accurately visualize spatio-temporal activity. For instance, electro-anatomical catheter mapping is severely limited by the sparsity of the measurements, and optical mapping is prone to noise and motion artifacts. In the past, several approaches have been proposed to create more reliable maps from noisy or sparse mapping data. Here, we demonstrate that deep learning can be used to compute phase maps and detect phase singularities in optical mapping videos of ventricular fibrillation, as well as in very noisy, low-resolution and extremely sparse simulated data of reentrant wave chaos mimicking catheter mapping data. The self-supervised deep learning approach is fundamentally different from classical phase mapping techniques. Rather than encoding a phase signal from time-series data, a deep neural network instead learns to directly associate phase maps and the positions of phase singularities with short spatio-temporal sequences of electrical data. We tested several neural network architectures, based on a convolutional neural network (CNN) with an encoding and decoding structure, to predict phase maps or rotor core positions either directly or indirectly via the prediction of phase maps and a subsequent classical calculation of phase singularities. Predictions can be performed across different data, with models being trained on one species and then successfully applied to another, or being trained solely on simulated data and then applied to experimental data. Neural networks provide a promising alternative to conventional phase mapping and rotor core localization methods. Future uses may include the analysis of optical mapping studies in basic cardiovascular research, as well as the mapping of atrial fibrillation in the clinical setting.

Simultaneous T1 , T2 , and T1ρ cardiac magnetic resonance fingerprinting for contrast agent-free myocardial tissue characterization.

To develop a simultaneous T1 , T2 , and T1ρ cardiac magnetic resonance fingerprinting (MRF) approach to enable comprehensive contrast agent-free myocardial tissue characterization in a single breath-hold scan. A 2D gradient-echo electrocardiogram-triggered cardiac MRF sequence with low flip angles, varying magnetization preparation, and spiral trajectory was acquired at 1.5 T to encode T1 , T2 , and T1⍴ simultaneously. The MRF images were reconstructed using low-rank inversion, regularized with a multicontrast patch-based higher-order reconstruction. Parametric maps were generated and matched in the singular value domain to extended phase graph-based dictionaries. The proposed approach was tested in phantoms and 10 healthy subjects and compared against conventional methods in terms of coefficients of determination and best fits for the phantom study, and in terms of Bland-Altman agreement, average values and coefficient of variation of T1 , T2 , and T1⍴ for the healthy subjects study. The T1 , T2 , and T1⍴ MRF values showed excellent correlation with conventional spin-echo and clinical mapping methods in phantom studies (r2 > 0.97). Measured MRF values in myocardial tissue (mean ± SD) were 1133 ± 33 ms, 38.8 ± 3.5 ms, and 52.0 ± 4.0 ms for T1 , T2 and T1⍴ , respectively, against 1053 ± 47 ms, 50.4 ± 3.9 ms, and 55.9 ± 3.3 ms for T1 modified Look-Locker inversion imaging, T2 gradient and spin echo, and T1⍴ turbo field echo, respectively. A cardiac MRF approach for simultaneous quantification of myocardial T1 , T2 , and T1ρ in a single breath-hold MR scan of about 16 seconds has been proposed. The approach has been investigated in phantoms and healthy subjects showing good agreement with reference spin echo measurements and conventional clinical maps.

Deep learning-based hydrothermal alteration mapping using GaoFen-5 hyperspectral data in the Duolong Ore District, Western Tibet, China

