Suppr超能文献

锥形束 CT 引导的经口机器人舌根手术的可变形图像配准。

Deformable image registration for cone-beam CT guided transoral robotic base-of-tongue surgery.

机构信息

Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.

出版信息

Phys Med Biol. 2013 Jul 21;58(14):4951-79. doi: 10.1088/0031-9155/58/14/4951. Epub 2013 Jun 27.

Abstract

Transoral robotic surgery (TORS) offers a minimally invasive approach to resection of base-of-tongue tumors. However, precise localization of the surgical target and adjacent critical structures can be challenged by the highly deformed intraoperative setup. We propose a deformable registration method using intraoperative cone-beam computed tomography (CBCT) to accurately align preoperative CT or MR images with the intraoperative scene. The registration method combines a Gaussian mixture (GM) model followed by a variation of the Demons algorithm. First, following segmentation of the volume of interest (i.e. volume of the tongue extending to the hyoid), a GM model is applied to surface point clouds for rigid initialization (GM rigid) followed by nonrigid deformation (GM nonrigid). Second, the registration is refined using the Demons algorithm applied to distance map transforms of the (GM-registered) preoperative image and intraoperative CBCT. Performance was evaluated in repeat cadaver studies (25 image pairs) in terms of target registration error (TRE), entropy correlation coefficient (ECC) and normalized pointwise mutual information (NPMI). Retraction of the tongue in the TORS operative setup induced gross deformation >30 mm. The mean TRE following the GM rigid, GM nonrigid and Demons steps was 4.6, 2.1 and 1.7 mm, respectively. The respective ECC was 0.57, 0.70 and 0.73, and NPMI was 0.46, 0.57 and 0.60. Registration accuracy was best across the superior aspect of the tongue and in proximity to the hyoid (by virtue of GM registration of surface points on these structures). The Demons step refined registration primarily in deeper portions of the tongue further from the surface and hyoid bone. Since the method does not use image intensities directly, it is suitable to multi-modality registration of preoperative CT or MR with intraoperative CBCT. Extending the 3D image registration to the fusion of image and planning data in stereo-endoscopic video is anticipated to support safer, high-precision base-of-tongue robotic surgery.

摘要

经口机器人手术(TORS)为舌根肿瘤的切除提供了一种微创方法。然而,由于术中设置的高度变形,手术目标和相邻关键结构的精确定位可能会受到挑战。我们提出了一种使用术中锥形束计算机断层扫描(CBCT)的变形配准方法,以将术前 CT 或 MR 图像与术中场景准确对齐。该配准方法结合了高斯混合(GM)模型和 Demons 算法的变体。首先,在对感兴趣的体积(即延伸到舌骨的舌体体积)进行分割后,应用 GM 模型对表面点云进行刚性初始化(GM 刚性),然后进行非刚性变形(GM 非刚性)。其次,应用 Demons 算法对(GM 配准的)术前图像和术中 CBCT 的距离图变换进行配准细化。在重复尸体研究(25 对图像)中,根据目标配准误差(TRE)、熵相关系数(ECC)和归一化点对点互信息(NPMI)评估性能。在 TORS 手术设置中,舌体的回缩会导致超过 30mm 的大体变形。在 GM 刚性、GM 非刚性和 Demons 步骤之后,TRE 的平均值分别为 4.6、2.1 和 1.7mm。ECC 分别为 0.57、0.70 和 0.73,NPMI 分别为 0.46、0.57 和 0.60。配准精度在舌体的上表面和靠近舌骨的区域最好(由于这些结构的表面点的 GM 配准)。Demons 步骤主要在离表面和舌骨更远的舌体深部细化配准。由于该方法不直接使用图像强度,因此适合于将术前 CT 或 MR 与术中 CBCT 进行多模态配准。预计将 3D 图像配准扩展到立体内窥镜视频中图像和规划数据的融合,将支持更安全、高精度的舌根机器人手术。

相似文献

1
Deformable image registration for cone-beam CT guided transoral robotic base-of-tongue surgery.
Phys Med Biol. 2013 Jul 21;58(14):4951-79. doi: 10.1088/0031-9155/58/14/4951. Epub 2013 Jun 27.
4
Deformable image registration with local rigidity constraints for cone-beam CT-guided spine surgery.
Phys Med Biol. 2014 Jul 21;59(14):3761-87. doi: 10.1088/0031-9155/59/14/3761. Epub 2014 Jun 17.
6
Deformable registration for intra-operative cone-beam CT guidance of head and neck surgery.
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:3634-7. doi: 10.1109/IEMBS.2008.4649995.
7
Intraoperative image-guided transoral robotic surgery: pre-clinical studies.
Int J Med Robot. 2015 Jun;11(2):256-67. doi: 10.1002/rcs.1602. Epub 2014 Jul 28.
10
A momentum-based diffeomorphic demons framework for deformable MR-CT image registration.
Phys Med Biol. 2018 Oct 24;63(21):215006. doi: 10.1088/1361-6560/aae66c.

