Compensation of intrafractional motion for lung stereotactic body radiotherapy (SBRT) on helical TomoTherapy

Abstract

Helical TomoTherapy has unique challenges in handling intrafractional motion compared to a conventional LINAC. In this study, we analyzed the impact of intrafractional motion on cumulative dosimetry using actual patient motion data from clinically treated patients and investigated real time jaw and multileaf collimator (MLC) compensation approaches to minimize the motion-induced dose discrepancy in clinically acceptable TomoTherapy lung SBRT treatments. Intrafractional motion traces from eight fiducial tracking CyberKnife lung tumor treatment cases were used in this study. These cases were re-planned on TomoTherapy for SBRT, with 18 Gy × 3 fractions to a planning target volume (PTV) defined on the breath-hold CT without ITV expansion. Each case was planned with four different jaw settings: 1 cm static, 2.5 cm static, 2.5 cm dynamic and 5 cm dynamic. In-house 4D dose accumulation software was used to compute the dose distributions with tumor motion and then compensate for that motion by modifying the original jaw and MLC positions to track the trajectory of the tumor. The impact of motion and effectiveness of compensation on the PTV coverage depends on the motion type and plan settings. On average, the PTV V100% (the percent volume of the PTV receiving the prescription dose) accumulated from three fractions changed from 96.6% (motion-free) to 83.1% (motion-included), 97.5% to 93.0%, 97.7% to 92.1%, and 98.1% to 93.7% for the 1 cm static jaw, 2.5 cm static jaw, 2.5 cm dynamic jaw and 5 cm dynamic jaw setting, respectively. When the jaw and MLC compensation algorithm was engaged, the PTV V100% was restored back to 92.2%, 95.9%, 96.6% and 96.4%, for the four jaw settings mentioned above respectively. TomoTherapy lung tumor SBRT treatments using a field width of 2.5 cm or larger are less sensitive to motion than treatments using a 1 cm field width. For 1 cm field width plans, PTV coverage can be greatly compromised, even over three fractions. Once the motion pattern is known, the jaw and MLC compensation algorithm can largely minimize the loss of PTV coverage induced by the motion.