Joint synthesis and registration network for deformable MR-CBCT image registration for neurosurgical guidance.
Joint synthesis and registration network for deformable MR-CBCT image registration for neurosurgical guidance.
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DOI：
10.1088/1361-6560/ac72ef
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发表时间：
2022-06-10
影响因子：
3.5
通讯作者：
Siewerdsen, J. H.
中科院分区：
文献类型：
--
作者：
Han, R.;Jones, C. K.;Lee, J.;Zhang, X.;Wu, P.;Vagdargi, P.;Uneri, A.;Helm, P. A.;Luciano, M.;Anderson, W. S.;Siewerdsen, J. H.
在微创神经外科手术中，导航的准确性经常受到脑深部畸形的挑战(在神经内窥镜手术中，由于脑脊液流出，最大可达10 mm)。我们提出了一种基于深度学习的可变形配准方法来解决术前MR和术中CBCT之间的这种变形。该配准方法使用联合图像合成和配准网络(记为JSR)来同时将MR和CBCT图像合成到CT域，并使用多分辨率金字塔进行CT域配准。首先使用模拟数据集(模拟CBCT和模拟变形)训练JSR，然后通过转移学习在真实临床图像上进行细化。多分辨率JSR的性能与单分辨率架构以及一系列替代配准方法(对称归一化(SYN)、VoxelMorph和基于图像合成的配准方法)进行了比较。JSR在脑深部结构中的中位骰子系数(DSC)为0.69，在模拟数据集中的中位目标配准误差(TRE)为1.94 mm，比单分辨率结构(中位DSC=0.68，中位TRE=2.14 mm)有所改善。此外，与其他方法--例如SYN(中位数DSC=0.54，中位数TRE=2.77 mm)、VoxelMorph(中位数DSC=0.52，中位数TRE=2.66 mm)相比，JSR获得了更好的注册效果，并提供了不到3个S的注册时间。多分辨率JSR网络解决了MR和CBCT图像之间的脑深部变形，性能优于其他最先进的方法。该方法的准确性和运行时间支持了该方法在高精度神经外科中进一步的临床研究。
The accuracy of navigation in minimally invasive neurosurgery is often challenged by deep brain deformations (up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach). We propose a deep learning-based deformable registration method to address such deformations between preoperative MR and intraoperative CBCT. The registration method uses a joint image synthesis and registration network (denoted JSR) to simultaneously synthesize MR and CBCT images to the CT domain and perform CT domain registration using a multi-resolution pyramid. JSR was first trained using a simulated dataset (simulated CBCT and simulated deformations) and then refined on real clinical images via transfer learning. The performance of the multi-resolution JSR was compared to a single-resolution architecture as well as a series of alternative registration methods (symmetric normalization (SyN), VoxelMorph, and image synthesis-based registration methods). JSR achieved median Dice coefficient (DSC) of 0.69 in deep brain structures and median target registration error (TRE) of 1.94 mm in the simulation dataset, with improvement from single-resolution architecture (median DSC = 0.68 and median TRE = 2.14 mm). Additionally, JSR achieved superior registration compared to alternative methods—e.g. SyN (median DSC = 0.54, median TRE = 2.77 mm), VoxelMorph (median DSC = 0.52, median TRE = 2.66 mm) and provided registration runtime of less than 3 s. Similarly in the clinical dataset, JSR achieved median DSC = 0.72 and median TRE = 2.05 mm. The multi-resolution JSR network resolved deep brain deformations between MR and CBCT images with performance superior to other state-of-the-art methods. The accuracy and runtime support translation of the method to further clinical studies in high-precision neurosurgery.
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影响因子：
3.8
作者：
Harms J;Lei Y;Wang T;Zhang R;Zhou J;Tang X;Curran WJ;Liu T;Yang X
通讯作者：
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影响因子：
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作者：
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Gee, J. C.
影响因子：
10.9
作者：
Hu Y;Modat M;Gibson E;Li W;Ghavami N;Bonmati E;Wang G;Bandula S;Moore CM;Emberton M;Ourselin S;Noble JA;Barratt DC;Vercauteren T
通讯作者：
Vercauteren T