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Datasets for Adaptive Radiotherapy Simulations in Lung Cancer
Using Principal Component Analysis (PCA)  C Dial  C Dial*, G
Hugo, J Siebers, Virginia Commonwealth University, Richmond, VA
SU-E-J-208 Sunday 3:00PM - 6:00PM Room: Exhibit Hall  Purpose:
To evaluate feasibility of PCA to produce realistic lung cancer
datasets that demonstrate clinically relevant trends of tumor
regression for simulating adaptive radiotherapy treatments.
Methods: For four non small cell lung cancer patients, weekly
helical CTs were acquired during the course of radiotherapy
under active breathing control (ABC) and structures of interest
were delineated by a qualified physician. Following a rigid
alignment, week one images were registered to each of the
remaining weeks using the ITK Demons algorithm. Resulting
displacement vector fields (DVF) were reshaped into a composite
matrix for PCA, with each row consisting of weekly displacements
for homologous points. Intermediate and