# Adaptive Percutaneous Prostate Interventions using Sensorized Needle

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2024 · $625,285

## Abstract

Project Summary / Abstract
 This project aims to improve needle placement accuracy for image-guided prostate interventions,
including biopsy and focal treatment. Building upon the success of the previous cycle, the project seeks to
enhance the robustness and effectiveness of the technology in complex anatomical structures, thereby aiding
clinical translation for prostate cancer care and broader applications. Percutaneous needle placement is a critical
procedure in both the diagnosis and treatment of prostate cancer. Although these procedures are often assisted
by an external needle-guiding device for accuracy, the unpredictability of needle deflection due to interactions
with varying tissue densities frequently necessitates multiple placement attempts. This prolongs the procedure
time and can lead to excessive tissue damage. The project’s previous cycle aimed to tackle this problem by
developing two technologies: a fiber-Bragg-grating (FBG)-based shape-sensing needle (sensorized needle) for
real-time feedback and a data-driven needle steering algorithm (COADAP) for active compensation of needle
deflection. Together, these technologies created a closed-loop adaptive needle placement system, enhancing
needle placement accuracy. However, the team identified these technologies’ limitations when the needle
encountered complex, interconnected multistructural anatomy. In such situations, the needle’s interactions with
different structures led to significant deflection, limiting the technology’s application in clinical settings. Therefore,
this phase aims to address this challenge by extending the capabilities of the sensorized needle and COADAP
algorithm. The research plan comprises three specific aims: (Aim 1) Develop a multi-core FBG sensorized needle
for robust distributed shape sensing: We will enhance the design of sensorized needles using multi-core fiber
(MCF) sensors for robust and distributed needle shape sensing. We will develop a machine-learning model to
predict the needle trajectory using real-time shape information. (Aim 2) Extend the COADAP algorithm for
interconnected multistructural anatomy: The objective is to compensate for needle deflection in interconnected
multistructural anatomy. This involves the development of an extended COADAP algorithm, called Shape-
Control COADAP (SC-COADAP), to account for the full needle shape in model predictive control. (Aim 3)
Validate the sensorized needle with COADAP in interconnected multistructural anatomy: We will test the
hypothesis that adaptive needle placement with the MCF sensorized needle and SC-COADAP meets the
required accuracy. This will be done via ex vivo and in vivo validation, using a multistructural anatomy-mimicking
phantom and swine models, respectively.

## Key facts

- **NIH application ID:** 10880065
- **Project number:** 2R01CA235134-04
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Nobuhiko Hata
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $625,285
- **Award type:** 2
- **Project period:** 2019-08-01 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10880065

## Citation

> US National Institutes of Health, RePORTER application 10880065, Adaptive Percutaneous Prostate Interventions using Sensorized Needle (2R01CA235134-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10880065. Licensed CC0.

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