Abstract
INTRODUCTION: The accuracy of stereotactic treatment planning is primarily limited by the least accurate process in the whole chain of events, and is particularly important in cranial radiosurgery. Ameliorating this process can improve treatment targeting, providing additional reliability for these indications. Quality assurance (QA) in radiotherapy is often performed on the dose delivery and planning section rather than the localization. Magnetic Resonance Images (MRI) are notably subject to distortions, due to the nonlinearity of gradient fields, potentially source of geometric errors. This study aimed to analyze the impact of a patient-specific algorithm, rather than manufacturer-specific, to correct spatial distortion in cranial MRI by using a novel software-only paradigm. MATERIAL AND METHODS: An unbiased simulated T1-Weighted MRI validated dataset is utilized to create a synthetic CT (sCT). By introducing controlled distortion in simulated datasets, we can evaluate the influence of noise and intensity non-uniformity ("RF") ranging from 0 to 9% noise and 0 to 40% RF. These MRIs were corrected using the sCT as base modality for distortion correction. To evaluate the impact of the distortion correction, each corrected/non-corrected image set was compared to the unbiased MRI using Root-mean-square-error (RMSE) as a full-image reference comparison metric. RESULTS: The distortion correction allows for an improvement based on the RMSE correlation between baseline and distorted MRIs. The amelioration of average RMSE in corrected versus non-corrected MRI is up to 42.22% for the most distorted datasets. CONCLUSION: The distortion correction results show a proportional improvement with increased noise and intensity non-uniformity. This provides additional robustness and reliability to the accuracy of SRS treatment planning using MR T1-W sequences as imaging reference for target definition and organ delineation, remaining consistent independently from the variability of the non-uniformity gradient values. This virtual phantom methodology primarily aims to provide a simple/robust evaluation metric in radiotherapy for MR distortion correction solutions, providing an additional/complement QA procedure to dedicated hardware phantoms, comparatively costly in time and resources. This approach is also designed to assist with an easily implementable secondary QA for validation during commissioning of distortion correction software, focusing on this feature, to better isolate and identify sources of geometric errors resulting from MR distortions.