scholarly journals Large field-of-view real-time MRI with a 32-channel system

2004 ◽  
Vol 52 (4) ◽  
pp. 878-884 ◽  
Author(s):  
Christopher J. Hardy ◽  
Robert D. Darrow ◽  
Manojkumar Saranathan ◽  
Randy O. Giaquinto ◽  
Yudong Zhu ◽  
...  
Keyword(s):  
Real Time ◽  
Field Of View ◽  
Large Field ◽  
Channel System

Live Demonstration: Real-Time Calcium Trace Extraction from Large-Field-of-View Miniscope

2021 ◽  
Author(s):  
Zhe Chen ◽  
Garrett J. Blair ◽  
Changliang Guo ◽  
Daniel Aharoni ◽  
Hugh T. Blair ◽  
...  
Keyword(s):  
Real Time ◽  
Field Of View ◽  
Large Field ◽  
Live Demonstration

Large field of view real-time three-dimensional imaging for ports

2011 ◽  
Author(s):  
Meijing Gao ◽  
Weilong Wu ◽  
Haihua Gu ◽  
Weihong Bi
Keyword(s):  
Real Time ◽  
Three Dimensional ◽  
Field Of View ◽  
Large Field ◽  
Three Dimensional Imaging

Development of large field of view and high-definition real-time digital radiography system

1995 ◽  
Author(s):  
Hisatake Yokouchi ◽  
Fumitaka Takahashi ◽  
Yoichi Onodera ◽  
Ken Ueda ◽  
Tetsuro Endo
Keyword(s):  
Real Time ◽  
Digital Radiography ◽  
Field Of View ◽  
Large Field ◽  
High Definition ◽  
Digital Radiography System ◽  
Radiography System

scholarly journals Mechanosensetive ATP Release Involves a Non-Conductive Pathway: Evidence from Large Field of View Real-Time Imaging

2017 ◽  
Vol 112 (3) ◽  
pp. 311a ◽  
Author(s):  
Olga Ponomarchuk ◽  
Francis Boudreault ◽  
Sergei N. Orlov ◽  
Ryszard Grygorczyk
Keyword(s):  
Real Time ◽  
Atp Release ◽  
Field Of View ◽  
Large Field ◽  
Real Time Imaging ◽  
Conductive Pathway

Compound Scanning with a Phased Array

1982 ◽  
Vol 4 (2) ◽  
pp. 93-107 ◽  
Author(s):  
David P. Shattuck ◽  
Olaf T. von Ramm
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Phased Array ◽  
Speckle Noise ◽  
Abdominal Imaging ◽  
Field Of View ◽  
Large Field ◽  
Grey Scale ◽  
Computer Controlled

Compound scans made with a dynamically focussed phased array system have been produced in real time. The scanner, intended for abdominal imaging, has a large field of view. The compounding improves the acquisition of echoes from specular targets by changing the orientation of the insonifying beam and also reduces the speckle noise in grey scale images. These gains are achieved while maintaining the high resolution and flexibility of a computer controlled phased array sector scanner. The configuration of the compound scanner is described, and in vivo abdominal scans are presented.

A real-time 3D large field-of-view MRI system with interactive table motion

2006 ◽  
Vol 29B (1) ◽  
pp. 28-41 ◽  
Author(s):  
M. Sabati ◽  
M.L. Lauzon ◽  
N. Nagarajappa ◽  
R. Frayne
Keyword(s):  
Real Time ◽  
Field Of View ◽  
Large Field ◽  
Table Motion

scholarly journals Computational Large Field-of-View RGB-D Integral Imaging System

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7407
Author(s):  
Geunho Jung ◽  
Yong-Yuk Won ◽  
Sang Min Yoon
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Imaging System ◽  
Field Of View ◽  
Large Field ◽  
Depth Information ◽  
Research Attention ◽  
Integral Imaging ◽  
Computational Integral Imaging ◽  
Micro Lens

The integral imaging system has received considerable research attention because it can be applied to real-time three-dimensional image displays with a continuous view angle without supplementary devices. Most previous approaches place a physical micro-lens array in front of the image, where each lens looks different depending on the viewing angle. A computational integral imaging system with a virtual micro-lens arrays has been proposed in order to provide flexibility for users to change micro-lens arrays and focal length while reducing distortions due to physical mismatches with the lens arrays. However, computational integral imaging methods only represent part of the whole image because the size of virtual lens arrays is much smaller than the given large-scale images when dealing with large-scale images. As a result, the previous approaches produce sub-aperture images with a small field of view and need additional devices for depth information to apply to integral imaging pickup systems. In this paper, we present a single image-based computational RGB-D integral imaging pickup system for a large field of view in real time. The proposed system comprises three steps: deep learning-based automatic depth map estimation from an RGB input image without the help of an additional device, a hierarchical integral imaging system for a large field of view in real time, and post-processing for optimized visualization of the failed pickup area using an inpainting method. Quantitative and qualitative experimental results verify the proposed approach’s robustness.

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