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MRI-guided PET image reconstruction ...

Hutchcroft, Will Adrian.

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MRI-guided PET image reconstruction by the Kernel Method.

Record Type:	Electronic resources : Monograph/item
Title/Author:	MRI-guided PET image reconstruction by the Kernel Method./
Author:	Hutchcroft, Will Adrian.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	54 p.
Notes:	Source: Masters Abstracts International, Volume: 56-02.
Contained By:	Masters Abstracts International56-02(E).
Subject:	Medical imaging. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10165769
ISBN:	9781369201451

MRI-guided PET image reconstruction by the Kernel Method.
Hutchcroft, Will Adrian.

MRI-guided PET image reconstruction by the Kernel Method. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 54 p.

Source: Masters Abstracts International, Volume: 56-02.

Thesis (M.S.)--University of California, Davis, 2016.

This thesis extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a maximum a posteriori (MAP) framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest (ROI) quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization (EM) algorithm.

ISBN: 9781369201451Subjects--Topical Terms:

3172799
Medical imaging.

MRI-guided PET image reconstruction by the Kernel Method.
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100 1 $a Hutchcroft, Will Adrian. $3 3286104
245 1 0 $a MRI-guided PET image reconstruction by the Kernel Method.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 54 p.
500 $a Source: Masters Abstracts International, Volume: 56-02.
500 $a Adviser: Jinyi Qi.
502 $a Thesis (M.S.)--University of California, Davis, 2016.
520 $a This thesis extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a maximum a posteriori (MAP) framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest (ROI) quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization (EM) algorithm.
590 $a School code: 0029.
650 4 $a Medical imaging. $3 3172799
690 $a 0574
710 2 $a University of California, Davis. $b Biomedical Engineering. $3 1035546
773 0 $t Masters Abstracts International $g 56-02(E).
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10165769