Medical Physics Unit, McGill University, Montréal, QC, Canada; Biomedical Engineering, McGill University, Montréal, QC, Canada.
Medical Physics Unit, McGill University, Montréal, QC, Canada.
Phys Med. 2021 May;85:137-146. doi: 10.1016/j.ejmp.2021.05.001. Epub 2021 May 15.
Radiotherapy treatment planning based on magnetic resonance imaging (MRI) benefits from increased soft-tissue contrast and functional imaging. MRI-only planning is attractive but limited by the lack of electron density information required for dose calculation, and the difficulty to differentiate air and bone. MRI can map magnetic susceptibility to separate bone from air. A method is introduced to produce synthetic CT (sCT) through automatic voxel-wise assignment of CT numbers from an MRI dataset processed that includes magnetic susceptibility mapping.
Volumetric multi-echo gradient echo datasets were acquired in the heads of five healthy volunteers and fourteen patients with cancer using a 3 T MRI system. An algorithm for CT synthesis was designed using the volunteer data, based on fuzzy c-means clustering and adaptive thresholding of the MR data (magnitude, fat, water, and magnetic susceptibility). Susceptibility mapping was performed using a modified version of the iterative phase replacement algorithm. On patient data, the algorithm was assessed by direct comparison to X-ray computed tomography (CT) scans.
The skull, spine, teeth, and major sinuses were clearly distinguished in all sCT, from healthy volunteers and patients. The mean absolute CT number error between X-ray CT and sCT in patients ranged from 78 and 134 HU.
Susceptibility mapping using MRI can differentiate air and bone for CT synthesis. The proposed method is automated, fast, and based on a commercially available MRI pulse sequence. The method avoids registration errors and does not rely on a priori information, making it suitable for nonstandard anatomy.
基于磁共振成像（MRI）的放射治疗计划受益于软组织对比度和功能成像的提高。仅 MRI 计划具有吸引力，但受到缺乏剂量计算所需的电子密度信息以及难以区分空气和骨骼的限制。MRI 可以映射磁化率以将骨骼与空气分离。引入了一种通过自动将 CT 编号分配给处理后的 MRI 数据集来生成合成 CT（sCT）的方法，该数据集包括磁化率映射。
使用 3T MRI 系统在五名健康志愿者和十四名癌症患者的头部采集容积多回波梯度回波数据集。使用志愿者数据设计了一种 CT 合成算法，该算法基于模糊 c-均值聚类和对 MR 数据（幅度、脂肪、水和磁化率）的自适应阈值处理。使用迭代相位替换算法的修改版本进行磁化率映射。在患者数据上，通过与 X 射线计算机断层扫描（CT）扫描的直接比较来评估该算法。
在所有 sCT 中，都可以清楚地区分来自健康志愿者和患者的颅骨、脊柱、牙齿和主要窦腔。患者 X 射线 CT 和 sCT 之间的平均绝对 CT 数误差范围为 78 至 134 HU。
使用 MRI 进行磁化率映射可区分空气和骨骼以进行 CT 合成。所提出的方法是自动化的、快速的，并且基于商业可用的 MRI 脉冲序列。该方法避免了配准误差，并且不依赖于先验信息，因此适用于非标准解剖结构。
