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Shape-correlated Statistical Modelin...

Liu, Xiaoxiao.

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Shape-correlated Statistical Modeling and Analysis for Respiratory Motion Estimation.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Shape-correlated Statistical Modeling and Analysis for Respiratory Motion Estimation./
Author:	Liu, Xiaoxiao.
Description:	118 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-08, Section: B, page: .
Contained By:	Dissertation Abstracts International72-08B.
Subject:	Physics, Radiation. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3456282
ISBN:	9781124656250

Shape-correlated Statistical Modeling and Analysis for Respiratory Motion Estimation.
Liu, Xiaoxiao.

Shape-correlated Statistical Modeling and Analysis for Respiratory Motion Estimation. - 118 p.

Source: Dissertation Abstracts International, Volume: 72-08, Section: B, page: .

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2010.

Respiratory motion challenges image-guided radiation therapy (IGRT) with location uncertainties of important anatomical structures in the thorax. Eective and accurate respiration estimation is crucial to account for the motion eects on the radiation dose to tumors and organs at risk. Moreover, serious image artifacts present in treatment-guidance images such 4D cone-beam CT cause diculties in identifying spatial variations. Commonly used non-linear dense image matching methods easily fail in regions where artifacts interfere.

ISBN: 9781124656250Subjects--Topical Terms:

1019212
Physics, Radiation.

Shape-correlated Statistical Modeling and Analysis for Respiratory Motion Estimation.
LDR:03154nam 2200373 4500 001 1403736
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008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124656250
035 $a (UMI)AAI3456282
035 $a AAI3456282
040 $a UMI $c UMI
100 1 $a Liu, Xiaoxiao. $3 1683010
245 1 0 $a Shape-correlated Statistical Modeling and Analysis for Respiratory Motion Estimation.
300 $a 118 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-08, Section: B, page: .
500 $a Adviser: Stephen M. Pizer.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2010.
520 $a Respiratory motion challenges image-guided radiation therapy (IGRT) with location uncertainties of important anatomical structures in the thorax. Eective and accurate respiration estimation is crucial to account for the motion eects on the radiation dose to tumors and organs at risk. Moreover, serious image artifacts present in treatment-guidance images such 4D cone-beam CT cause diculties in identifying spatial variations. Commonly used non-linear dense image matching methods easily fail in regions where artifacts interfere.
520 $a Learning-based linear motion modeling techniques have the advantage of incorporating prior knowledge for robust motion estimation. In this research shape-correlation deformation statistics (SCDS) capture strong correlations between the shape of the lung and the dense deformation field under breathing. Dimension reduction and linear regression techniques are used to extract the correlation statistics. Based on the assumption that the deformation correlations are consistent between planning and treatment time, patient-specic SCDS trained from a 4D planning image sequence is used to predict the respiratory motion in the patient's artifact-laden 4D treatment image sequence.
520 $a Furthermore, a prediction-driven atlas formation method is developed to weaken the consistency assumption, by integrating intensity information from the target images and the SCDS predictions into a common optimization framework. The strategy of balancing between the prediction constraints and the intensity-matching forces makes the method less sensitive to variation in the correlation and utilizes intensity information besides the lung boundaries. This strategy thus provides improved motion estimation accuracy and robustness.
520 $a The SCDS-based methods are shown to be eective in modeling and estimating respiratory motion in lung, with evaluations and comparisons carried out on both simulated images and patient images.
590 $a School code: 0153.
650 4 $a Physics, Radiation. $3 1019212
650 4 $a Computer Science. $3 626642
690 $a 0756
690 $a 0984
710 2 $a The University of North Carolina at Chapel Hill. $b Computer Science. $3 1020590
773 0 $t Dissertation Abstracts International $g 72-08B.
790 1 0 $a Pizer, Stephen M., $e advisor
790 1 0 $a Niethammer, Marc $e committee member
790 1 0 $a Marron, J.S. $e committee member
790 1 0 $a Davis, Bradley C. $e committee member
790 1 0 $a Shen, Dinggang $e committee member
790 1 0 $a Lalush, David S. $e committee member
790 $a 0153
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3456282