東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Multivariate statistical techniques ...

Malinowski, Kathleen T.

FindBook

Google Book

Amazon

博客來

Multivariate statistical techniques for accurately and noninvasively localizing tumors subject to respiration-induced motion.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Multivariate statistical techniques for accurately and noninvasively localizing tumors subject to respiration-induced motion./
作者:	Malinowski, Kathleen T.
面頁冊數:	199 p.
附註:	Source: Dissertation Abstracts International, Volume: 73-12(E), Section: B.
Contained By:	Dissertation Abstracts International73-12B(E).
標題:	Engineering, Biomedical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3517691
ISBN:	9781267481559

Multivariate statistical techniques for accurately and noninvasively localizing tumors subject to respiration-induced motion.
Malinowski, Kathleen T.

Multivariate statistical techniques for accurately and noninvasively localizing tumors subject to respiration-induced motion. - 199 p.

Source: Dissertation Abstracts International, Volume: 73-12(E), Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2012.

Tumors in the lung, liver, and pancreas can move considerably with normal respiration. The tumor motion extent, path, and baseline position change over time. This creates a complex "moving target" for external beam radiation and is a major obstacle to treating cancer. Real-time tumor motion compensation systems have emerged, but device performance is limited by tumor localization accuracy. Direct tumor tracking is not feasible for these tumors, but tumor displacement can be predicted from surrogate measurements of respiration.

ISBN: 9781267481559Subjects--Topical Terms:

1017684
Engineering, Biomedical.

Multivariate statistical techniques for accurately and noninvasively localizing tumors subject to respiration-induced motion.
LDR:03268nam a2200301 4500 001 1958203
005 20140327081049.5
008 150210s2012 ||||||||||||||||| ||eng d
020 $a 9781267481559
035 $a (MiAaPQ)AAI3517691
035 $a AAI3517691
040 $a MiAaPQ $c MiAaPQ
100 1 $a Malinowski, Kathleen T. $3 2093219
245 1 0 $a Multivariate statistical techniques for accurately and noninvasively localizing tumors subject to respiration-induced motion.
300 $a 199 p.
500 $a Source: Dissertation Abstracts International, Volume: 73-12(E), Section: B.
500 $a Advisers: Warren D. D'Souza; Yang Tao.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2012.
520 $a Tumors in the lung, liver, and pancreas can move considerably with normal respiration. The tumor motion extent, path, and baseline position change over time. This creates a complex "moving target" for external beam radiation and is a major obstacle to treating cancer. Real-time tumor motion compensation systems have emerged, but device performance is limited by tumor localization accuracy. Direct tumor tracking is not feasible for these tumors, but tumor displacement can be predicted from surrogate measurements of respiration.
520 $a In this dissertation, we have developed a series of multivariate statistical techniques for reliably and accurately localizing tumors from respiratory surrogate markers affixed to the torso surface. Our studies utilized radiographic tumor localizations measured concurrently with optically tracked respiratory surrogates during 176 lung, liver, and pancreas radiation treatment and dynamic MR imaging sessions. We identified measurement precision, tumor-surrogate correlation, training data selection, inter-patient variations, and algorithm design as factors impacting localization accuracy. Training data timing was particularly important, as tumor localization errors increased over time in 63% of 30-min treatments. This was a result of the changing relationship between surrogate signals and tumor motion. To account for these changes, we developed a method for detecting and correcting large localization errors. By monitoring the surrogate-to-surrogate and surrogate-to-model relationships, tumor localization errors exceeding 3 mm could be detected at a sensitivity of 95%. The method that we have proposed and validated in this dissertation leads to 69% fewer treatment interruptions than conventional respiratory surrogate model monitoring techniques. Finally, we extended respiratory surrogate-based tumor motion prediction to the otherwise time-consuming process of contouring respiratory-correlated computed tomography scans. This dissertation clarifies the scope and significance of problems underlying existing surrogate-based tumor localization models. Furthermore, it presents novel solutions that make it possible to improve radiation delivery to tumors without increasing the time required to plan and deliver radiation treatments.
590 $a School code: 0117.
650 4 $a Engineering, Biomedical. $3 1017684
650 4 $a Statistics. $3 517247
650 4 $a Engineering, Robotics. $3 1018454
690 $a 0541
690 $a 0463
690 $a 0771
710 2 $a University of Maryland, College Park. $b Bioengineering. $3 2093220
773 0 $t Dissertation Abstracts International $g 73-12B(E).
790 $a 0117
791 $a Ph.D.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3517691