A simple and efficient methodology to improve geometric accuracy in gamma knife radiation surgery: implementation in multiple brain metastases

Purpose To propose, verify, and implement a simple and efficient methodology for the improvement of total geometric accuracy in multiple brain metastases gamma knife (GK) radiation surgery. Methods and Materials The proposed methodology exploits the directional dependence of magnetic resonance imaging (MRI)-related spatial distortions stemming from background field inhomogeneities, also known as sequence-dependent distortions, with respect to the read-gradient polarity during MRI acquisition. First, an extra MRI pulse sequence is acquired with the same imaging parameters as those used for routine patient imaging, aside from a reversal in the read-gradient polarity. Then, “average” image data are compounded from data acquired from the 2 MRI sequences and are used for treatment planning purposes. The method was applied and verified in a polymer gel phantom irradiated with multiple shots in an extended region of the GK stereotactic space. Its clinical impact in dose delivery accuracy was assessed in 15 patients with a total of 96 relatively small ( Results Phantom study results showed that use of average MR images eliminates the effect of sequence-dependent distortions, leading to a total spatial uncertainty of less than 0.3 mm, attributed mainly to gradient nonlinearities. In brain metastases patients, non-eliminated sequence-dependent distortions lead to target localization uncertainties of up to 1.3 mm (mean: 0.51 ± 0.37 mm) with respect to the corresponding target locations in the “average” MRI series. Due to these uncertainties, a considerable underdosage (5%-32% of the prescription dose) was found in 33% of the studied targets. Conclusions The proposed methodology is simple and straightforward in its implementation. Regarding multiple brain metastases applications, the suggested approach may substantially improve total GK dose delivery accuracy in smaller, outlying targets.