scholarly journals A fully automatic and highly efficient navigator gating technique for high-resolution free-breathing acquisitions: Continuously adaptive windowing strategy

2010 ◽  
Vol 64 (4) ◽  
pp. 1015-1026 ◽  
Author(s):  
P. Jhooti ◽  
J. Keegan ◽  
D. N. Firmin
Keyword(s):  
High Resolution ◽  
Free Breathing ◽  
Navigator Gating ◽  
Highly Efficient ◽  
Fully Automatic

SEM-Based Nanoprobing on 40, 32 and 28 nm CMOS Devices Challenges for Semiconductor Failure Analysis

2013 ◽  
Author(s):  
Erik Paul ◽  
Holger Herzog ◽  
Sören Jansen ◽  
Christian Hobert ◽  
Eckhard Langer
Keyword(s):  
Electron Microscope ◽  
High Resolution ◽  
Scanning Electron Microscope ◽  
Failure Analysis ◽  
Case Studies ◽  
State Of The Art ◽  
Root Cause ◽  
Highly Efficient ◽  
Technology Nodes ◽  
Scanning Electron

Abstract This paper presents an effective device-level failure analysis (FA) method which uses a high-resolution low-kV Scanning Electron Microscope (SEM) in combination with an integrated state-of-the-art nanomanipulator to locate and characterize single defects in failing CMOS devices. The presented case studies utilize several FA-techniques in combination with SEM-based nanoprobing for nanometer node technologies and demonstrate how these methods are used to investigate the root cause of IC device failures. The methodology represents a highly-efficient physical failure analysis flow for 28nm and larger technology nodes.

High-Resolution Fully Automatic Light Field Image Acquisition System Using One-Dimensional Camera Array

2021 ◽  
Author(s):  
Munkh-Uchral Erdenebat ◽  
Ki-Chul Kwon ◽  
Nyamsuren Darkhanbaatar ◽  
Jin Kyu Jung ◽  
Sang-Keun Gil ◽  
...  
Keyword(s):  
High Resolution ◽  
Light Field ◽  
Image Acquisition ◽  
Acquisition System ◽  
One Dimensional ◽  
Camera Array ◽  
Image Acquisition System ◽  
Fully Automatic ◽  
Light Field Image

scholarly journals An objective and efficient method for estimating probabilistic coastal inundation hazards

2019 ◽  
Vol 99 (2) ◽  
pp. 1105-1130 ◽  
Author(s):  
Kun Yang ◽  
Vladimir Paramygin ◽  
Y. Peter Sheng
Keyword(s):  
High Resolution ◽  
Storm Surge ◽  
Computational Cost ◽  
Joint Probability ◽  
Water Levels ◽  
Computational Time ◽  
Probability Method ◽  
Highly Efficient ◽  
Inundation Maps ◽  
Flood Elevation

Abstract The joint probability method (JPM) is the traditional way to determine the base flood elevation due to storm surge, and it usually requires simulation of storm surge response from tens of thousands of synthetic storms. The simulated storm surge is combined with probabilistic storm rates to create flood maps with various return periods. However, the map production requires enormous computational cost if state-of-the-art hydrodynamic models with high-resolution numerical grids are used; hence, optimal sampling (JPM-OS) with a small number of (~ 100–200) optimal (representative) storms is preferred. This paper presents a significantly improved JPM-OS, where a small number of optimal storms are objectively selected, and simulated storm surge responses of tens of thousands of storms are accurately interpolated from those for the optimal storms using a highly efficient kriging surrogate model. This study focuses on Southwest Florida and considers ~ 150 optimal storms that are selected based on simulations using either the low fidelity (with low resolution and simple physics) SLOSH model or the high fidelity (with high resolution and comprehensive physics) CH3D model. Surge responses to the optimal storms are simulated using both SLOSH and CH3D, and the flood elevations are calculated using JPM-OS with highly efficient kriging interpolations. For verification, the probabilistic inundation maps are compared to those obtained by the traditional JPM and variations of JPM-OS that employ different interpolation schemes, and computed probabilistic water levels are compared to those calculated by historical storm methods. The inundation maps obtained with the JPM-OS differ less than 10% from those obtained with JPM for 20,625 storms, with only 4% of the computational time.

scholarly journals Iterative motion‐compensation reconstruction ultra‐short TE (iMoCo UTE) for high‐resolution free‐breathing pulmonary MRI

