Shape-sensing needle and data-driven needle guiding robot for precision needle placement.
This collaborative research project with SNR Lab. at Brigham and Women’s Hospital and AMIRo Research Lab at Johns Hopkins University aims to improve needle placement accuracy for image-guided prostate interventions, including biopsy and focal treatment, by developing a novel shape-sensing needle and data-driven needle-guiding robot.
Needle placement plays a fundamental role in both the diagnosis and treatment of prostate cancer (PCa). Nearly one million prostate biopsies are performed annually in the United States, where tissues are sampled from the prostate gland either systematically or targetedly by using a core biopsy needle for pathological examination. The confirmed lesions may then be treated percutaneously using brachytherapy or thermal ablation (i.e. laser, cryoablation) using needle-shape probes – depending on their grade, clinical indications, and patient preference. For those procedures, accurate needle placement is crucial as it would avoid false-negative biopsies and ensure an optimal dose distribution for brachytherapy or an optimal ablation zone for thermal ablations.
Needle-guide devices (robots) can potentially improve the accuracy of needle placement and address those clinical unment needs. They are typically guided by transrectal ultrasound (TRUS), MRI, or a combination of both. However, even if the needle is physically guided by a needle-guide device outside the patient’s body, the needle tip can still deviate from the intended pathdue to the tissue-needle interaction, and fail to sample tissue from the target lesion or deliver the treatment accurately.
To provide a clinically-viable solution to this inherent problem, the team is working to develop and validate an optical fiber-based shape-sensing needle (sensorized needle) that can detect deviation of the needle in vivo, and an adaptive needle guide device that actively compensate for the deviation during insertion. The sensorized needle does not require imaging to detect the actual deviation and it can be used in conjunction with any type of imaging guidance, including TRUS and MRI. Our hypothesis is that real-time feedback from the sensorized needle, using fiber Bragg gratings (FBG) optical fibers, will allow the needle guide to accurately compensate for the deviation of the needle, hence minimizing the targeting errors.
Collaborators
- Dr. Nobuhiko Hata, Ph.D. (Brigham and Women’s Hospital)
- Dr. Iulian Iordachita, Ph.D. (Johns Hopkins University)
- Dr. Kemal Tuncali, M.D. (Brigham and Women’s Hospital)
References
Shape-Sensing Needle Developed by JHU AMIRo Lab.
- Lezcano DA, Iordachita II, Kim JS. Lie-Group Theoretic Approach to Shape-Sensing Using FBG-Sensorized Needles Including Double-Layer Tissue and S-Shape Insertions. IEEE Sens J. 2022;22(22):22232–22243. doi:10.1109/jsen.2022.3212209 PMID: 37216067. PMCID: PMC10193911.
- Lezcano DA, Kim MJ, Iordachita II, Kim JS. Toward FBG-Sensorized Needle Shape Prediction in Tissue Insertions. Proc IEEE/RSJ International Conference on Intelligent Robots and Systems. 2022;2022:3505–3511. doi:10.1109/iros47612.2022.9981856 PMID: 36636257. PMCID: PMC9832576.
Data-driven needle-guiding robot by Tokuda Lab.
- Bernardes MC, Moreira P, Mareschal L, Tempany C, Tuncali K, Hata N, Tokuda J. Data-driven adaptive needle insertion assist for transperineal prostate interventions. Phys Med Biol. 2023;68(10). doi:10.1088/1361-6560/accefa PMID: 37080237. PMCID: PMC10249778.
Acknowledgements
The study was funded in part by the National Institutes of Health (R01CA235134, R01EB020667, and P41EB028741). The content of the material is solely the responsibility of the authors and does not necessarily represent the official views of these agencies.
