反射计
光纤布拉格光栅
光纤
校准
曲率
计算机科学
度量(数据仓库)
声学
外推法
插值(计算机图形学)
时域
计算
光纤传感器
分割
光学
计算机视觉
人工智能
频域
点(几何)
准确度和精密度
干涉测量
采样(信号处理)
可视化
前列腺癌
作者
Jacynthe Francoeur,Melika S. Hosseini,Raman Kashyap,Samuel Kadoury
摘要
Prostate cancer interventions, including biopsies and brachytherapy, require precise needle placement to avoid damage to surrounding tissues and ensure accurate targeting of the prostate gland. Standard force sensing methods, such as sparse fiber Bragg grating sensors, measure strain at discrete points and thus rely on interpolation or extrapolation techniques, introducing inaccuracies in force estimations. Deflection-based methods estimate forces from shape changes but are computationally expensive and slow due to complex model optimization, while tip force sensors only measure forces at the tip, missing data along the instrument’s length. A key advantage of using an optical frequency domain reflectometry (OFDR)-based sensor is the ability to perform linear segmentation on real measured curvature data, removing the need for discrete point approximation, improving both the accuracy and speed of force estimations along the entire length of the instrument. This study introduces a fiber optic needle sensor based on OFDR to measure shape and external forces during needle insertions in prostate interventions. Integrated into an MRI-compatible nitinol needle, the sensor employs distributed strain sensing to directly compute needle curvature and external forces, enabling precise force detection during insertion. Calibration procedures ensured high accuracy, with force estimation errors within 1-3g and sub-centimeter localization precision. Simulations demonstrated that the segmentation approach achieved comparable accuracy to cost-function optimization methods while achieving a more than 100-fold reduction in computation time. These results highlight the sensor’s potential to enhance prostate cancer interventions by providing fast, precise force sensing and needle tracking.
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