Comparison study of intraoperative surface acquisition methods on registration accuracy for soft-tissue surgical navigation.

IF 1.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Medical Imaging Pub Date : 2024-03-01 Epub Date: 2024-03-04 DOI:10.1117/1.JMI.11.2.025001
Bowen Xiang, Jon S Heiselman, Winona L Richey, Michael I D'Angelica, Alice Wei, T Peter Kingham, Frankangel Servin, Kyvia Pereira, Sunil K Geevarghese, William R Jarnagin, Michael I Miga
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Abstract

Purpose: To study the difference between rigid registration and nonrigid registration using two forms of digitization (contact and noncontact) in human in vivo liver surgery.

Approach: A Conoprobe device attachment and sterilization process was developed to enable prospective noncontact intraoperative acquisition of organ surface data in the operating room (OR). The noncontact Conoprobe digitization method was compared against stylus-based acquisition in the context of image-to-physical registration for image-guided surgical navigation. Data from n=10 patients undergoing liver resection were analyzed under an Institutional Review Board-approved study at Memorial Sloan Kettering Cancer Center. Organ surface coverage of each surface acquisition method was compared. Registration accuracies resulting from the acquisition techniques were compared for (1) rigid registration method (RRM), (2) model-based nonrigid registration method (NRM) using surface data only, and (3) NRM with one subsurface feature (vena cava) from tracked intraoperative ultrasound (NRM-VC). Novel vessel centerline and tumor targets were segmented and compared to their registered preoperative counterparts for accuracy validation.

Results: Surface data coverage collected by stylus and Conoprobe were 24.6%±6.4% and 19.6%±5.0%, respectively. The average difference between stylus data and Conoprobe data using NRM was -1.05  mm and using NRM-VC was -1.42  mm, indicating the registrations to Conoprobe data performed worse than to stylus data with both NRM approaches. However, using the stylus and Conoprobe acquisition methods led to significant improvement of NRM-VC over RRM by average differences of 4.48 and 3.66 mm, respectively.

Conclusion: The first use of a sterile-field amenable Conoprobe surface acquisition strategy in the OR is reported for open liver surgery. Under clinical conditions, the nonrigid registration significantly outperformed standard-of-care rigid registration, and acquisition by contact-based stylus and noncontact-based Conoprobe produced similar registration results. The accuracy benefits of noncontact surface acquisition with a Conoprobe are likely obscured by inferior data coverage and intrinsic noise within acquisition systems.

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术中表面采集方法对软组织手术导航注册准确性的比较研究。
目的:研究在人体活体肝脏手术中使用两种数字化形式(接触式和非接触式)进行刚性配准和非刚性配准之间的差异:方法:开发了一种Conoprobe设备连接和消毒流程,以便在手术室(OR)内前瞻性地非接触式获取器官表面数据。非接触式Conoprobe数字化方法与基于触针的采集方法在图像引导手术导航的图像到物理注册方面进行了比较。在纪念斯隆-凯特琳癌症中心(Memorial Sloan Kettering Cancer Center)进行的研究获得了机构审查委员会(Institutional Review Board)的批准,对接受肝脏切除术的 10 名患者的数据进行了分析。比较了每种表面采集方法的器官表面覆盖率。比较了(1)刚性配准法(RRM)、(2)仅使用表面数据的基于模型的非刚性配准法(NRM)和(3)带有术中超声追踪的一个次表面特征(腔静脉)的非刚性配准法(NRM-VC)的采集技术所产生的配准精度。对新的血管中心线和肿瘤目标进行分割,并将其与术前登记的对应目标进行比较,以验证准确性:结果:测针和 Conoprobe 采集的表面数据覆盖率分别为 24.6%±6.4% 和 19.6%±5.0%。使用 NRM 时,测针数据与 Conoprobe 数据之间的平均差异为-1.05 毫米,而使用 NRM-VC 时则为-1.42 毫米,这表明在两种 NRM 方法中,Conoprobe 数据的注册性能都比测针数据差。然而,使用测针和 Conoprobe 采集方法后,NRM-VC 比 RRM 有了显著改善,平均差值分别为 4.48 毫米和 3.66 毫米:结论:首次报道了在手术室中将无菌场适用的 Conoprobe 表面采集策略用于开腹肝脏手术。在临床条件下,非刚性套准明显优于常规刚性套准,接触式测针和非接触式Conoprobe采集的套准结果相似。使用Conoprobe进行非接触式表面采集的精确度优势很可能被劣质的数据覆盖范围和采集系统的固有噪声所掩盖。
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来源期刊
Journal of Medical Imaging
Journal of Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.10
自引率
4.20%
发文量
0
期刊介绍: JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.
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