Data Driven Cone Beam CT Motion Management for Radiotherapy Application
- Resource Type
- Conference
- Authors
- Akintonde, Adeyemi; McClelland, Jamie; Grimes, Helen; Moinuddin, Syed; Sharma, Ricky A.; Rit, Simon.; Thielemans, Kris.
- Source
- 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2017 IEEE. :1-4 Oct, 2017
- Subject
- Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
Nuclear Engineering
Photonics and Electrooptics
Principal component analysis
Tumors
Computed tomography
Image reconstruction
Biomedical imaging
Detectors
Physics
- Language
- ISSN
- 2577-0829
The ability to identify respiratory motion is crucial during radiation therapy treatment. In our study we introduced a novel data driven method based on principal component analysis (PCA) to extract a signal related to respiratory motion from cone beam CT projection data. Projection data acquired on cone beam CT devices normally has two motion component information within it, (1) respiratory induced motion and (2) detector rotational induced motion. Our novel approach for extracting a respiratory induced motion signal from projection data was based on computing PCA for different sections of the data set independently, and introducing a technique of combining the extracted signal from each section in a manner to represent the respiratory signal from the entire data set. We tested our method using simulation data set from XCAT software and a real patient data set. The respiratory signal extracted with the XCAT simulation yielded comparable result when compared to the ground truth respiratory signal. Initial results for the real patient data set are encouraging but show need for further refinements.