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增强现实在前哨淋巴结活检中的应用。

Augmented reality for sentinel lymph node biopsy.

机构信息

Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland.

Nuclear Medicine Clinic, University Hospital Basel, Basel, Switzerland.

出版信息

Int J Comput Assist Radiol Surg. 2024 Jan;19(1):171-180. doi: 10.1007/s11548-023-03014-w. Epub 2023 Sep 25.

Abstract

INTRODUCTION

Sentinel lymph node biopsy for oral and oropharyngeal squamous cell carcinoma is a well-established staging method. One variation is to inject a radioactive tracer near the primary tumor of the patient. After a few minutes, audio feedback from an external hand-held [Formula: see text]-detection probe can monitor the uptake into the lymphatic system. Such probes place a high cognitive load on the surgeon during the biopsy, as they require the simultaneous use of both hands and the skills necessary to correlate the audio signal with the location of tracer accumulation in the lymph nodes. Therefore, an augmented reality (AR) approach to directly visualize and thus discriminate nearby lymph nodes would greatly reduce the surgeons' cognitive load.

MATERIALS AND METHODS

We present a proof of concept of an AR approach for sentinel lymph node biopsy by ex vivo experiments. The 3D position of the radioactive [Formula: see text]-sources is reconstructed from a single [Formula: see text]-image, acquired by a stationary table-attached multi-pinhole [Formula: see text]-detector. The position of the sources is then visualized using Microsoft's HoloLens. We further investigate the performance of our SLNF algorithm for a single source, two sources, and two sources with a hot background.

RESULTS

In our ex vivo experiments, a single [Formula: see text]-source and its AR representation show good correlation with known locations, with a maximum error of 4.47 mm. The SLNF algorithm performs well when only one source is reconstructed, with a maximum error of 7.77 mm. For the more challenging case to reconstruct two sources, the errors vary between 2.23 mm and 75.92 mm.

CONCLUSION

This proof of concept shows promising results in reconstructing and displaying one [Formula: see text]-source. Two simultaneously recorded sources are more challenging and require further algorithmic optimization.

摘要

简介

口腔和口咽鳞状细胞癌的前哨淋巴结活检是一种成熟的分期方法。一种变化是在患者的原发肿瘤附近注射放射性示踪剂。几分钟后,外部手持式 γ 探测探头的音频反馈可以监测到淋巴系统的摄取情况。这种探头在活检过程中给外科医生带来了很高的认知负荷,因为它们需要双手同时使用,并需要将音频信号与淋巴结中示踪剂积聚的位置相关联的技能。因此,一种用于直接可视化和因此区分附近淋巴结的增强现实 (AR) 方法将大大降低外科医生的认知负荷。

材料和方法

我们通过离体实验展示了一种用于前哨淋巴结活检的 AR 方法的概念验证。放射性 γ 源的 3D 位置是从单个 γ 图像重建的,该图像是由固定在桌子上的多针孔 γ 探测器采集的。然后使用 Microsoft 的 HoloLens 可视化源的位置。我们进一步研究了我们的 SLNF 算法在单个源、两个源和具有热背景的两个源的性能。

结果

在我们的离体实验中,单个 γ 源及其 AR 表示与已知位置具有很好的相关性,最大误差为 4.47 毫米。当仅重建一个源时,SLNF 算法的性能良好,最大误差为 7.77 毫米。对于更具挑战性的重建两个源的情况,误差在 2.23 毫米到 75.92 毫米之间变化。

结论

该概念验证在重建和显示单个 γ 源方面显示出有希望的结果。同时记录的两个源更具挑战性,需要进一步的算法优化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e731/10770201/efb8493fa8d2/11548_2023_3014_Fig1_HTML.jpg

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