SU-HH-AUD C-08: Spatial Weighted Mutual Information for Image Registration in Image Guided Radiation Therapy

2008 ◽  
Vol 35 (6Part18) ◽  
pp. 2855-2855 ◽  
Author(s):  
SB Park ◽  
FC Rhee ◽  
JI Monroe ◽  
JW Sohn
Keyword(s):  
Radiation Therapy ◽  
Image Registration ◽  
Mutual Information ◽  
Image Guided Radiation Therapy ◽  
Image Guided

Spatially weighted mutual information image registration for image guided radiation therapy

2010 ◽  
Vol 37 (9) ◽  
pp. 4590-4601 ◽  
Author(s):  
Samuel B. Park ◽  
Frank C. Rhee ◽  
James I. Monroe ◽  
Jason W. Sohn
Keyword(s):  
Radiation Therapy ◽  
Image Registration ◽  
Mutual Information ◽  
Image Guided Radiation Therapy ◽  
Image Guided ◽  
Information Image

SU-E-T-466: A New Evaluation Method of Deformable Image Registration Algorithms for Image-Guided Radiation Therapy

2013 ◽  
Vol 40 (6Part18) ◽  
pp. 312-312
Author(s):  
Y Saito ◽  
K Tateoka ◽  
A Nakata ◽  
T Nakazawa ◽  
T Abe ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Image Registration ◽  
Evaluation Method ◽  
Deformable Image Registration ◽  
Image Guided Radiation Therapy ◽  
Image Guided

Sensitivity of plan re-optimizaton to errors in deformable image registration in online adaptive image-guided radiation therapy

2016 ◽  
Author(s):  
◽  
Brian Douglas McClain
Keyword(s):  
Radiation Therapy ◽  
Image Registration ◽  
Treatment Planning ◽  
Planning Process ◽  
Treatment Plan ◽  
Dose Calculation ◽  
Deformable Image Registration ◽  
University Of Missouri ◽  
Image Guided Radiation Therapy ◽  
Image Guided

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Online adaptive image-guided radiation therapy has been a longstanding topic of interest in the field of radiation oncology due to its unique ability to tailor a dose distribution to account for inter-fractional variations and motion of critical structuresthrough daily online re-planning. Efforts are now being made to optimize steps of the adaptive process so that treatment planning and dose delivery can be practically administered while the patient is on the treatment couch. Automated image deformation and segmentation algorithms, along with fast dose calculation and plan re-optimization, have been implemented to streamline the online adaptive treatment planning process. Due to the complexity of inter-fractional anatomical deformations, obtaining precise delineation of target and structure volumes through deformable image registration (DIR) and auto-segmentation is a challenge. Mapping accurate organ at risk (OAR) contours through DIR and auto-segmentation is especially challenging for abdomen and pelvis treatment sites known to have significant interfractionaanatomical variations. While others have studied the accuracy of auto-deformed contours and potential errors and risk factors in the adaptive radiotherapy (ART) process, this study aims to determine if accounting for these errors within specific regions of interest (ROIs) can produce a comparable treatment plan without compromising PTV coverage, OAR sparing or overall plan quality. Once the correlation between dosimetric differences and geometric errors has been identified, a system will be developed to guide the physician in focusing their contour edits to the locations that matter most to the non-deterministic optimization algorithm.

Sign in / Sign up

Export Citation Format

Share Document