引用本文的文献

1
Estimating tongue deformation during laryngoscopy using a hybrid FEM-multibody model and intraoperative tracking - a cadaver study.
Comput Methods Biomech Biomed Engin. 2025 May;28(6):739-749. doi: 10.1080/10255842.2023.2301672. Epub 2024 Jan 9.
3
A review of deep learning-based deformable medical image registration.
Front Oncol. 2022 Dec 7;12:1047215. doi: 10.3389/fonc.2022.1047215. eCollection 2022.
4
A personalized image-guided intervention system for peripheral lung cancer on patient-specific respiratory motion model.
Int J Comput Assist Radiol Surg. 2022 Oct;17(10):1751-1764. doi: 10.1007/s11548-022-02676-2. Epub 2022 May 31.
5
Initial experience with image-guided surgical navigation in transoral surgery.
Head Neck. 2019 Jan;41(1):E1-E10. doi: 10.1002/hed.25380. Epub 2018 Dec 16.
6
Quantifying Anatomic Deformations During Laryngoscopy.
Ann Biomed Eng. 2018 Jun;46(6):912-925. doi: 10.1007/s10439-018-2006-x. Epub 2018 Mar 14.
7
Integration of free-hand 3D ultrasound and mobile C-arm cone-beam CT: Feasibility and characterization for real-time guidance of needle insertion.
Comput Med Imaging Graph. 2017 Jun;58:13-22. doi: 10.1016/j.compmedimag.2017.03.003. Epub 2017 Apr 3.
8
Performance evaluation of MIND demons deformable registration of MR and CT images in spinal interventions.
Phys Med Biol. 2016 Dec 7;61(23):8276-8297. doi: 10.1088/0031-9155/61/23/8276. Epub 2016 Nov 3.
9
Augmented reality and cone beam CT guidance for transoral robotic surgery.
J Robot Surg. 2015 Sep;9(3):223-33. doi: 10.1007/s11701-015-0520-5. Epub 2015 Jul 21.
10
dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images.
Phys Med Biol. 2014 Sep 7;59(17):4799-826. doi: 10.1088/0031-9155/59/17/4799. Epub 2014 Aug 6.

本文引用的文献

1
Deformable Registration of the Inflated and Deflated Lung for Cone-Beam CT-Guided Thoracic Surgery.
Proc SPIE Int Soc Opt Eng. 2012 Feb 4;8316. doi: 10.1117/12.911440.
2
Toward Intraoperative Image-Guided Transoral Robotic Surgery.
J Robot Surg. 2013 Sep;7(3):217-25. doi: 10.1007/s11701-013-0420-5.
3
Margin mapping in transoral surgery for head and neck cancer.
Laryngoscope. 2013 May;123(5):1190-8. doi: 10.1002/lary.23900. Epub 2013 Feb 4.
6
Cone-Beam CT with a Flat-Panel Detector: From Image Science to Image-Guided Surgery.
Nucl Instrum Methods Phys Res A. 2011 Aug 21;648(S1):S241-S250. doi: 10.1016/j.nima.2010.11.088.
7
Surgical margins in head and neck cancer: a contemporary review.
Head Neck. 2013 Sep;35(9):1362-70. doi: 10.1002/hed.23110. Epub 2012 Sep 3.
8
A finite element method to correct deformable image registration errors in low-contrast regions.
Phys Med Biol. 2012 Jun 7;57(11):3499-515. doi: 10.1088/0031-9155/57/11/3499. Epub 2012 May 11.
9
The rise of transoral robotic surgery in the head and neck: emerging applications.
Expert Rev Anticancer Ther. 2012 Mar;12(3):373-80. doi: 10.1586/era.12.7.
10
A feature-based approach for refinement of model-based segmentation of low contrast structures.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:7977-80. doi: 10.1109/IEMBS.2011.6091967.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验