Reliable and accurate needle localization in curvilinear ultrasound images using signature‐based analysis of ultrasound beamformed radio frequency signals

Daoud, Mohammad I.; Abu-Hani, Ayah F.S.; Alazrai, Rami

doi:10.1002/mp.14126

Cited by 6 publications

(5 citation statements)

References 35 publications

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“…Some authors [ 8 – 10 ] used full or partial brightness of the needle in the US image to reconstruct its shape. Other authors [ 11 ] introduced a two-phase method based on a needle-specific multi-echo model, showing very good performance but lacking in generalizability. To address this, dynamic intensity changes arising from needle movement in the US image have been used [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Bridging the simulation-to-real gap for AI-based needle and target detection in robot-assisted ultrasound-guided interventions

et al. 2023

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Background Artificial intelligence (AI)-powered, robot-assisted, and ultrasound (US)-guided interventional radiology has the potential to increase the efficacy and cost-efficiency of interventional procedures while improving postsurgical outcomes and reducing the burden for medical personnel. Methods To overcome the lack of available clinical data needed to train state-of-the-art AI models, we propose a novel approach for generating synthetic ultrasound data from real, clinical preoperative three-dimensional (3D) data of different imaging modalities. With the synthetic data, we trained a deep learning-based detection algorithm for the localization of needle tip and target anatomy in US images. We validated our models on real, in vitro US data. Results The resulting models generalize well to unseen synthetic data and experimental in vitro data making the proposed approach a promising method to create AI-based models for applications of needle and target detection in minimally invasive US-guided procedures. Moreover, we show that by one-time calibration of the US and robot coordinate frames, our tracking algorithm can be used to accurately fine-position the robot in reach of the target based on 2D US images alone. Conclusions The proposed data generation approach is sufficient to bridge the simulation-to-real gap and has the potential to overcome data paucity challenges in interventional radiology. The proposed AI-based detection algorithm shows very promising results in terms of accuracy and frame rate. Relevance statement This approach can facilitate the development of next-generation AI algorithms for patient anatomy detection and needle tracking in US and their application to robotics. Key points • AI-based methods show promise for needle and target detection in US-guided interventions. • Publicly available, annotated datasets for training AI models are limited. • Synthetic, clinical-like US data can be generated from magnetic resonance or computed tomography data. • Models trained with synthetic US data generalize well to real in vitro US data. • Target detection with an AI model can be used for fine positioning of the robot. Graphical Abstract

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Section: Introductionmentioning

confidence: 99%

Bridging the simulation-to-real gap for AI-based needle and target detection in robot-assisted ultrasound-guided interventions

et al. 2023

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“…The method by Daoud et al 16 . was extended recently 17 to enable the detection of the needle axis and tip using the beamformed ultrasound radio‐frequency signals. Despite the effective needle detection achieved by the method introduced in Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the prebeamformed ultrasound radio-frequency signals acquired by the transducer elements were processed to detect the needle axis. The method by Daoud et al 16 was extended recently 17 to enable the detection of the needle axis and tip using the beamformed ultrasound radio-frequency signals. Despite the effective needle detection achieved by the method introduced in Ref.…”

Section: Introductionmentioning

confidence: 99%

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Needle detection using ultrasound B‐mode and power Doppler analyses

