Bernardes Mariana C, Moreira Pedro, Lezcano Dimitri, Foley Lori, Tuncali Kemal, Tempany Clare, Kim Jin Seob, Hata Nobuhiko, Iordachita Iulian, Tokuda Junichi
Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Johns Hopkins University, Baltimore, MD 21218, USA.
IEEE Robot Autom Lett. 2024 Oct;9(10):8975-8982. doi: 10.1109/lra.2024.3455940. Epub 2024 Sep 6.
This study addresses the targeting challenges in MRI-guided transperineal needle placement for prostate cancer (PCa) diagnosis and treatment, a procedure where accuracy is crucial for effective outcomes. We introduce a parameter-agnostic trajectory correction approach incorporating a data-driven closed-loop strategy by radial displacement and an FBG-based shape sensing to enable autonomous needle steering. In an animal study designed to emulate clinical complexity and assess MRI compatibility through a PCa mock biopsy procedure, our approach demonstrated a significant improvement in targeting accuracy (p<0.05), with mean target error of only 2.2 ± 1.9 mm on first insertion attempts, without needle reinsertions. To the best of our knowledge, this work represents the first evaluation of robotic needle steering with FBG-sensor feedback, marking a significant step towards its clinical translation.
本研究探讨了磁共振成像（MRI）引导下经会阴穿刺针置入术在前列腺癌（PCa）诊断和治疗中的靶向挑战，该手术的准确性对于有效治疗结果至关重要。我们引入了一种与参数无关的轨迹校正方法，该方法结合了基于径向位移的数据驱动闭环策略和基于光纤布拉格光栅（FBG）的形状传感技术，以实现穿刺针的自主操控。在一项旨在模拟临床复杂性并通过PCa模拟活检程序评估MRI兼容性的动物研究中，我们的方法在靶向准确性方面有显著提高（p<0.05），首次插入尝试时平均目标误差仅为2.2±1.9毫米，无需重新插入穿刺针。据我们所知，这项工作代表了对具有FBG传感器反馈的机器人穿刺针操控的首次评估，朝着其临床转化迈出了重要一步。
