IEEE Trans Biomed Eng. 2020 Dec;67(12):3542-3552. doi: 10.1109/TBME.2020.2990669. Epub 2020 Nov 19.
The efficacy of deep brain stimulation (DBS) depends on accurate placement of electrodes. Although stereotactic frames enable co-registration of image-based surgical planning and the operative field, the accuracy of electrode placement can be degraded by intra-operative brain shift. In this study, we adapted a biomechanical model to estimate whole brain displacements from which we deformed preoperative CT (preCT) to generate an updated CT (uCT) that compensates for brain shift.
We drove the deformation model using displacement data derived from deformation in the frontal cortical surface that occurred during the DBS intervention. We evaluated 15 patients, retrospectively, who underwent bilateral DBS surgery, and assessed the accuracy of uCT in terms of target registration error (TRE) relative to a CT acquired post-placement (postCT). We further divided subjects into large (Group L) and small (Group S) deformation groups based on a TRE threshold of 1.6mm. Anterior commissure (AC), posterior commissure (PC) and pineal gland (PG) were identified on preCT and postCT and used to quantify TREs in preCT and uCT.
In the group of large brain deformation, average TREs for uCT were 1.11 ± 0.13 and 1.07 ± 0.38 mm at AC and PC, respectively, compared to 1.85 ± 0.17 and 0.92 ± 0.52 mm for preCT. The model updating approach improved AC localization but did not alter TREs at PC.
This preliminary study suggests that our image updating method may compensate for brain shift around surgical targets of importance during DBS surgery, although further investigation is warranted before conclusive evidence will be available.
With further development and evaluation, our model-based image updating method using intraoperative sparse data may compensate for brain shift in DBS surgery efficiently, and have utility in updating targeting coordinates.
深部脑刺激（DBS）的疗效取决于电极的准确放置。尽管立体定向框架可以实现基于图像的手术计划和手术区域的配准，但电极放置的准确性可能会因术中脑移位而降低。在这项研究中，我们改编了一个生物力学模型，以从整体脑位移估计中得出，我们通过该位移来对术前 CT（preCT）进行变形，以生成补偿脑移位的更新 CT（uCT）。
我们使用从 DBS 干预过程中发生的额皮质表面变形中得出的位移数据来驱动变形模型。我们回顾性地评估了 15 名接受双侧 DBS 手术的患者，并根据相对于放置后 CT（postCT）的目标注册误差（TRE）评估了 uCT 的准确性。我们还根据 1.6mm 的 TRE 阈值将患者分为大脑变形大（Group L）和小（Group S）组。在前 CT 和 postCT 上识别前联合（AC）、后联合（PC）和松果体（PG），并用于在 preCT 和 uCT 中量化 TRE。
在大脑变形大的组中，uCT 的平均 TRE 分别为 AC 和 PC 处的 1.11 ± 0.13mm 和 1.07 ± 0.38mm，而 preCT 分别为 1.85 ± 0.17mm 和 0.92 ± 0.52mm。该模型更新方法改善了 AC 的定位，但没有改变 PC 处的 TRE。
这项初步研究表明，我们的图像更新方法可能可以补偿 DBS 手术过程中重要手术靶点周围的脑移位，尽管在获得确凿的证据之前还需要进一步的研究。
随着进一步的开发和评估，我们使用术中稀疏数据的基于模型的图像更新方法可以有效地补偿 DBS 手术中的脑移位，并在更新靶向坐标方面具有实用性。
