TY - GEN
T1 - Accurate tracking of tumor volume change during radiotherapy by CT-CBCT registration with intensity correction
AU - Park, Seyoun
AU - Robinson, Adam
AU - Quon, Harry
AU - Kiess, Ana P.
AU - Shen, Colette
AU - Wong, John
AU - Plishker, William
AU - Shekhar, Raj
AU - Lee, Junghoon
N1 - Publisher Copyright:
© 2016 SPIE.
PY - 2016
Y1 - 2016
N2 - In this paper, we propose a CT-CBCT registration method to accurately predict the tumor volume change based on daily cone-beam CTs (CBCTs) during radiotherapy. CBCT is commonly used to reduce patient setup error during radiotherapy, but its poor image quality impedes accurate monitoring of anatomical changes. Although physician's contours drawn on the planning CT can be automatically propagated to daily CBCTs by deformable image registration (DIR), artifacts in CBCT often cause undesirable errors. To improve the accuracy of the registration-based segmentation, we developed a DIR method that iteratively corrects CBCT intensities by local histogram matching. Three popular DIR algorithms (B-spline, demons, and optical flow) with the intensity correction were implemented on a graphics processing unit for efficient computation. We evaluated their performances on six head and neck (HN) cancer cases. For each case, four trained scientists manually contoured the nodal gross tumor volume (GTV) on the planning CT and every other fraction CBCTs to which the propagated GTV contours by DIR were compared. The performance was also compared with commercial image registration software based on conventional mutual information (MI), VelocityAI (Varian Medical Systems Inc.). The volume differences (mean±std in cc) between the average of the manual segmentations and automatic segmentations are 3.70±2.30 (B-spline), 1.25±1.78 (demons), 0.93±1.14 (optical flow), and 4.39±3.86 (VelocityAI). The proposed method significantly reduced the estimation error by 9% (B-spline), 38% (demons), and 51% (optical flow) over the results using VelocityAI. Although demonstrated only on HN nodal GTVs, the results imply that the proposed method can produce improved segmentation of other critical structures over conventional methods.
AB - In this paper, we propose a CT-CBCT registration method to accurately predict the tumor volume change based on daily cone-beam CTs (CBCTs) during radiotherapy. CBCT is commonly used to reduce patient setup error during radiotherapy, but its poor image quality impedes accurate monitoring of anatomical changes. Although physician's contours drawn on the planning CT can be automatically propagated to daily CBCTs by deformable image registration (DIR), artifacts in CBCT often cause undesirable errors. To improve the accuracy of the registration-based segmentation, we developed a DIR method that iteratively corrects CBCT intensities by local histogram matching. Three popular DIR algorithms (B-spline, demons, and optical flow) with the intensity correction were implemented on a graphics processing unit for efficient computation. We evaluated their performances on six head and neck (HN) cancer cases. For each case, four trained scientists manually contoured the nodal gross tumor volume (GTV) on the planning CT and every other fraction CBCTs to which the propagated GTV contours by DIR were compared. The performance was also compared with commercial image registration software based on conventional mutual information (MI), VelocityAI (Varian Medical Systems Inc.). The volume differences (mean±std in cc) between the average of the manual segmentations and automatic segmentations are 3.70±2.30 (B-spline), 1.25±1.78 (demons), 0.93±1.14 (optical flow), and 4.39±3.86 (VelocityAI). The proposed method significantly reduced the estimation error by 9% (B-spline), 38% (demons), and 51% (optical flow) over the results using VelocityAI. Although demonstrated only on HN nodal GTVs, the results imply that the proposed method can produce improved segmentation of other critical structures over conventional methods.
KW - CBCT
KW - Tumor volume tracking
KW - deformable registration
UR - http://www.scopus.com/inward/record.url?scp=84982102798&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84982102798&partnerID=8YFLogxK
U2 - 10.1117/12.2217047
DO - 10.1117/12.2217047
M3 - Conference contribution
AN - SCOPUS:84982102798
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2016
A2 - Webster, Robert J.
A2 - Yaniv, Ziv R.
PB - SPIE
T2 - Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling
Y2 - 28 February 2016 through 1 March 2016
ER -