1Department of Neurosurgery, Bergman Clinics, Amsterdam.
2Amsterdam UMC, Vrije Universiteit Amsterdam, Neurosurgery, Amsterdam Movement Sciences, Amsterdam.
Neurosurg Focus. 2021 Jan;50(1):E13. doi: 10.3171/2020.10.FOCUS20801.
Computed tomography scanning of the lumbar spine incurs a radiation dose ranging from 3.5 mSv to 19.5 mSv as well as relevant costs and is commonly necessary for spinal neuronavigation. Mitigation of the need for treatment-planning CT scans in the presence of MRI facilitated by MRI-based synthetic CT (sCT) would revolutionize navigated lumbar spine surgery. The authors aim to demonstrate, as a proof of concept, the capability of deep learning-based generation of sCT scans from MRI of the lumbar spine in 3 cases and to evaluate the potential of sCT for surgical planning.
Synthetic CT reconstructions were made using a prototype version of the "BoneMRI" software. This deep learning-based image synthesis method relies on a convolutional neural network trained on paired MRI-CT data. A specific but generally available 4-minute 3D radiofrequency-spoiled T1-weighted multiple gradient echo MRI sequence was supplemented to a 1.5T lumbar spine MRI acquisition protocol.
In the 3 presented cases, the prototype sCT method allowed voxel-wise radiodensity estimation from MRI, resulting in qualitatively adequate CT images of the lumbar spine based on visual inspection. Normal as well as pathological structures were reliably visualized. In the first case, in which a spiral CT scan was available as a control, a volume CT dose index (CTDIvol) of 12.9 mGy could thus have been avoided. Pedicle screw trajectories and screw thickness were estimable based on sCT findings.
The evaluated prototype BoneMRI method enables generation of sCT scans from MRI images with only minor changes in the acquisition protocol, with a potential to reduce workflow complexity, radiation exposure, and costs. The quality of the generated CT scans was adequate based on visual inspection and could potentially be used for surgical planning, intraoperative neuronavigation, or for diagnostic purposes in an adjunctive manner.
腰椎计算机断层扫描（CT）的辐射剂量范围为 3.5 毫西弗至 19.5 毫西弗，同时还涉及相关费用，通常是脊柱神经导航所必需的。如果能够通过基于 MRI 的合成 CT（sCT）来简化 MRI 引导下的治疗计划 CT 扫描，将彻底改变导航下的腰椎手术。作者旨在通过 3 个案例证明从腰椎 MRI 生成 sCT 扫描的深度学习方法的可行性，并评估 sCT 用于手术计划的潜力。
使用“BoneMRI”软件的原型版本进行合成 CT 重建。这种基于深度学习的图像合成方法依赖于经过配对 MRI-CT 数据训练的卷积神经网络。在 1.5T 腰椎 MRI 采集方案中补充特定但通常可用的 4 分钟 3D 射频扰相 T1 加权多梯度回波 MRI 序列。
在 3 个展示的案例中，原型 sCT 方法允许从 MRI 进行体素级别的放射密度估计，从而根据视觉检查得到质量足够的腰椎 CT 图像。可以可靠地显示正常和病理结构。在第一个案例中，作为对照，存在螺旋 CT 扫描，因此可以避免 12.9 毫戈瑞的容积 CT 剂量指数（CTDIvol）。基于 sCT 发现可以估计椎弓根螺钉轨迹和螺钉厚度。
评估的 BoneMRI 原型方法可以仅通过在采集方案中进行少量更改，从 MRI 图像生成 sCT 扫描，从而有潜力降低工作流程的复杂性、辐射暴露和成本。基于视觉检查，生成的 CT 扫描的质量足够好，并且可能用于手术计划、术中神经导航或辅助诊断。
