SU-E-T-620: Effect of Different Motion Management Strategies in Liver Cancer Using Four-Dimesional Computed Tomography Image and Active Breathing Control Technology

2011 ◽  
Vol 38 (6Part20) ◽  
pp. 3632-3632
Author(s):  
C Ma ◽  
Y Yin ◽  
J Lu ◽  
T Liu ◽  
G Gong
Keyword(s):  
Computed Tomography ◽  
Liver Cancer ◽  
Management Strategies ◽  
Control Technology ◽  
Motion Management ◽  
Breathing Control ◽  
Tomography Image ◽  
Active Breathing Control ◽  
Computed Tomography Image

A Tumor Segmentation Method of Improved Chan-Vese Model for Liver Cancer Ablation Computed Tomography Image

2017 ◽  
Vol 54 (2) ◽  
pp. 021702
Author(s):  
谢志南 Xie Zhinan ◽  
郑东 Zheng Dong ◽  
陈嘉耀 Chen Jiayao ◽  
洪国斌 Hong Guobin
Keyword(s):  
Computed Tomography ◽  
Liver Cancer ◽  
Tumor Segmentation ◽  
Segmentation Method ◽  
Tomography Image ◽  
Computed Tomography Image ◽  
Cancer Ablation

A Geometric Model of the Beating Heart

2007 ◽  
Vol 46 (03) ◽  
pp. 282-286 ◽  
Author(s):  
C. Lorenz ◽  
J. von Berg
Keyword(s):  
Computed Tomography ◽  
A Priori ◽  
Anatomical Landmarks ◽  
Motion Model ◽  
A Priori Information ◽  
Average Accuracy ◽  
Tomography Image ◽  
The Mean ◽  
Priori Information ◽  
Computed Tomography Image

Summary Objectives : A comprehensive model of the human heart that covers multiple surfaces, like those of the four chambers and the attached vessels, is presented. It also contains the coronary arteries and a set of 25 anatomical landmarks. The statistical model is intended to provide a priori information for automated diagnostic and interventional procedures. Methods : The end-diastolic phase of the model was adapted to fit 27 clinical multi-slice computed tomography images, thus reflecting the anatomical variability to be observed in that sample. A mean cardiac motion model was also calculated from a set of eleven multi-phase computed tomography image sets. A number of experiments were performed to determine the accuracy of model-based predictions done on unseen cardiac images. Results : Using an additional deformable surface technique, the model allows for determination of all chambers and the attached vessels on the basis of given anatomical landmarks with an average accuracy of 1.1 mm. After such an individualization of the model by surface adaptation the centerlines of the three main coronary arteries may be estimated with an average accuracy of 5.2 mm. The mean motion model was used to estimate the cardiac phase of an unknown multislice computed tomography image. Conclusion : The mean shape model of the human heart as presented here complements automated image analysis methods with the required a priori information about anatomical constraints to make them work fast and robustly.

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