Hydrothermal alteration mapping is of great significance for porphyry copper deposit (PCD) exploration. Due to different metallogenetic environments, hydrothermal processes are heterogeneous especially in large-scale ore districts, leading to diverse physicochemical properties and spectral variations of wall-rock alteration. Conventional mapping methods mainly rely on shallow spectral features to identify hydrothermal alteration and cannot fully characterize the properties of the materials. By contrast, deep learning (DL) frameworks with multiple nonlinear layers can extract deep spectral and spatial features from hyperspectral imagery which is more robust and discriminative. Deep models were employed to the GaoFen-5 (GF-5) hyperspectral dataset in the Duolong district, to investigate the role of deep features in hydrothermal alteration mapping. For this purpose, we assess stacked autoencoder and several convolution neural networks which are computationally affordable for achieving large-area mineral exploration. More importantly, the mixed convolutions and covariance pooling algorithm achieved the best mapping results with the overall accuracy of 96.77%. The feature fusion method is also recommended, because of its lightweight structures. These two spectral–spatial classifiers produced appealing classification performance while reducing the misclassification among spectrally similar minerals and alleviating noise, demonstrating their powerful learning capabilities. Finally, spatial patterns of the hydrothermal alteration in the Duolong district can be taken as an important indicator of erosion degree of the porphyry-epithermal system. It is proved that the combination of the DL methods and the GF-5 hyperspectral data is effective in large-area mineral exploration and can be applied to other PCDs in the neighboring Bangong Co-Nujiang metallogenic belt.

Temperature dependence, accuracy, and repeatability of T1 and T2 relaxation times for the ISMRM/NIST system phantom measured using MR fingerprinting.

Before MR fingerprinting (MRF) can be adopted clinically, the derived quantitative values must be proven accurate and repeatable over a range of T1 and T2 values and temperatures. Correct assessment of accuracy and precision as well as comparison between measurements can only be performed when temperature is either controlled or corrected for. The purpose of this study was to investigate the temperature dependence of T1 and T2 MRF values and evaluate the accuracy and repeatability of temperature-corrected relaxation values derived from a B1 -corrected MRF-fast imaging with steady-state precession implementation using 2 different dictionary sizes. The International Society of MR in Medicine/National Institute of Standards and Technology phantom was scanned using an MRF sequence of 2 different lengths, a variable flip angle T1 , and a multi-echo spin echo T2 at 14 temperatures ranging from 15°C to 28°C and investigated with a linear regression model. Temperature-corrected accuracy was evaluated by correlating T1 and T2 times from each MRF dictionary with reference values. Repeatability was assessed using the coefficient of variation, with measurements taken over 30 separate sessions. There was a statistically significant fit of the model for MRF-derived T1 and T2 and temperature (p < 0.05) for all the spheres with a T1 > 500 ms. Both MRF methods showed a strong linear correlation with reference values for T1 (R2 = 0.996) and T2 (R2 = 0.982). MRF repeatability for T1 values was ≤1.4% and for T2 values was ≤3.4%. MRF demonstrated relaxation times with a temperature dependence similar to that of conventional mapping methods. Temperature-corrected T1 and T2 values from both dictionaries showed adequate accuracy and excellent repeatability in this phantom study.

Conductivity Tensor Imaging of the Human Brain Using Water Mapping Techniques.

Conductivity tensor imaging (CTI) has been recently proposed to map the conductivity tensor in 3D using magnetic resonance imaging (MRI) at the frequency range of the brain at rest, i.e., low-frequencies. Conventional CTI mapping methods process the trans-receiver phase of the MRI signal using the MR electric properties tomography (MR-EPT) technique, which in turn involves the application of the Laplace operator. This results in CTI maps with a low signal-to-noise ratio (SNR), artifacts at tissue boundaries and a limited spatial resolution. In order to improve on these aspects, a methodology independent from the MR-EPT method is proposed. This relies on the strong assumption for which electrical conductivity is univocally pre-determined by water concentration. In particular, CTI maps are calculated by combining high-frequency conductivity derived from water maps and multi b-value diffusion tensor imaging (DTI) data. Following the implementation of a pipeline to optimize the pre-processing of diffusion data and the fitting routine of a multi-compartment diffusivity model, reconstructed conductivity images were evaluated in terms of the achieved spatial resolution in five healthy subjects scanned at rest. We found that the pre-processing of diffusion data and the optimization of the fitting procedure improve the quality of conductivity maps. We achieve reproducible measurements across healthy participants and, in particular, we report conductivity values across subjects of 0.55 ± 0.01, 0.3 ± 0.01 and 2.15 ± 0.02 for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), respectively. By attaining an actual spatial resolution of the conductivity tensor close to 1 mm in-plane isotropic, partial volume effects are reduced leading to good discrimination of tissues with similar conductivity values, such as GM and WM. The application of the proposed framework may contribute to a better definition of the head tissue compartments in electroencephalograpy/magnetoencephalography (EEG/MEG) source imaging and be used as biomarker for assessing conductivity changes in pathological conditions, such as stroke and brain tumors.

