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立体定向系统中误差表征的通用框架。

A General Framework for Characterizing Inaccuracy in Stereotactic Systems.

作者信息

Jensen Michael A, Neimat Joseph S, Kerezoudis Panagiotis, Ali Rushna, Richardson R Mark, Halpern Casey H, Ojemann Steven, Ponce Francisco A, Lee Kendall H, Haugen Laura M, Klassen Bryan T, Kondziolka Douglas, Miller Kai J

机构信息

Department of Neurologic Surgery, Mayo Clinic, Rochester , Minnesota , USA.

Department of Neurosurgery, University of Louisville, Louisville , Kentucky , USA.

出版信息

Oper Neurosurg (Hagerstown). 2025 Mar 1;28(3):322-336. doi: 10.1227/ons.0000000000001423. Epub 2024 Dec 2.

DOI:10.1227/ons.0000000000001423
PMID:39627169
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11809961/
Abstract

BACKGROUND AND OBJECTIVES

Identifying and characterizing sources of targeting error in stereotactic procedures is essential to maximizing accuracy, potentially improving surgical outcomes. We aim to describe a generic framework which characterizes sources of stereotactic inaccuracy.

METHODS

We assembled a list of stereotactic systems: ROSA, Neuromate, Mazor Renaissance, ExcelsiusGPS, Cirq, STarFix (FHC), Nexframe, ClearPoint, CRW, and Leksell. We searched the literature for qualitative and quantitative work identifying and quantifying potential sources of inaccuracy and describing each system's implementation using Standards for Reporting Qualitative Research guidelines. Our literature search spanned 1969 to 2024, and various studies were included, with formats ranging from phantom studies to systematic reviews. Keyword searches were conducted, and the details about each system were used to create a framework for identifying and describing the unique targeting error profile of each system.

RESULTS

We describe and illustrate the details of various sources of stereotactic inaccuracies and generate a framework to unify these sources into a single framework. This framework entails 5 domains: imaging, registration, mechanical accuracy, target planning and adjustment, and trajectory planning and adjustment. This framework was applied to 10 stereotactic systems.

CONCLUSION

This framework provides a rubric to analyze the sources of error for any stereotactic system. Illustrations allow the reader to understand sources of error conceptually so that they may apply them to their practice.

摘要

背景与目的

识别并描述立体定向手术中靶点误差的来源对于最大化手术精度、潜在改善手术效果至关重要。我们旨在描述一个通用框架，以表征立体定向不准确的来源。

方法

我们整理了一份立体定向系统列表：ROSA、Neuromate、Mazor Renaissance、ExcelsiusGPS、Cirq、STarFix（FHC）、Nexframe、ClearPoint、CRW和Leksell。我们检索了文献，查找定性和定量研究，以识别和量化潜在的误差来源，并使用定性研究报告标准指南描述每个系统的应用情况。我们的文献检索涵盖1969年至2024年，纳入了各种研究，形式从体模研究到系统评价不等。进行了关键词搜索，并利用每个系统的详细信息创建了一个框架，用于识别和描述每个系统独特的靶点误差特征。

结果

我们描述并说明了立体定向不准确的各种来源的细节，并生成了一个框架，将这些来源统一到一个单一框架中。这个框架包含5个领域：成像、配准、机械精度、靶点规划与调整以及轨迹规划与调整。这个框架应用于10个立体定向系统。

结论

这个框架提供了一个用于分析任何立体定向系统误差来源的准则。图示使读者能够从概念上理解误差来源，以便他们将其应用于自己的实践中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7178/11809961/b3cc0bcf9b2b/ons-28-322-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7178/11809961/1babf3644c9f/ons-28-322-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7178/11809961/dd6049ff0f2b/ons-28-322-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7178/11809961/72fbac7dd840/ons-28-322-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7178/11809961/b3cc0bcf9b2b/ons-28-322-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7178/11809961/1babf3644c9f/ons-28-322-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7178/11809961/41a507a27d4f/ons-28-322-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7178/11809961/5cbe49311c88/ons-28-322-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7178/11809961/dd6049ff0f2b/ons-28-322-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7178/11809961/0e6ac0ca8820/ons-28-322-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7178/11809961/bf1ad15f21e4/ons-28-322-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7178/11809961/7040988677b7/ons-28-322-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7178/11809961/37860e083288/ons-28-322-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7178/11809961/b3cc0bcf9b2b/ons-28-322-g014.jpg

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