2019 ◽  
Vol 83 (4) ◽  
pp. 1208-1221 ◽  
Author(s):  
Xucheng Zhu ◽  
Marilynn Chan ◽  
Michael Lustig ◽  
Kevin M. Johnson ◽  
Peder E. Z. Larson
Keyword(s):  
High Resolution ◽  
Motion Compensation ◽  
Free Breathing

scholarly journals Accelerated high-resolution free-breathing 3D whole-heart T2-prepared black-blood and bright-blood cardiovascular magnetic resonance

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Teresa Correia ◽  
Giulia Ginami ◽  
Imran Rashid ◽  
Giovanna Nordio ◽  
Reza Hajhosseiny ◽  
...  
Keyword(s):  
Cardiovascular Magnetic Resonance ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Healthy Subjects ◽  
Free Breathing ◽  
Black Blood ◽  
Contrast Injection ◽  
Post Contrast ◽  
Whole Heart ◽  
Bright Blood

Abstract Background The free-breathing 3D whole-heart T2-prepared Bright-blood and black-blOOd phase SensiTive inversion recovery (BOOST) cardiovascular magnetic resonance (CMR) sequence was recently proposed for simultaneous bright-blood coronary CMR angiography and black-blood late gadolinium enhancement (LGE) imaging. This sequence enables simultaneous visualization of cardiac anatomy, coronary arteries and fibrosis. However, high-resolution (< 1.4 × 1.4 × 1.4 mm3) fully-sampled BOOST requires long acquisition times of ~ 20 min. Methods In this work, we propose to extend a highly efficient respiratory-resolved motion-corrected reconstruction framework (XD-ORCCA) to T2-prepared BOOST to enable high-resolution 3D whole-heart coronary CMR angiography and black-blood LGE in a clinically feasible scan time. Twelve healthy subjects were imaged without contrast injection (pre-contrast BOOST) and 10 patients with suspected cardiovascular disease were imaged after contrast injection (post-contrast BOOST). A quantitative analysis software was used to compare accelerated pre-contrast BOOST against the fully-sampled counterpart (vessel sharpness and length of the left and right coronary arteries). Moreover, three cardiologists performed diagnostic image quality scoring for clinical 2D LGE and both bright- and black-blood 3D BOOST imaging using a 4-point scale (1–4, non-diagnostic–fully diagnostic). A two one-sided test of equivalence (TOST) was performed to compare the pre-contrast BOOST images. Nonparametric TOST was performed to compare post-contrast BOOST image quality scores. Results The proposed method produces images from 3.8 × accelerated non-contrast-enhanced BOOST acquisitions with comparable vessel length and sharpness to those obtained from fully- sampled scans in healthy subjects. Moreover, in terms of visual grading, the 3D BOOST LGE datasets (median 4) and the clinical 2D counterpart (median 3.5) were found to be statistically equivalent (p < 0.05). In addition, bright-blood BOOST images allowed for visualization of the proximal and middle left anterior descending and right coronary sections with high diagnostic quality (mean score > 3.5). Conclusions The proposed framework provides high‐resolution 3D whole-heart BOOST images from a single free-breathing acquisition in ~ 7 min.

scholarly journals AUTOMATIC ROAD EXTRACTION BASED ON INTEGRATION OF HIGH RESOLUTION LIDAR AND AERIAL IMAGERY

2015 ◽  
Vol XL-1-W5 ◽  
pp. 583-587 ◽  
Author(s):  
S. Rahimi ◽  
H. Arefi ◽  
R. Bahmanyar
Keyword(s):  
High Resolution ◽  
Principal Component ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Road Extraction ◽  
The Road ◽  
Human Effort ◽  
Fully Automatic ◽  
Straight Lines ◽  
Road Network Detection

In recent years, the rapid increase in the demand for road information together with the availability of large volumes of high resolution Earth Observation (EO) images, have drawn remarkable interest to the use of EO images for road extraction. Among the proposed methods, the unsupervised fully-automatic ones are more efficient since they do not require human effort. Considering the proposed methods, the focus is usually to improve the road network detection, while the roads’ precise delineation has been less attended to. In this paper, we propose a new unsupervised fully-automatic road extraction method, based on the integration of the high resolution LiDAR and aerial images of a scene using Principal Component Analysis (PCA). This method discriminates the existing roads in a scene; and then precisely delineates them. Hough transform is then applied to the integrated information to extract straight lines; which are further used to segment the scene and discriminate the existing roads. The roads’ edges are then precisely localized using a projection-based technique, and the round corners are further refined. Experimental results demonstrate that our proposed method extracts and delineates the roads with a high accuracy.

Sign in / Sign up

Export Citation Format

Share Document