On the use of Sentinel-2 satellites and lidar surveys for the change detection of shallow bathymetry: The case study of North Carolina inlets

Mapping bathymetric change is a core task for a wide range of navigation, research, monitoring, and design applications. Satellite-derived bathymetry (SDB) can support this activity, particularly when using data from a platform, like the Sentinel-2A/B twin mission of the Copernicus programme, which provides routine and repetitive image acquisition at 10 m spatial resolution. As a result, SDB can expand on the temporal and spatial scope of more conventional mapping methods, such as lidar (or multi-beam). In this study, we use SDB, in comparison with high-resolution airborne lidar bathymetry (ALB), to quantify bathymetric changes at two inlets in North Carolina following the impacts of the devastating Hurricane Florence in September 2018. A recently developed multi-temporal SDB model that overcomes problems associated with turbidity and noise effects (whitecaps, ships, cloud shadows, etc.) is used with Sentinel-2 imagery. We identify bathymetric changes in shallow areas with navigation channels in two of the most dynamic inlets in the Outer Banks, Oregon and Hatteras. Multiple lidar surveys are used to validate the SDB method and for an assessment of accuracy and vertical uncertainty. The multi-temporal SDB products and ALB both show similar results in the erosion/accretion patterns. Comparing the change determined from the two methods, gives a median absolute error of ~0.5 m of SDB compared with ALB, with bias of ±0.2 m for depths ≤7 m; errors that are equivalent to those associated with the SDB estimated absolute depths. The composited SDB yields greater spatial coverage than lidar because it can retrieve data in areas where the lidar mission may have been constrained by turbidity or surf, and also because of the 300-km swath width. The Sentinel-2 constellation provides five-day revisit at the equator (more frequent at higher latitudes), allowing rapid construction of a bathymetric map as well as developing an archive for retrospective analysis. By implementing the multi-temporal turbidity correction, SDB based on Sentinel-2 may substantially enhance existing survey methods for change detection and support operational and recursive coastal monitoring on local to regional scales.

Genome-wide association mapping in maize: status and prospects.

The online version contains supplementary material available at 10.1007/s13205-021-02799-4.

High-Resolution Ion-Mobility-Enabled Peptide Mapping for High-Throughput Critical Quality Attribute Monitoring.

Characterization and monitoring of post-translational modifications (PTMs) by peptide mapping is a ubiquitous assay in biopharmaceutical characterization. Often, this assay is coupled to reversed-phase liquid chromatographic (LC) separations that require long gradients to identify all components of the protein digest and resolve critical modifications for relative quantitation. Incorporating ion mobility (IM) as an orthogonal separation that relies on peptide structure can supplement the LC separation by providing an additional differentiation filter to resolve isobaric peptides, potentially reducing ambiguity in identification through mobility-aligned fragmentation and helping to reduce the run time of peptide mapping assays. A next-generation high-resolution ion mobility (HRIM) technique, based on structures for lossless ion manipulations (SLIM) technology with a 13 m ion path, provides peak capacities and higher resolving power that rivals traditional chromatographic separations and, owing to its ability to resolve isobaric peptides that coelute in faster chromatographic methods, allows for up to 3× shorter run times than conventional peptide mapping methods. In this study, the NIST monoclonal antibody IgG1κ (NIST RM 8671, NISTmAb) was characterized by LC-HRIM-MS and LC-HRIM-MS with collision-induced dissociation (HRIM-CID-MS) using a 20 min analytical method. This approach delivered a sequence coverage of 96.5%. LC-HRIM-CID-MS experiments provided additional confidence in sequence determination. HRIM-MS resolved critical oxidations, deamidations, and isomerizations that coelute with their native counterparts in the chromatographic dimension. Finally, quantitative measurements of % modification were made using only the m/z-extracted HRIM arrival time distributions, showing good agreement with the reference liquid-phase separation. This study shows, for the first time, the analytical capability of HRIM using SLIM technology for enhancing peptide mapping workflows relevant to biopharmaceutical characterization.

Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China

Abstract Landslide hazards along the Jinsha River corridor pose serious threats to the lives and property of local residents and can affect the safety of hydropower facilities because of the large size, number, strong activity, and disaster chain characteristics that occur following such events (such as a landslide creating a dammed lake which fails and leads to flooding), thus attracting widespread attention in both China and the rest of the world. As there is currently no complete landslide inventory map that covers the entire Jinsha River corridor, in this study the Sentinel-1 and ALOS/PALSAR-2 datasets were employed to detect and map active landslides along the entire Jinsha River corridor. Complex geomorphological conditions, such as the humid climate, dense vegetation, and steep terrain, pose great challenges to conventional InSAR-based landslide mapping methods, which can lead to a high probability of mis-judgements and omissions of landslides. Therefore, we propose a new InSAR-based procedure that can be used to conduct large-area landslide mapping through the integration of surface deformation and geomorphological features. More than 360 SAR images covering the Jinsha River corridor were processed and more than 900 active landslides were detected and mapped over the entire Jinsha River corridor for the first time. In particular, several large-scale landslides with a length and/or width of >1 km were found. Our results show that the landslides over the Jinsha River corridor are mainly located in three high earthquake-prone areas and reservoir areas, and that the landslides are mainly distributed at elevations of 1500–2000 m a.s.l. and have slope angles of 15–25°. Moreover, the deformation time series results indicate that the heavy rainfall in the summer and the rapid decline of water level in the Jinsha River might be two significant factors that accelerate the deformation of active landslides and reactivate unstable slopes. The findings in this research can be directly applied to landslide hazard mitigation and prevention along the entire Jinsha River corridor. In particular, the proposed procedure can be used for the efficient and systematic mapping of active landslides in other regions with similarly complex geomorphological conditions.

Genetic Dissection of the Regulatory Mechanisms of Ace2 in the Infected Mouse Lung.

Acute lung injury (ALI) is an important cause of morbidity and mortality after viral infections, including influenza A virus H1N1, SARS-CoV, MERS-CoV, and SARS-CoV-2. The angiotensin I converting enzyme 2 (ACE2) is a key host membrane-bound protein that modulates ALI induced by viral infection, pulmonary acid aspiration, and sepsis. However, the contributions of ACE2 sequence variants to individual differences in disease risk and severity after viral infection are not understood. In this study, we quantified H1N1 influenza-infected lung transcriptomes across a family of 41 BXD recombinant inbred strains of mice and both parents—C57BL/6J and DBA/2J. In response to infection Ace2 mRNA levels decreased significantly for both parental strains and the expression levels was associated with disease severity (body weight loss) and viral load (expression levels of viral NA segment) across the BXD family members. Pulmonary RNA-seq for 43 lines was analyzed using weighted gene co-expression network analysis (WGCNA) and Bayesian network approaches. Ace2 not only participated in virus-induced ALI by interacting with TNF, MAPK, and NOTCH signaling pathways, but was also linked with high confidence to gene products that have important functions in the pulmonary epithelium, including Rnf128, Muc5b, and Tmprss2. Comparable sets of transcripts were also highlighted in parallel studies of human SARS-CoV-infected primary human airway epithelial cells. Using conventional mapping methods, we determined that weight loss at two and three days after viral infection maps to chromosome X—the location of Ace2. This finding motivated the hierarchical Bayesian network analysis, which defined molecular endophenotypes of lung infection linked to Ace2 expression and to a key disease outcome. Core members of this Bayesian network include Ace2, Atf4, Csf2, Cxcl2, Lif, Maml3, Muc5b, Reg3g, Ripk3, and Traf3. Collectively, these findings define a causally-rooted Ace2 modulatory network relevant to host response to viral infection and identify potential therapeutic targets for virus-induced respiratory diseases, including those caused by influenza and coronaviruses.

