Skull phantom‐based methodology to validate MRI co‐registration accuracy for Gamma Knife radiosurgery. Issue 11 (26th July 2022) - Oglesby, Ryan T.; Lam, Wilfred W.; Ruschin, Mark; Holden, Lori; Sarfehnia, Arman; Yeboah, Collins; Sahgal, Arjun; Soliman, Hany; Detsky, Jay; Tseng, Chia‐Lin; Myrehaug, Sten; Husain, Zain; et al.
Abstract: Purpose: Target localization, for stereotactic radiosurgery (SRS) treatment with Gamma Knife, has become increasingly reliant on the co‐registration between the planning MRI and the stereotactic cone‐beam computed tomography (CBCT). Validating image registration between modalities would be particularly beneficial when considering the emergence of novel functional and metabolic MRI pulse sequences for target delineation. This study aimed to develop a phantom‐based methodology to quantitatively compare the co‐registration accuracy of the standard clinical imaging protocol to a representative MRI sequence that was likely to fail co‐registration. The comparative methodology presented in this study may serve as a useful tool to evaluate the clinical translatability of novel MRI sequences. Methods: A realistic human skull phantom with fiducial marker columns was designed and manufactured to fit into a typical MRI head coil and the Gamma Knife patient positioning system. A series of "optimized" 3D MRI sequences—T1 ‐weighted Dixon, T1 ‐weighted fast field echo (FFE), and T2 ‐weighted fluid‐attenuated inversion recovery (FLAIR)—were acquired and co‐registered to the CBCT. The same sequences were "compromised" by reconstructing without geometric distortion correction and re‐collecting with lower signal‐to‐noise‐ratio (SNR) to simulate a novel MRI sequence with poor co‐registration accuracy. Image similarity metrics—structural similarity (SSIM) index, mean squared error (MSE), and peak SNR (PSNR)—were used to quantitatively compare the co‐registration of the optimized and compromised MR images. Results: The ground truth fiducial positions were compared to positions measured from each optimized image volume revealing a maximum median geometric uncertainty of 0.39 mm (LR), 0.92 mm (AP), and 0.13 mm (SI) between the CT and CBCT, 0.60 mm (LR), 0.36 mm (AP), and 0.07 mm (SI) between the CT and T1 ‐weighted Dixon, 0.42 mm (LR), 0.23 mm (AP), and 0.08 mm (SI) between the CT and T1 ‐weighted FFE, and 0.45 mm (LR), 0.19 mm (AP), and 1.04 mm (SI) between the CT and T2 ‐weighted FLAIR. Qualitatively, pairs of optimized and compromised image slices were compared using a fusion image where separable colors were used to differentiate between images. Quantitatively, MSE was the most predictive and SSIM the second most predictive metric for evaluating co‐registration similarity. A clinically relevant threshold of MSE, SSIM, and/or PSNR may be defined beyond which point an MRI sequence should be rejected for target delineation based on its dissimilarity to an optimized sequence co‐registration. All dissimilarity thresholds calculated using correlation coefficients with in‐plane geometric uncertainty would need to be defined on a sequence‐by‐sequence basis and validated with patient data. Conclusion: This study utilized a realistic skull phantom and image similarity metrics to develop a methodology capable of quantitatively assessing whether a modern research‐based MRI sequence can be co‐registered to the Gamma Knife CBCT with equal or less than equal accuracy when compared to a clinically accepted protocol.