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High Performance Tomography.

ProQuest Information and Learning Co.

High Performance Tomography.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	High Performance Tomography./
作者:	Wang, Xiao.
面頁冊數:	1 online resource (123 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Contained By:	Dissertation Abstracts International79-03B(E).
標題:	Engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355258226

High Performance Tomography.
Wang, Xiao.

High Performance Tomography. - 1 online resource (123 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Computed Tomography (CT) Image Reconstruction is an important technique used in a wide range of applications, ranging from explosive detection, medical imaging to scientific imaging. Among available reconstruction methods, Model Based Iterative Reconstruction (MBIR) produces higher quality images and allows for the use of more general CT scanner geometries than is possible with more commonly used methods. The high computational cost of MBIR, however, often makes it impractical in applications for which it would otherwise be ideal.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355258226Subjects--Topical Terms:

561152
Engineering.
Index Terms--Genre/Form:

554714
Electronic books.

High Performance Tomography.
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035 $a AAI10607965
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Wang, Xiao. $3 1021052
245 1 0 $a High Performance Tomography.
264 0 $c 2017
300 $a 1 online resource (123 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
500 $a Advisers: Charles A. Bouman; Samuel P. Midkiff.
502 $a Thesis (Ph.D.) $c Purdue University $d 2017.
504 $a Includes bibliographical references
520 $a Computed Tomography (CT) Image Reconstruction is an important technique used in a wide range of applications, ranging from explosive detection, medical imaging to scientific imaging. Among available reconstruction methods, Model Based Iterative Reconstruction (MBIR) produces higher quality images and allows for the use of more general CT scanner geometries than is possible with more commonly used methods. The high computational cost of MBIR, however, often makes it impractical in applications for which it would otherwise be ideal.
520 $a This dissertation describes the concept of a super-voxel (SV) that significantly reduces the computational cost of MBIR while retaining its benefits. It describes how scanner data can be organized into super- voxels (SV) that, dramatically increases locality and prefetching, regularizes data access pattern, enables massive parallelism, and ensures fast convergence. Results indicate that the super-voxel algorithm has an average speedup of 9776 compared to the fastest state-of-the-art 3D image reconstruction implementation on a 69632-core distributed system.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Engineering. $3 561152
650 4 $a Computer engineering. $3 569006
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0537
690 $a 0464
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Purdue University. $b Electrical and Computer Engineering. $3 1148521
773 0 $t Dissertation Abstracts International $g 79-03B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10607965 $z click for full text (PQDT)

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