Presurgical brain mapping of the language network in pediatric patients with epilepsy using resting-state fMRI.

Epilepsy affects neural processing and often causes intra- or interhemispheric language reorganization, rendering localization solely based on anatomical landmarks (e.g., Broca's area) unreliable. Preoperative brain mapping is necessary to weigh the risk of resection with the risk of postoperative deficit. However, the use of conventional mapping methods (e.g., somatosensory stimulation, task-based functional MRI [fMRI]) in pediatric patients is technically difficult due to low compliance and their unique neurophysiology. Resting-state fMRI (rs-fMRI), a "task-free" technique based on the neural activity of the brain at rest, has the potential to overcome these limitations. The authors hypothesized that language networks can be identified from rs-fMRI by applying functional connectivity analyses. Cases in which both task-based fMRI and rs-fMRI were acquired as part of the preoperative clinical protocol for epilepsy surgery were reviewed. Task-based fMRI consisted of 2 language tasks and 1 motor task. Resting-state fMRI data were acquired while the patients watched an animated movie and were analyzed using independent component analysis (i.e., data-driven method). The authors extracted language networks from rs-fMRI data by performing a similarity analysis with functionally defined language network templates via a template-matching procedure. The Dice coefficient was used to quantify the overlap. Thirteen children underwent conventional task-based fMRI (e.g., verb generation, object naming), rs-fMRI, and structural imaging at 1.5T. The language components with the highest overlap with the language templates were identified for each patient. Language lateralization results from task-based fMRI and rs-fMRI mapping were comparable, with good concordance in most cases. Resting-state fMRI-derived language maps indicated that language was on the left in 4 patients (31%), on the right in 5 patients (38%), and bilateral in 4 patients (31%). In some cases, rs-fMRI indicated a more extensive language representation. Resting-state fMRI-derived language network data were identified at the patient level using a template-matching method. More than half of the patients in this study presented with atypical language lateralization, emphasizing the need for mapping. Overall, these data suggest that this technique may be used to preoperatively identify language networks in pediatric patients. It may also optimize presurgical planning of electrode placement and thereby guide the surgeon's approach to the epileptogenic zone.

Mapping replication timing domains genome wide in single mammalian cells with single-cell DNA replication sequencing.

Replication timing (RT) domains are stable units of chromosome structure that are regulated in the context of development and disease. Conventional genome-wide RT mapping methods require many S-phase cells for either the effective enrichment of replicating DNA through bromodeoxyuridine (BrdU) immunoprecipitation or the determination of copy-number differences during S-phase, which precludes their application to non-abundant cell types and single cells. Here, we provide a simple, cost-effective, and robust protocol for single-cell DNA replication sequencing (scRepli-seq). The scRepli-seq methodology relies on whole-genome amplification (WGA) of genomic DNA (gDNA) from single S-phase cells and next-generation sequencing (NGS)-based determination of copy-number differences that arise between replicated and unreplicated DNA. Haplotype-resolved scRepli-seq, which distinguishes pairs of homologous chromosomes within a single cell, is feasible by using single-nucleotide polymorphism (SNP)/indel information. We also provide computational pipelines for quality control, normalization, and binarization of the scRepli-seq data. The experimental portion of this protocol (before sequencing) takes 3 d.

