The OncoReach Stylet for Brachytherapy: Design Evaluation and Pilot Study
- URL: http://arxiv.org/abs/2601.13529v1
- Date: Tue, 20 Jan 2026 02:25:47 GMT
- Title: The OncoReach Stylet for Brachytherapy: Design Evaluation and Pilot Study
- Abstract summary: The OncoReach stylet is a handheld, tendon-driven steerable stylet designed for compatibility with standard ISBT 15- and 13-gauge needles.<n>A two-tube Cosserat rod model accurately predicted the centerline shape of the needle for most trials.<n>Results showed the ability to steer from less-invasive, medial entry points to reach the lateral-most targets.
- Score: 0.18936012617655987
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cervical cancer accounts for a significant portion of the global cancer burden among women. Interstitial brachytherapy (ISBT) is a standard procedure for treating cervical cancer; it involves placing a radioactive source through a straight hollow needle within or in close proximity to the tumor and surrounding tissue. However, the use of straight needles limits surgical planning to a linear needle path. We present the OncoReach stylet, a handheld, tendon-driven steerable stylet designed for compatibility with standard ISBT 15- and 13-gauge needles. Building upon our prior work, we evaluated design parameters like needle gauge, spherical joint count and spherical joint placement, including an asymmetric disk design to identify a configuration that maximizes bending compliance while retaining axial stiffness. Free space experiments quantified tip deflection across configurations, and a two-tube Cosserat rod model accurately predicted the centerline shape of the needle for most trials. The best performing configuration was integrated into a reusable handheld prototype that enables manual actuation. A patient-derived, multi-composite phantom model of the uterus and pelvis was developed to conduct a pilot study of the OncoReach steerable stylet with one expert user. Results showed the ability to steer from less-invasive, medial entry points to reach the lateral-most targets, underscoring the significance of steerable stylets.
Related papers
- Anatomy-Change-Aware Bidirectional Selective State-Space Memory for Clinically Deployed Thoracic Radiotherapy Auto-Contouring [0.6439594960617159]
DAMM-Net++ is a 2.5D architecture for thoracic radiotherapy auto-contouring.<n>It addresses inter-slice surface incoherence, systematic failure on small low-contrast targets, and the absence of per-case reliability signals.<n>It achieves a mean Dice of 0.955 and HD95 of 3.78 mm, with the largest gains on low-contrast organs-at-risk.
arXiv Detail & Related papers (2026-09-11T11:13:06Z) - Modular Robotic Catheters for Endovascular Aneurysm Repair [55.52876285801259]
endovascular aneurysm repair require surgeons to navigate catheters and guidewires into various branches of the abdominal aorta.<n>Previous clinical studies suggest that surgeons continue to struggle with vessel access using standard commercial instruments.<n>We present two contributions to solving this problem: 1) A bespoke 2-segment steerable catheter, consisting of 4 degrees of freedom to enhance dexterity.
arXiv Detail & Related papers (2026-07-28T14:53:03Z) - Rendering Novel Views of MRI Using 3D Gaussian Splatting [54.6286495315908]
We adapt 3D Gaussian Splatting to form a reconstruction starting from sparse anisotropic MRIs, and imaging planes aligned with the anatomy relevant for clinical evaluation are then sampled and rendered.<n>The novel view plane is optimal for diagnostic radiological grading of the target anatomy, whereas the original MRI is not.
arXiv Detail & Related papers (2026-06-24T18:00:03Z) - Bilinear Model Predictive Control Framework of the OncoReach, a Tendon-Driven Steerable Stylet for Brachytherapy [0.11726720776908518]
Steerable needles have the potential to improve interstitial brachytherapy by enabling curved trajectories that avoid sensitive anatomical structures.<n>Existing modeling and control approaches are primarily developed for custom needle designs and are not directly applicable to stylets compatible with commercially available clinical needles.<n>This paper presents a bilinear model predictive control framework for a tendon-driven steerable stylet integrated with a standard brachytherapy needle.
