Title
Prediction of 3D internal organ position from skin surface motion: results from electromagnetic tracking studies
Abstract
An effective treatment method for organs that move with respiration (such as the lungs, pancreas, and liver) is a major goal of radiation medicine. In order to treat such tumors, we need (1) real-time knowledge of the current location of the tumor, and (2) the ability to adapt the radiation delivery system to follow this constantly changing location. In this study, we used electromagnetic tracking in a swine model to address the first challenge, and to determine if movement of a marker attached to the skin could accurately predict movement of an internal marker embedded in an organ. Under approved animal research protocols, an electromagnetically tracked needle was inserted into a swine liver and an electromagnetically tracked guidewire was taped to the abdominal skin of the animal. The Aurora (Northern Digital Inc., Waterloo, Canada) electromagnetic tracking system was then used to monitor the position of both of these sensors every 40 msec. Position readouts from the sensors were then tested to see if any of the movements showed correlation. The strongest correlations were observed between external anterior-posterior motion and internal inferior-superior motion. with many other axes exhibiting only weak correlation. We also used these data to build a predictive model of internal motion by taking segments from the data and using them to derive a general functional relationship between the internal needle and the external guidewire. For the axis with the strongest correlation, this model enabled us to predict internal organ motion to within 1. mm.
Year
DOI
Venue
2005
10.1117/12.596843
PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE)
Keywords
Field
DocType
respiratory motion,radiotherapy,radiosurgery,image guidance,electromagnetic tracking
Biomedical engineering,Electromagnetic tracking,Abdominal skin,Respiratory motion,Organ Motion,Computer science,Delivery system
Conference
Volume
ISSN
Citations 
5744
0277-786X
2
PageRank 
References 
Authors
0.42
0
5
Name
Order
Citations
PageRank
Kenneth H. Wong153.98
jonathan tang2152.72
hui j zhang320.42
e varghese420.42
Kevin Cleary534356.78