Automated Tax Mapping from UAV Multispectral Imagery

Tax mapping is an essential element for efficient management of real estate property and tax management, paving the way for effective implementation of g-governance practices in India. Conventional mapping of individual buildings and their attributes involves large effort and is time-consuming. The use of high-resolution UAV imagery facilitates the process of automatic extraction of land parcel, roof area and height by providing accurate and latest building and landuse information for improved tax assessment and collection. Image segmentation has been performed on UAV imagery applied to differentiate rooftop surfaces from other features such as roads and vegetation using gray level thresholding. An example-based feature extraction algorithm was adopted to extract the land parcel and rooftop surfaces from the segmented image. Further, DSM and DTM derived from UAV imagery were used to determine the height of the buildings by extraction of terrain points and rooftop points. The extracted building height was used to estimate the numbers of floors in the building using thresholding. The number of floors and rooftop area were used to derive the total floor area of each building. The property tax to be levied for each building was calculated automatically using total floor area in efficient and scientific manner. The UAV imagery used in the study enabled rapid mapping of buildings parcels for tax assessment compared to conventional mapping methods.

Rapid T1rho dispersion imaging for improved characterization of myocardial tissue using synthetic dispersion reconstruction

Abstract Introduction Over the past decade, CMRI has become the method of choice for characterizing fibrotic scars. Native T1ρ mapping offers an alternative to conventional T1 and T2 quantification techniques due to its high sensitivity to low-frequency processes. In addition, there is the possibility of T1ρ dispersion imaging, which could be used as a sensitive biomarker for assessing myocardial fibrosis [1]. However, due to a very long measurement time, T1ρ dispersion quantification in myocardium can hardly be done in the limited time of a small animal study. In this work we present a concept for rapid T1ρ dispersion quantification based on the new approach of synthetic dispersion reconstruction (SynDR). Theory A T1ρ map is calculated by measuring Nt T1ρ weighted images using different spin lock (SL) times. T1ρ dispersion quantification requires Nf T1ρ maps with different SL amplitudes. Hence the measurement time is very time consuming, because it requires the acquisition of Nt*Nf images (full mapping). With our new approach (SynDR), only a single T1ρ reference map and a series of dispersion weighted images need to be acquired. The T1ρ dispersion can be reconstructed by synthetically generated maps, whereby each map is calculated from the reference map and the dispersion weighted images, only requiring Nt+Nf images. Methods All measurements were performed on a 7T small animal scanner. The method was based on an optional cartesian/radial gradient echo sequence using large flip angles (45°) and an optimized readout sorting. The quantification accuracy of SynDR was compared with full mapping measurements in a phantom experiment and validated in vivo on mice. The synthetic T1ρ maps were used to perform a dispersion analysis in myocardium. Results The comparison between SynDR and the full mapping reference in phantoms showed a very high quantification accuracy with a mean/maximum deviation of 1.1% and 1.7%. Fig. 1 shows synthetic T1ρ maps (a) in healthy mice and the obtained dispersion map (b) using SynDR. In the dispersion analysis (c) a T1ρ slope of 5.6±1.5ms/kHz was obtained for myocardium. Here an acceleration factor of 4 could be realized in comparison to full mapping. In further measurements, an acceleration of 7.4 could be reached using a radial readout with KWIC filter view sharing. Discussion In this work, a novel T1ρ dispersion imaging method was presented that far exceeds the speed of conventional full mapping methods. The acceleration is based on avoiding unnecessary measurements of T1ρ weighted images through more efficient mathematical modeling. Further acceleration could be achieved using an optimized radial data acquisition. The method shows good image quality and high quantification accuracy both in phantom and in vivo. Based on the promising results, further studies in mice are planned to investigate the dispersion character of healthy and diseased tissues. Reference [1] Yin Q et al. Magn Reson Imaging. 2017 Oct; 42:69–73. SynDR method and T1ρ dispersion analysis Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): BRD, Bundesministerium für Bildung und Forschung

Acoustoelectric imaging of deep dipoles in a human head phantom for guiding treatment of epilepsy