et al. 2022

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Background: Ultrasound is employed in needle interventions to visualize the anatomical structures and track the needle. Nevertheless, needle detection in ultrasound images is a difficult task, specifically at steep insertion angles. Purpose: A new method is presented to enable effective needle detection using ultrasound B-mode and power Doppler analyses. Methods: A small buzzer is used to excite the needle and an ultrasound system is utilized to acquire B-mode and power Doppler images for the needle. The B-mode and power Doppler images are processed using Radon transform and local-phase analysis to initially detect the axis of the needle. The detection of the needle axis is improved by processing the power Doppler image using alpha shape analysis to define a region of interest (ROI) that contains the needle. Also, a set of feature maps is extracted from the ROI in the B-mode image. The feature maps are processed using a machine learning classifier to construct a likelihood image that visualizes the posterior needle likelihoods of the pixels. Radon transform is applied to the likelihood image to achieve an improved needle axis detection. Additionally, the region in the B-mode image surrounding the needle axis is analyzed to identify the needle tip using a custom-made probabilistic approach. Our method was utilized to detect needles inserted in ex vivo animal tissues at shallow [20 • − 40 • ), moderate [40 • − 60 • ), and steep [60 • − 85 • ] angles.Results: Our method detected the needles with failure rates equal to 0% and mean angle, axis, and tip errors less than or equal to 0.7 • , 0.6 mm, and 0.7 mm, respectively. Additionally, our method achieved favorable results compared to two recently introduced needle detection methods. Conclusions:The results indicate the potential of applying our method to achieve effective needle detection in ultrasound images.

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“…Hardware methods currently include the use of piezoelectric actuators to vibrate the needle, the use of a 3D power ultrasound to segment the vibrating bending needle, and the placement of a small ultrasonic sensor as a surgical tool to inspect the sound waves transferred in the period of the 3D ultrasound image process by moving needle as well as inspecting the decreased intensity change of ultrasound volume (7)(8)(9)(10). This existing software-based methods include the use of principle component analysis (PCA) to detect the 3D ultrasound volume needle, as well as the use of radar transformation according to the radio frequency (RF) signal obtained by the 3D ultrasound probe to locate the needle (11,12). Real-time algorithms can also track the linear surgical instrument under the 3D ultrasound volume using Parallel Integral Projection (PIP) transformation to locate the thin needle inserted into the 3D ultrasound image (13)(14)(15).…”

Section: Introductionmentioning

confidence: 99%

A 3D multi-modal intelligent intervention system using electromagnetic navigation for real-time positioning and ultrasound images: a prospective randomized controlled trial

Tang¹,

Zhou²,

Zhao³

et al. 2022

Ann Transl Med

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Background: Anesthesia, nerve block, therapeutic injections, and biopsies all require an acupuncture intervention. However, traditional two-dimensional (2D) ultrasound-guided needle puncture is often challenging and therefore requires the use of three-dimensional (3D) ultrasound images to accurately identify and evaluate the patient's anatomical structure.Methods: In this study, a 3D multi-modal intelligent intervention system using electromagnetic navigation for real-time positioning and ultrasound images was described. A total of 190 cases requiring puncture were randomly divided into control (conventional 2D ultrasound instrument) and experimental (novel 3D ultrasound imedis9000) groups. The advantages and disadvantages of the two puncture methods were prospectively analyzed in the 190 cases, and the feasibility of electromagnetic navigation real-time positioning was compared to ultrasound imaging.Results: This study included 190 cases from two centers that required puncture treatment and were randomly assigned to the control (conventional 2D ultrasound instrument; n=95) or the experimental (novel 3D ultrasound imedis9000; n=95) groups. Percutaneous vascular puncture, percutaneous biopsy, percutaneous bile duct puncture, thoracic paravertebral nerve block, and sciatic nerve block operations were performed separately. The results indicated that the puncture time and number of trials in the experimental group were significantly lower than those in the control group. No significant difference was identified in the basic vital signs between the two groups before and after surgery. The success rate of the novel 3D ultrasound imedis9000 was 100%, and the success rate of the conventional 2D ultrasound instrument was 95.7%. Furthermore, the results also showed that the novel 3D ultrasound imedis9000 and the matching

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Reliable and accurate needle localization in curvilinear ultrasound images using signature‐based analysis of ultrasound beamformed radio frequency signals

Cited by 6 publications

References 35 publications

Bridging the simulation-to-real gap for AI-based needle and target detection in robot-assisted ultrasound-guided interventions

Bridging the simulation-to-real gap for AI-based needle and target detection in robot-assisted ultrasound-guided interventions

Needle detection using ultrasound B‐mode and power Doppler analyses

A 3D multi-modal intelligent intervention system using electromagnetic navigation for real-time positioning and ultrasound images: a prospective randomized controlled trial

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