Fully Distributed Shape Sensing of a Flexible Surgical Needle Using Optical Frequency Domain Reflectometry for Prostate Interventions.

In minimally invasive procedures such as biopsies and prostate cancer brachytherapy, accurate needle placement remains challenging due to limitations in current tracking methods related to interference, reliability, resolution or image contrast. This often leads to frequent needle adjustments and reinsertions. To address these shortcomings, we introduce an optimized needle shape-sensing method using a fully distributed grating-based sensor.

Hybrid Deep Learning and Model-Based Needle Shape Prediction.

Needle insertion using flexible bevel tip needles are a common minimally-invasive surgical technique for prostate cancer interventions. Flexible, asymmetric bevel tip needles enable physicians for complex needle steering techniques to avoid sensitive anatomical structures during needle insertion. For accurate placement of the needle, predicting the trajectory of these needles intra-operatively would greatly reduce the need for frequently needle reinsertions thus improving patient comfort and positive outcomes.

Optical Fiber-Based Needle Shape Sensing in Real Tissue: Single Core vs. Multicore Approaches.

Flexible needle insertion procedures are common in minimally-invasive surgeries for diagnosing and treating prostate cancer. Bevel-tip needles provide physicians the capability to steer the needle during long insertions to avoid vital anatomical structures in the patient and reduce post-operative patient discomfort. To provide needle placement feedback to the physician, sensors are embedded into needles for determining the real-time 3D shape of the needle during operation without needing to visualize the needle intra-operatively.

Optical Fiber-Based Needle Shape Sensing in Real Tissue: Single Core vs. Multicore Approaches.

Flexible needle insertion procedures are common for minimally-invasive surgeries for diagnosing and treating prostate cancer. Bevel-tip needles provide physicians the capability to steer the needle during long insertions to avoid vital anatomical structures in the patient and reduce post-operative patient discomfort. To provide needle placement feedback to the physician, sensors are embedded into needles for determining the real-time 3D shape of the needle during operation without needing to visualize the needle intra-operatively.

Optical Fiber -Based Needle Shape Sensing: Three-channel Single Core vs. Multicore Approaches.

Bevel-tip needles are commonly utilized in percutaneous medical interventions where a curved insertion trajectory is required. To avoid deviation from the intended trajectory, needle shape sensing and tip localization is crucial in providing the operator with feedback. There is an abundance of previous work that investigate the medical application of fiber Bragg grating (FBG) sensors, but most works select only one specific type of fiber among the many available sensor options to integrate into their hardware designs.

Lie-Group Theoretic Approach to Shape-Sensing Using FBG-Sensorized Needles Including Double-Layer Tissue and S-Shape Insertions.

Flexible bevel-tipped needles are often used for needle insertion in minimally-invasive surgical techniques due to their ability to be steered in cluttered environments. Shapesensing enables physicians to determine the location of needles intra-operatively without requiring radiation of the patient, enabling accurate needle placement. In this paper, we validate a theoretical method for flexible needle shape-sensing that allows for complex curvatures, extending upon a previous sensor-based model.

Toward FBG-Sensorized Needle Shape Prediction in Tissue Insertions.

Complex needle shape prediction remains an issue for planning of surgical interventions of flexible needles. In this paper, we validate a theoretical method for flexible needle shape prediction allowing for non-uniform curvatures, extending upon a previous sensor-based model which combines curvature measurements from fiber Bragg grating (FBG) sensors and the mechanics of an inextensible elastic rod to determine and predict the 3D needle shape during insertion. We evaluate the model's effectiveness in single-layer isotropic tissue for shape sensing and shape prediction capabilities.

Toward FBG-Sensorized Needle Shape Detection in Real Tissue Insertions.

The determination of flexible needle shape during insertion is critical for planning and validation in minimally invasive surgical percutaneous procedures. In this paper, we validate a needle shape-sensing method using fiber Bragg grating (FBG) sensors over sequential needle insertion lengths in gel phantom and real tissue. Experiments on a four-active area, FBG-sensorized needle were performed in both isotropic simulated tissue and inhomogeneous animal tissue with computed tomography (CT) as the ground truth of the needle shape.

Towards Automatic Robotic Calibration System for Flexible Needles with FBG Sensors.

There has been much research exploring the use of fiber Bragg grating (FBG)-sensorized needles in the prostate biopsy procedure, but all FBG needles used in the research need to be calibrated, which is time consuming and prone to human errors. In this work, a semi-automatic robotic system was developed to perform FBG needle calibration. Compared to manual calibration results, the robotic system is able to calibrate FBG needles with the similar level of accuracy as achieved by an experienced manual operator, thus reducing the time cost during the needle calibration process.

Trajectory Generation of FBG-Sensorized Needles for Insertions into Multi-Layer Tissue.

Several models incorporate needle shape prediction, however prediction in multi-layer tissue for complex needle shape remains an issue. In this work, we present a method for trajectory generation of flexible needles that allows for complex curvatures, extending upon a previous sensor-based model. This model combines curvature measurements from fiber Bragg grating (FBG) sensors and the mechanics of an inextensible elastic rod for shape-sensing.