Objective. This study employs a human head model with real skull to demonstrate the feasibility of transcranial acoustoelectric brain imaging (tABI) as a new modality for electrical mapping of deep dipole sources during treatment of epilepsy with much better resolution and accuracy than conventional mapping methods. Approach. This technique exploits an interaction between a focused ultrasound (US) beam and tissue resistivity to localize current source densities as deep as 63 mm at high spatial resolution (1 to 4 mm) and resolve fast time-varying currents with sub-ms precision. Main results. Detection thresholds through a thick segment of the human skull at biologically safe US intensities was below 0.5 mA and within range of strong currents generated by the human brain. Significance. This work suggests that 4D tABI may emerge as a revolutionary modality for real-time high-resolution mapping of neuronal currents for the purpose of monitoring, staging, and guiding treatment of epilepsy and other brain disorders characterized by abnormal rhythms.

Estimating Flood Characteristics Using Geomorphologic Flood Index with Regards to Rainfall Intensity-Duration-Frequency-Area Curves and CADDIES-2D Model in Three Iranian Basins

There is not enough data and computational power for conventional flood mapping methods in many parts of the world, thus fast and low-data-demanding methods are very useful in facing the disaster. This paper presents an innovative procedure for estimating flood extent and depth using only DEM SRTM 30 m and the Geomorphic Flood Index (GFI). The Geomorphologic Flood Assessment (GFA) tool which is the corresponding application of the GFI in QGIS is implemented to achieved the results in three basins in Iran. Moreover, the novel concept of Intensity-Duration-Frequency-Area (IDFA) curves is introduced to modify the GFI model by imposing a constraint on the maximum hydrologically contributing area of a basin. The GFA model implements the linear binary classification algorithm to classify a watershed into flooded and non-flooded areas using an optimized GFI threshold that minimizes the errors with a standard flood map of a small region in the study area. The standard hydraulic model envisaged for this study is the Cellular Automata Dual-DraInagE Simulation (CADDIES) 2D model which employs simple transition rules and a weight-based system rather than complex shallow water equations allowing fast flood modelling for large-scale problems. The results revealed that the floodplains generated by the GFI has a good agreement with the standard maps, especially in the fluvial rivers. However, the performance of the GFI decreases in the less steep and alluvial rivers. With some overestimation, the GFI model is also able to capture the general trend of water depth variations in comparison with the CADDIES-2D flood depth map. The modifications made in the GFI model, to confine the maximum precipitable area through implementing the IDFAs, improved the classification of flooded area and estimation of water depth in all study areas. Finally, the calibrated GFI thresholds were used to achieve the complete 100-year floodplain maps of the study areas.

High-definition map update framework for intelligent autonomous transfer vehicles

ABSTRACT Autonomous transfer vehicles (ATVs) can be considered as one of the critical components of context-aware structured smart factories in Industry 4.0 era. Conventional mapping methods such as grid maps can provide information for navigation, but they are not enough for complex environments that require interactions. On the other hand, high-definition (HD) mapping, which is mainly used in traffic networks, includes more information about an environment to perform excellent autonomous behaviour. In order to increase the efficiency of ATVs in flexible factories, an up-to-date environmental map information is required to perform successful long-term autonomous navigation. Therefore, when there exists a change in the environment, a simultaneous update of HD-map is as important as the creation of it. In this study, we propose an HD-map update methodology for ATVs that operates in smart factories. To the best of our knowledge, HD mapping has not been applied in smart factories. The proposed method includes the object detection and localisation tool to detect objects visually and determines their positions in connection with the conventional maps of the environment. Experimental results of a simulated factory environment demonstrate that the ATV can properly update the HD-map when a predefined sign is removed from or a new sign is added to the environment.

Indocyanine Green Fluorescence Versus Blue Dye or Radioisotope Regarding Detection Rate of Sentinel Lymph Node Biopsy and Nodes Removed in Breast Cancer: A Systematic Review and Meta-Analysis.

