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Noise and error propagation in diffu...

Desai, Shital Bipin.

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Noise and error propagation in diffusion tensor imaging.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Noise and error propagation in diffusion tensor imaging./
作者:	Desai, Shital Bipin.
面頁冊數:	85 p.
附註:	Source: Masters Abstracts International, Volume: 43-06, page: 2333.
Contained By:	Masters Abstracts International43-06.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1426580
ISBN:	9780542103735

Noise and error propagation in diffusion tensor imaging.
Desai, Shital Bipin.

Noise and error propagation in diffusion tensor imaging. - 85 p.

Source: Masters Abstracts International, Volume: 43-06, page: 2333.

Thesis (M.S.E.E.)--West Virginia University, 2005.

Diffusion Tensor Imaging (DTI) is the in vivo visualization and analysis of white matter fiber tracts by measuring the anisotropy of water molecule diffusion in the brain tissue. DTI has been increasingly used in clinical imaging. Diffusion weighted images are affected by noise from the human subject and the MRI scanner. This thesis studies the error propagation in the calculation of the DTI invariant anisotropy, mainly the Fractional Anisotropy (FA) using four methods and their comparison in terms of error, filtering and computational efficiency using simulated and human brain data. These methods were Diffusion Tensor, Diffusion Ellipsoid, Hasan and Platonic Variance. The results showed similar trends across the simulated and real data sets. Of the four methods used to calculate FA, the Hasan method without diffusion tensor yielded best computational efficiency, but poor noise robustness, whereas the Platonic Variance method was more robust to noise and provided good computational efficiency.

ISBN: 9780542103735Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Noise and error propagation in diffusion tensor imaging.
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520 $a Diffusion Tensor Imaging (DTI) is the in vivo visualization and analysis of white matter fiber tracts by measuring the anisotropy of water molecule diffusion in the brain tissue. DTI has been increasingly used in clinical imaging. Diffusion weighted images are affected by noise from the human subject and the MRI scanner. This thesis studies the error propagation in the calculation of the DTI invariant anisotropy, mainly the Fractional Anisotropy (FA) using four methods and their comparison in terms of error, filtering and computational efficiency using simulated and human brain data. These methods were Diffusion Tensor, Diffusion Ellipsoid, Hasan and Platonic Variance. The results showed similar trends across the simulated and real data sets. Of the four methods used to calculate FA, the Hasan method without diffusion tensor yielded best computational efficiency, but poor noise robustness, whereas the Platonic Variance method was more robust to noise and provided good computational efficiency.
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