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Sparsity enhanced reconstruction met...

Yao, Jixing.

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Sparsity enhanced reconstruction methods for diffuse optical tomography.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Sparsity enhanced reconstruction methods for diffuse optical tomography./
作者:	Yao, Jixing.
面頁冊數:	105 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.
Contained By:	Dissertation Abstracts International75-05B(E).
標題:	Health Sciences, Radiology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3610568
ISBN:	9781303704246

Sparsity enhanced reconstruction methods for diffuse optical tomography.
Yao, Jixing.

Sparsity enhanced reconstruction methods for diffuse optical tomography. - 105 p.

Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.

Thesis (Ph.D.)--The University of Texas at Arlington, 2013.

Conventional reconstruction algorithms for diffuse optical tomography (DOT) is based on Tikhonov regularization method and the simple white Gaussian noise (WGN) assumption. These approaches usually lead to the following problems: 1. The reconstructed images are blurry; 2. In 3D reconstruction problems, the reconstructed objects are usually in the wrong depths; 3. The shape of a reconstructed object might be different from its original one due to the noise.

ISBN: 9781303704246Subjects--Topical Terms:

1019076
Health Sciences, Radiology.

Sparsity enhanced reconstruction methods for diffuse optical tomography.
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245 1 0 $a Sparsity enhanced reconstruction methods for diffuse optical tomography.
300 $a 105 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.
500 $a Adviser: Soontorn Oraintara.
502 $a Thesis (Ph.D.)--The University of Texas at Arlington, 2013.
520 $a Conventional reconstruction algorithms for diffuse optical tomography (DOT) is based on Tikhonov regularization method and the simple white Gaussian noise (WGN) assumption. These approaches usually lead to the following problems: 1. The reconstructed images are blurry; 2. In 3D reconstruction problems, the reconstructed objects are usually in the wrong depths; 3. The shape of a reconstructed object might be different from its original one due to the noise.
520 $a In this work, after study the nature of DOT images as well as the data acquisition process, several sparsity regularization related reconstruction methods were developed to improve the spatial resolution as well as the fidelity of the reconstructed image. First, this thesis presents a simple reconstruction formula that adjusts the sensing matrix to improve the depth reconstruction in the 3D space. With the sparsity constraint, the spatial resolutions of a reconstructed image can also be improved even with WGN added to the measurements.
520 $a Next, to make the reconstruction formula more practical, the physical sensing model and its relationship with the measurement noise are further studied. Consequently, an effective noise quantification method is derived. By incorporating this noise level information to the reconstruction process, objects with more complex shapes can be recovered almost correctly.
520 $a Finally, the relative noise (RN) model is derived by considering the transformation from the light intensity measurements to relative light density changes. Thereafter, a maximum a posteriori (MAP) estimator, together with both l1 norm and l2 norm regularization terms, is developed. The resulting optimization problem is solved by the ellipsoid algorithm. The improvement of using this more accurate noise model is demonstrated by both computer simulation and phantom experiments.
590 $a School code: 2502.
650 4 $a Health Sciences, Radiology. $3 1019076
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Statistics. $3 517247
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710 2 $a The University of Texas at Arlington. $b Electrical Engineering. $3 1018571
773 0 $t Dissertation Abstracts International $g 75-05B(E).
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793 $a English
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