Background:Either blue dye (BD) or radioisotope (RI) is mainly used for sentinel lymph node biopsy (SLNB) in breast cancer patients. Unlike the BD, RI has lower false-negative rate of SLNB. However, its lymphoscintigraphy, difficulty in preoperative injection, and undetected sentinel lymph nodes in some cases cause surgeons to rely only on BD. Currently, indocyanine green (ICG) fluorescence method (ICG-SLNB) is increasingly used as an alternative to the conventional mapping methods in many centers. This systematic review compared ICG with the conventional method of BD or RI in terms of detection rate of SLNB and the number of sentinel lymph nodes (SLNs) removed in. Methods: We searched all relevant studies published between January 2000 and October 2019. All data on for evaluation of SLN detection rate, number of SLNs removed per patient, and tumor positive rate of SLNB were extracted. Results:A total of 30 studies, including 4,216 SLN procedures were retrieved. There was a statistically significant difference between ICG and BD method in terms of SLN detection rate (OR, 6.73; 95% CI, 4.20-10.78). However, there was no significant difference between ICG and RI in this regard (OR, 0.90; 95% CI, 0.40-2.03). The number of SLNs removed per patient were 2.35 (1.46-5.4), 1.92 (1.0-3.64), and 1.72 (1.35-2.08) for ICG, BD, and RI, respectively. Only in 8 studies, the tumor positive rates in SLNB could be analyzed (ICG, 8.5-20.7%; BD, 12.7-21.4%; RI, 11.3-16%). Conclusion:ICG-SLNB could be an additional or an alternative method for axillary node mapping in breast cancer.

Towards measuring the effect of flow in blood T1 assessed in a flow phantom and in vivo

Measurement of the blood T1 time using conventional myocardial T1 mapping methods has gained clinical significance in the context of extracellular volume (ECV) mapping and synthetic hematocrit (Hct). However, its accuracy is potentially compromised by in-flow of non-inverted/non-saturated spins and in-flow of spins which are not partially saturated from previous imaging pulses.Bloch simulations were used to analyze various flow effects separately. T1 measurements of gadolinium doped water were performed using a flow phantom with adjustable flow velocities at 3 T. Additionally, in vivo blood T1 measurements were performed in 6 healthy subjects (26 ± 5 years, 2 female). To study the T1 time as a function of the instantaneous flow velocity, T1 times were evaluated in an axial imaging slice of the descending aorta. Velocity encoded cine measurements were performed to quantify the flow velocity throughout the cardiac cycle.Simulation results show more than 30% loss in accuracy for 10% non-prepared in-flowing spins. However, in- and out-flow to the imaging plane only demonstrated minor impact on the T1 time. Phantom T1 times were decreased by up to 200 ms in the flow phantom, due to in-flow of non-prepared spins. High flow velocities cause in-flow of spins that lack partial saturation from the imaging pulses but only lead to negligible T1 time deviation (less than 30 ms). In vivo measurements confirm a substantial variation of the T1 time depending on the flow velocity. The highest aortic T1 times are observed at the time point of minimal flow with increased flow velocity leading to reduction of the measured T1 time by up to at peak velocity.In this work we attempt to dissect the effects of flow on T1 times, by using simulations, well-controlled, simplified phantom setup and the linear flow pattern in the descending aorta in vivo.

Color image enhancement with high saturation using piecewise linear gamut mapping

Most of the color image enhancement algorithms are implemented in two stages: gray scale image enhancement, which finds the target intensity, and then gamut mapping of the original color coordinates to the target. Therefore, hue preserving gamut mapping is an essential and crucial step, which influences colorfulness. In conventional color mapping methods, color saturation is reduced after intensity modification, which deteriorates subjective image quality. In this paper, a new color enhancement algorithm resulting in high color saturation is proposed. The proposed method employs multiplicative and additive color mapping to improve color saturation without clipping of a color component for increased target intensity as well as decreased cases. This new scheme is fast and effective, therefore, it can be employed to real time applications such as video signal processing.