Neumann Jan-Oliver, Giese Henrik, Biller Armin, Nagel Armin M, Kiening Karl
Division of Stereotactic Neurosurgery, Department of Neurosurgery, University of Heidelberg, Heidelberg, Germany.
Stereotact Funct Neurosurg. 2015;93(6):380-6. doi: 10.1159/000441233. Epub 2015 Dec 16.
Magnetic resonance imaging (MRI) is replacing computed tomography (CT) as the main imaging modality for stereotactic transformations. MRI is prone to spatial distortion artifacts, which can lead to inaccuracy in stereotactic procedures.
Modern MRI systems provide distortion correction algorithms that may ameliorate this problem. This study investigates the different options of distortion correction using standard 1.5-, 3- and 7-tesla MRI scanners.
A phantom was mounted on a stereotactic frame. One CT scan and three MRI scans were performed. At all three field strengths, two 3-dimensional sequences, volumetric interpolated breath-hold examination (VIBE) and magnetization-prepared rapid acquisition with gradient echo, were acquired, and automatic distortion correction was performed. Global stereotactic transformation of all 13 datasets was performed and two stereotactic planning workflows (MRI only vs. CT/MR image fusion) were subsequently analysed.
Distortion correction on the 1.5- and 3-tesla scanners caused a considerable reduction in positional error. The effect was more pronounced when using the VIBE sequences. By using co-registration (CT/MR image fusion), even a lower positional error could be obtained. In ultra-high-field (7 T) MR imaging, distortion correction introduced even higher errors. However, the accuracy of non-corrected 7-tesla sequences was comparable to CT/MR image fusion 3-tesla imaging.
MRI distortion correction algorithms can reduce positional errors by up to 60%. For stereotactic applications of utmost precision, we recommend a co-registration to an additional CT dataset.
磁共振成像(MRI)正取代计算机断层扫描(CT)成为立体定向变换的主要成像方式。MRI容易出现空间失真伪影,这可能导致立体定向手术不准确。
现代MRI系统提供的失真校正算法可能会改善这一问题。本研究调查了使用标准1.5特斯拉、3特斯拉和7特斯拉MRI扫描仪进行失真校正的不同选项。
将一个体模安装在立体定向框架上。进行一次CT扫描和三次MRI扫描。在所有三个场强下,采集两个三维序列,即容积内插屏气检查(VIBE)和磁化准备快速梯度回波采集,并进行自动失真校正。对所有13个数据集进行全局立体定向变换,随后分析两种立体定向规划工作流程(仅MRI与CT/MR图像融合)。
1.5特斯拉和3特斯拉扫描仪上的失真校正使位置误差大幅降低。使用VIBE序列时效果更明显。通过使用配准(CT/MR图像融合),甚至可以获得更低的位置误差。在超高场(7T)MR成像中,失真校正引入了更高的误差。然而,未校正的7特斯拉序列的准确性与CT/MR图像融合的3特斯拉成像相当。
MRI失真校正算法可将位置误差降低多达60%。对于极高精度的立体定向应用,我们建议与额外的CT数据集进行配准。