The Experts below are selected from a list of 45 Experts worldwide ranked by ideXlab platform
Hsiu-ju Chen - One of the best experts on this subject based on the ideXlab platform.
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Clarifying the empirical Connection of new entrants' e-learning systems use to their job adaptation and their use patterns under the collective-individual training environment
Computers & Education, 2012Co-Authors: Hsiu-ju ChenAbstract:In recent years, with the development of e-learning, it is feasible for enterprises to adopt information systems to enhance organizations' human capital and knowledge renewal for competition. e-Learning systems designed for new entrants training aim to facilitate new entrants' job adaptation; however, the empirical link between their system use and job adaptation lacks. In addition, the influence of environmental variables on new entrants' e-learning training still needs clarification. Thus, based on the theoretical framework of the IS (information system) success model, this study is motivated to make an empirical Connection of new entrants' e-learning systems use to their job adaptation, including organizational socialization and overall job-adaptation outcome, and also to clarify their system use patterns under different collective-individual socialization training environments. Data from one hundred and eighty-six Valid respondents who entered their organizations within a year were gathered and analyzed with PLS (partial least square). The results suggested the Valid Connection of new entrants' e-learning systems use to their organizational socialization and overall job-adaptation outcome. New entrants also presented partially different system use patterns for adaptation under the different human interaction environments. The findings facilitated the design of training programs for new entrants at the e-learning environment.
Frederik Maes - One of the best experts on this subject based on the ideXlab platform.
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Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model
NeuroImage, 2015Co-Authors: Daan Christiaens, Marco Reisert, Thijs Dhollander, Stefan Sunaert, Paul Suetens, Frederik MaesAbstract:Diffusion-weighted imaging and tractography provide a unique, non-invasive technique to study the macroscopic structure and connectivity of brain white matter in vivo. Global tractography methods aim at reconstructing the full-brain fiber configuration that best explains the measured data, based on a generative signal model. In this work, we incorporate a multi-shell multi-tissue model based on spherical convolution, into a global tractography framework, which allows to deal with partial volume effects. The required tissue response functions can be estimated from and hence calibrated to the data. The resulting track reconstruction is quantitatively related to the apparent fiber density in the data. In addition, the fiber orientation distribution for white matter and the volume fractions of gray matter and cerebrospinal fluid are produced as ancillary results. Validation results on simulated data demonstrate that this data-driven approach improves over state-of-the-art streamline and global tracking methods, particularly in the Valid Connection rate. Results in human brain data correspond to known white matter anatomy and show improved modeling of partial voluming. This work is an important step toward detecting and quantifying white matter changes and connectivity in healthy subjects and patients.
Daan Christiaens - One of the best experts on this subject based on the ideXlab platform.
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Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model
NeuroImage, 2015Co-Authors: Daan Christiaens, Marco Reisert, Thijs Dhollander, Stefan Sunaert, Paul Suetens, Frederik MaesAbstract:Diffusion-weighted imaging and tractography provide a unique, non-invasive technique to study the macroscopic structure and connectivity of brain white matter in vivo. Global tractography methods aim at reconstructing the full-brain fiber configuration that best explains the measured data, based on a generative signal model. In this work, we incorporate a multi-shell multi-tissue model based on spherical convolution, into a global tractography framework, which allows to deal with partial volume effects. The required tissue response functions can be estimated from and hence calibrated to the data. The resulting track reconstruction is quantitatively related to the apparent fiber density in the data. In addition, the fiber orientation distribution for white matter and the volume fractions of gray matter and cerebrospinal fluid are produced as ancillary results. Validation results on simulated data demonstrate that this data-driven approach improves over state-of-the-art streamline and global tracking methods, particularly in the Valid Connection rate. Results in human brain data correspond to known white matter anatomy and show improved modeling of partial voluming. This work is an important step toward detecting and quantifying white matter changes and connectivity in healthy subjects and patients.
Marco Reisert - One of the best experts on this subject based on the ideXlab platform.
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Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model
NeuroImage, 2015Co-Authors: Daan Christiaens, Marco Reisert, Thijs Dhollander, Stefan Sunaert, Paul Suetens, Frederik MaesAbstract:Diffusion-weighted imaging and tractography provide a unique, non-invasive technique to study the macroscopic structure and connectivity of brain white matter in vivo. Global tractography methods aim at reconstructing the full-brain fiber configuration that best explains the measured data, based on a generative signal model. In this work, we incorporate a multi-shell multi-tissue model based on spherical convolution, into a global tractography framework, which allows to deal with partial volume effects. The required tissue response functions can be estimated from and hence calibrated to the data. The resulting track reconstruction is quantitatively related to the apparent fiber density in the data. In addition, the fiber orientation distribution for white matter and the volume fractions of gray matter and cerebrospinal fluid are produced as ancillary results. Validation results on simulated data demonstrate that this data-driven approach improves over state-of-the-art streamline and global tracking methods, particularly in the Valid Connection rate. Results in human brain data correspond to known white matter anatomy and show improved modeling of partial voluming. This work is an important step toward detecting and quantifying white matter changes and connectivity in healthy subjects and patients.
Paul Suetens - One of the best experts on this subject based on the ideXlab platform.
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Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model
NeuroImage, 2015Co-Authors: Daan Christiaens, Marco Reisert, Thijs Dhollander, Stefan Sunaert, Paul Suetens, Frederik MaesAbstract:Diffusion-weighted imaging and tractography provide a unique, non-invasive technique to study the macroscopic structure and connectivity of brain white matter in vivo. Global tractography methods aim at reconstructing the full-brain fiber configuration that best explains the measured data, based on a generative signal model. In this work, we incorporate a multi-shell multi-tissue model based on spherical convolution, into a global tractography framework, which allows to deal with partial volume effects. The required tissue response functions can be estimated from and hence calibrated to the data. The resulting track reconstruction is quantitatively related to the apparent fiber density in the data. In addition, the fiber orientation distribution for white matter and the volume fractions of gray matter and cerebrospinal fluid are produced as ancillary results. Validation results on simulated data demonstrate that this data-driven approach improves over state-of-the-art streamline and global tracking methods, particularly in the Valid Connection rate. Results in human brain data correspond to known white matter anatomy and show improved modeling of partial voluming. This work is an important step toward detecting and quantifying white matter changes and connectivity in healthy subjects and patients.
