Fan Xiaoyao, Roberts David W, Ji Songbai, Hartov Alex, Paulsen Keith D
Thayer School of Engineering and.
Geisel School of Medicine, Dartmouth College, Hanover; and.
J Neurosurg. 2015 Sep;123(3):721-31. doi: 10.3171/2014.12.JNS141321. Epub 2015 Jul 3.
Fiducial-based registration (FBR) is used widely for patient registration in image-guided neurosurgery. The authors of this study have developed an automatic fiducial-less registration (FLR) technique to find the patient-to-image transformation by directly registering 3D ultrasound (3DUS) with MR images without incorporating prior information. The purpose of the study was to evaluate the performance of the FLR technique when used prospectively in the operating room and to compare it with conventional FBR.
In 32 surgical patients who underwent conventional FBR, preoperative T1-weighted MR images (pMR) with attached fiducial markers were acquired prior to surgery. After craniotomy but before dural opening, a set of 3DUS images of the brain volume was acquired. A 2-step registration process was executed immediately after image acquisition: 1) the cortical surfaces from pMR and 3DUS were segmented, and a multistart sum-of-squared-intensity-difference registration was executed to find an initial alignment between down-sampled binary pMR and 3DUS volumes; and 2) the alignment was further refined by a mutual information-based registration between full-resolution grayscale pMR and 3DUS images, and a patient-to-image transformation was subsequently extracted.
To assess the accuracy of the FLR technique, the following were quantified: 1) the fiducial distance error (FDE); and 2) the target registration error (TRE) at anterior commissure and posterior commissure locations; these were compared with conventional FBR. The results showed that although the average FDE (6.42 ± 2.05 mm) was higher than the fiducial registration error (FRE) from FBR (3.42 ± 1.37 mm), the overall TRE of FLR (2.51 ± 0.93 mm) was lower than that of FBR (5.48 ± 1.81 mm). The results agreed with the intent of the 2 registration techniques: FBR is designed to minimize the FRE, whereas FLR is designed to optimize feature alignment and hence minimize TRE. The overall computational cost of FLR was approximately 4-5 minutes and minimal user interaction was required.
Because the FLR method directly registers 3DUS with MR by matching internal image features, it proved to be more accurate than FBR in terms of TRE in the 32 patients evaluated in this study. The overall efficiency of FLR in terms of the time and personnel involved is also improved relative to FBR in the operating room, and the method does not require additional image scans immediately prior to surgery. The performance of FLR and these results suggest potential for broad clinical application.
基于基准点的配准（FBR）在图像引导神经外科手术中广泛用于患者配准。本研究的作者开发了一种自动无基准点配准（FLR）技术，通过直接将三维超声（3DUS）与磁共振图像（MR）配准来找到患者与图像的变换，而无需纳入先验信息。本研究的目的是前瞻性评估FLR技术在手术室中的性能，并将其与传统FBR进行比较。
在32例行传统FBR的手术患者中，术前在手术前获取带有基准标记的T1加权MR图像（pMR）。开颅术后但在硬脑膜打开前，采集一组脑容积的3DUS图像。图像采集后立即执行两步配准过程：1）对pMR和3DUS的皮质表面进行分割，并执行多起点强度平方差配准以找到下采样后的二值pMR和3DUS容积之间的初始对齐；2）通过全分辨率灰度pMR和3DUS图像之间基于互信息的配准进一步优化对齐，随后提取患者与图像的变换。
为评估FLR技术的准确性，对以下指标进行了量化：1）基准点距离误差（FDE）；2）在前连合和后连合位置的目标配准误差（TRE）；并将这些指标与传统FBR进行比较。结果显示，虽然平均FDE（6.42±2.05mm）高于FBR的基准点配准误差（FRE）（3.42±1.37mm），但FLR的总体TRE（2.51±0.93mm）低于FBR（5.48±1.81mm）。结果与两种配准技术的意图相符：FBR旨在最小化FRE，而FLR旨在优化特征对齐并因此最小化TRE。FLR的总体计算成本约为4 - 5分钟，且所需的用户交互最少。
由于FLR方法通过匹配内部图像特征直接将3DUS与MR配准，在本研究评估的32例患者中，就TRE而言，它比FBR更准确。相对于手术室中的FBR，FLR在时间和人力方面的总体效率也有所提高，并且该方法在手术前不需要额外的图像扫描。FLR的性能及这些结果表明其具有广泛临床应用的潜力。
