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, Lau Angus Z, Stanisz Greg J, Chugh Brige P
Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.
Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Med Phys. 2022 Nov;49(11):7071-7084. doi: 10.1002/mp.15851. Epub 2022 Jul 26.
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.
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-T -weighted Dixon, T -weighted fast field echo (FFE), and T -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.
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 T -weighted Dixon, 0.42 mm (LR), 0.23 mm (AP), and 0.08 mm (SI) between the CT and T -weighted FFE, and 0.45 mm (LR), 0.19 mm (AP), and 1.04 mm (SI) between the CT and T -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.
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.
在使用伽玛刀进行立体定向放射外科治疗(SRS)时,靶点定位越来越依赖于计划磁共振成像(MRI)与立体定向锥形束计算机断层扫描(CBCT)之间的配准。在考虑用于靶点勾画的新型功能和代谢MRI脉冲序列出现时,验证不同模态之间的图像配准将特别有益。本研究旨在开发一种基于体模的方法,以定量比较标准临床成像方案与可能配准失败的代表性MRI序列的配准准确性。本研究中提出的比较方法可作为评估新型MRI序列临床可转化性的有用工具。
设计并制造了一个带有基准标记柱的逼真人体头骨体模,以适配典型的MRI头部线圈和伽玛刀患者定位系统。采集了一系列“优化的”三维MRI序列——T加权迪克森序列、T加权快速场回波(FFE)序列和T加权液体衰减反转恢复(FLAIR)序列,并与CBCT进行配准。通过在不进行几何失真校正的情况下重建以及以较低信噪比(SNR)重新采集,使相同序列“受损”,以模拟配准准确性较差的新型MRI序列。使用图像相似性指标——结构相似性(SSIM)指数、均方误差(MSE)和峰值SNR(PSNR)——来定量比较优化后的和受损的MR图像的配准情况。
将实际的基准位置与从每个优化图像体积测量得到的位置进行比较,结果显示CT与CBCT之间的最大中位数几何不确定性为0.39毫米(左右方向)、0.92毫米(前后方向)和0.13毫米(上下方向),CT与T加权迪克森序列之间为0.60毫米(左右方向)、0.36毫米(前后方向)和0.07毫米(上下方向),CT与T加权FFE序列之间为0.42毫米(左右方向)、0.23毫米(前后方向)和0.08毫米(上下方向),CT与T加权FLAIR序列之间为0.45毫米(左右方向)、0.19毫米(前后方向)和1.04毫米(上下方向)。定性地,使用融合图像比较优化后的和受损的图像切片对,其中使用可分离的颜色来区分图像。定量地,MSE是评估配准相似性最具预测性的指标,SSIM是第二具预测性的指标。可以定义MSE、SSIM和/或PSNR的临床相关阈值,超过该阈值,基于与优化序列配准的差异,应拒绝将MRI序列用于靶点勾画。使用与面内几何不确定性的相关系数计算的所有差异阈值都需要逐序列定义并用患者数据进行验证。
本研究利用逼真的头骨体模和图像相似性指标开发了一种方法,能够定量评估基于现代研究的MRI序列与临床可接受方案相比,是否能够以相同或更低的准确性与伽玛刀CBCT进行配准。