Assessment of Geometric Changes in ROI and its Dosimetric Consequences using Deformable Image Registration for Head and Neck Adaptive Radiation Therapy

Year 2022, Volume: 35 Issue: 2, 1 - 6, 09.03.2023

Sumeyra Can , Didem Karaçetin

Abstract

The aim of this study was to evaluate the change in volume and center of mass for a region of interest and how the changes affected cumulative dose though a Geometric Processing Unit (GPU)-based Deformable Image Registration. Ten head and neck cancer patients treated with simultaneous integrated boost on tomotherapy were retrospectively analyzed. Planning CT and 6–8 kV CT images were obtained for each case and these images were used for image registration though GPU-based dose accumulation and simulation framework to calculate accumulated dose and geometric changes for organs at risk. The cumulative dose was evaluated based on geometric changes and was compared with the planned dose. There was no statistical difference between the accumulated dose and planned dose for Dmean, V100, and V90 of PTV1 (p > 0.05). The accumulated dose was lower than the planned dose by 14.8% and 8.8% for V100 and V95 of PTV3, respectively. The cumulative dose to the medulla spinalis was higher than the planned dose by 7%; however, it was less than the planned dose by 6.6% and 4.1% for the left and right parotid glands, respectively. Weekly cumulative dose assessment is an essential quantity to determine how closely treatment planning is followed, since head and neck cancer patients undergo many anatomical changes. The GPU-based 3D image framework allows for the evaluation of real-time dose accumulation and tracking inter-fractional volume change for a region of interest. Deformable image registration is an essential tool to gain insight when adaptive radiation therapy is necessary.

Keywords

Adaptive Radiotherapy, Deformable Image Registration, Head and Neck Cancers, IGRT, Radiation Dose, Tomotherapy

Thanks

The authors are indebted to the University of California Los Angeles (UCLA) Radiation Oncology Department for their support providing head and neck cancer patients’ data treated with tomotherapy. Theoretical calculations based on GPU-based deformable image registration algorithm and data analysis would have been impossible without the kind collaboration of Daniel Low, Professor, and Vice Chair of Medical Physics at UCLA, Sharon Qui, Anand Santhanam, and John Neylon.

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