arXiv Detail & Related papers (2026-04-06T19:17:43Z) - Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steering [94.37535002230504]
We develop a training-free, inference-time control framework termed Semantically Decoupled Latent Steering.<n>Our approach constructs a semantic-free intervention vector via large language model (LLM)-driven semantic decomposition.<n>We show that our approach significantly reduces the probability of historical hallucinations.
arXiv Detail & Related papers (2026-02-27T04:49:01Z) - An Anatomy-specific Guidewire Shaping Robot for Improved Vascular Navigation [1.1360832156847105]
We present a bench-top guidewire shaping robot capable of producing navigation-specific desired wire configurations.<n>We show that the robot can produce clinically calibrated common tip geometries (C, S, Angled, Hook) and validate them with respect to the model-predicted shapes in 2D.
arXiv Detail & Related papers (2026-02-03T22:36:16Z) - Silhouette-to-Contour Registration: Aligning Intraoral Scan Models with Cephalometric Radiographs [10.70146635420186]
We propose DentalSCR, a pose-stable, contour-guided framework for accurate and interpretable silhouette-to-contour registration.<n>We evaluate DentalSCR on 34 expert-annotated clinical cases.
arXiv Detail & Related papers (2025-11-18T10:50:04Z) - Automatic Ultrasound Curve Angle Measurement via Affinity Clustering for Adolescent Idiopathic Scoliosis Evaluation [1.9747854071595796]
The current clinical gold standard for evaluating adolescent idiopathic scoliosis (AIS) is X-ray radiography, using Cobb angle measurement.
We introduce an estimation model for automatic ultrasound curve angle (UCA) measurement.
The model employs a dual-branch network to detect candidate landmarks and perform vertebra segmentation on ultrasound coronal images.
arXiv Detail & Related papers (2024-05-06T03:28:47Z) - Predicting Spine Geometry and Scoliosis from DXA Scans [49.68543422441626]
We first train a neural network to predict the middle spine curve in the scan, and then use an integral-based method to determine the curvature along the spine curve.
We show that the maximum curvature can be used as a scoring function for ordering the severity of spinal deformation.
arXiv Detail & Related papers (2023-11-15T22:49:08Z) - Collaborative Robotic Biopsy with Trajectory Guidance and Needle Tip
Force Feedback [49.32653090178743]
We present a collaborative robotic biopsy system that combines trajectory guidance with kinesthetic feedback to assist the physician in needle placement.
A needle design that senses forces at the needle tip based on optical coherence tomography and machine learning for real-time data processing.
We demonstrate that even smaller, deep target structures can be accurately sampled by performing post-mortem in situ biopsies of the pancreas.
arXiv Detail & Related papers (2023-06-12T14:07:53Z) - Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via
Volumetric Pseudo-Labeling [66.75096111651062]
We created a large-scale dataset of 10,021 thoracic CTs with 157 labels.
We applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels.
Our resulting segmentation models demonstrated remarkable performance on CXR.
arXiv Detail & Related papers (2023-06-06T18:01:08Z) - Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian
Shape Framework [65.19784967388934]
Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy.
We propose a knowledge-driven framework for RLN localization, mimicking the standard approach surgeons take to identify the RLN according to its surrounding organs.
Experimental results indicate that the proposed method achieves superior hit rates and substantially smaller distance errors compared with state-of-the-art methods.
arXiv Detail & Related papers (2022-06-30T13:04:42Z) - A Recurrent Neural Network Approach to Roll Estimation for Needle
Steering [5.556129660751467]
Steerable needles are a promising technology for delivering targeted therapies in the body.
Current sensors do not provide full orientation information or interfere with the needle's ability to deliver therapy.
We propose a model-free, learned-method that leverages LSTM neural networks to estimate the needle tip's orientation online.
arXiv Detail & Related papers (2021-01-13T03:40:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.