scholarly journals Real-time processing pipeline for 3D imaging applications

Author(s):  
D. P. Chaikalis ◽  
N. P. Sgouros ◽  
D. E. Maroulis
2021 ◽  
Vol 50 (2) ◽  
pp. 20200249-20200249
Author(s):  
贺文静 Wenjing He ◽  
胡坚 Jian Hu ◽  
陈育伟 Yuwei Chen ◽  
潘苗苗 Miaomiao Pan ◽  
朱运维 Yunwei Zhu ◽  
...  

2021 ◽  
Author(s):  
Masaya Misaki ◽  
Jerzy Bodurka ◽  
Martin P Paulus

We introduce a python library for real-time fMRI (rtfMRI) data processing systems, Real-Time Processing System in python (RTPSpy), to provide building blocks for a custom rtfMRI application with extensive and advanced functionalities. RTPSpy is a library package including 1) a fast, comprehensive, and flexible online fMRI denoising pipeline comparable to offline processing, 2) utilities for fast and accurate anatomical image processing to define a target region on-site, 3) a simulation system of online fMRI processing to optimize a pipeline and target signal calculation, 4) interface to an external application for feedback presentation, and 5) a boilerplate graphical user interface (GUI) integrating operations with RTPSpy library. Since online fMRI data processing cannot be equivalent to offline, we discussed the limitations of online analysis and their solutions in the RTPSpy implementation. We developed a fast and accurate anatomical image processing script with fast tissue segmentation (FastSeg), image alignment, and spatial normalization, utilizing the FastSurfer, AFNI, and ANTs. We confirmed that the FastSeg output was comparable with FreeSurfer, and could complete all the anatomical image processing in a few minutes. Thanks to its highly modular architecture, RTPSpy can easily be used for a simulation analysis to optimize a processing pipeline and target signal calculation. We present a sample script for building a real-time processing pipeline and running a simulation using RTPSpy. The library also offers a simple signal exchange mechanism with an external application. An external application can receive a real-time neurofeedback signal from RTPSpy in a background thread with a few lines of script. While the main components of the RTPSpy are the library modules, we also provide a GUI class for easy access to the RTPSpy functions. The boilerplate GUI application provided with the package allows users to develop a customized rtfMRI application with minimum scripting labor. Finally, we discussed the limitations of the package regarding environment-specific implementations. We believe that RTPSpy is an attractive option for developing rtfMRI applications highly optimized for individual purposes. The package is available from GitHub (https://github.com/mamisaki/RTPSpy) with GPL3 license.


2021 ◽  
Vol 50 (2) ◽  
pp. 20200249-20200249
Author(s):  
贺文静 Wenjing He ◽  
胡坚 Jian Hu ◽  
陈育伟 Yuwei Chen ◽  
潘苗苗 Miaomiao Pan ◽  
朱运维 Yunwei Zhu ◽  
...  

2008 ◽  
Author(s):  
Ivelin Bakalski ◽  
Joao Pereira Do Carmo ◽  
Stephen Bellis ◽  
Robert Bond ◽  
Martin Himphries ◽  
...  

2017 ◽  
Vol 13 (S337) ◽  
pp. 171-174 ◽  
Author(s):  
L. Levin ◽  
W. Armour ◽  
C. Baffa ◽  
E. Barr ◽  
S. Cooper ◽  
...  

AbstractThe Square Kilometre Array will be an amazing instrument for pulsar astronomy. While the full SKA will be sensitive enough to detect all pulsars in the Galaxy visible from Earth, already with SKA1, pulsar searches will discover enough pulsars to increase the currently known population by a factor of four, no doubt including a range of amazing unknown sources. Real time processing is needed to deal with the 60 PB of pulsar search data collected per day, using a signal processing pipeline required to perform more than 10 POps. Here we present the suggested design of the pulsar search engine for the SKA and discuss challenges and solutions to the pulsar search venture.


Author(s):  
Daiki Matsumoto ◽  
Ryuji Hirayama ◽  
Naoto Hoshikawa ◽  
Hirotaka Nakayama ◽  
Tomoyoshi Shimobaba ◽  
...  

Author(s):  
David J. Lobina

The study of cognitive phenomena is best approached in an orderly manner. It must begin with an analysis of the function in intension at the heart of any cognitive domain (its knowledge base), then proceed to the manner in which such knowledge is put into use in real-time processing, concluding with a domain’s neural underpinnings, its development in ontogeny, etc. Such an approach to the study of cognition involves the adoption of different levels of explanation/description, as prescribed by David Marr and many others, each level requiring its own methodology and supplying its own data to be accounted for. The study of recursion in cognition is badly in need of a systematic and well-ordered approach, and this chapter lays out the blueprint to be followed in the book by focusing on a strict separation between how this notion applies in linguistic knowledge and how it manifests itself in language processing.


2020 ◽  
pp. 1-25
Author(s):  
Theres Grüter ◽  
Hannah Rohde

Abstract This study examines the use of discourse-level information to create expectations about reference in real-time processing, testing whether patterns previously observed among native speakers of English generalize to nonnative speakers. Findings from a visual-world eye-tracking experiment show that native (L1; N = 53) but not nonnative (L2; N = 52) listeners’ proactive coreference expectations are modulated by grammatical aspect in transfer-of-possession events. Results from an offline judgment task show these L2 participants did not differ from L1 speakers in their interpretation of aspect marking on transfer-of-possession predicates in English, indicating it is not lack of linguistic knowledge but utilization of this knowledge in real-time processing that distinguishes the groups. English proficiency, although varying substantially within the L2 group, did not modulate L2 listeners’ use of grammatical aspect for reference processing. These findings contribute to the broader endeavor of delineating the role of prediction in human language processing in general, and in the processing of discourse-level information among L2 users in particular.


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