Enhancement of needle visualization and localization in ultrasound

Automated angular measurement for puncture angle using a computer-aided method in ultrasound-guided peripheral insertion

Ultrasound guidance has become the gold standard for obtaining vascular access. Angle information, which indicates the entry angle of the needle into the vein, is required to ensure puncture success. Although various image processing-based methods, such as deep learning, have recently been applied to improve needle visibility, these methods have limitations, in that the puncture angle to the target organ is not measured. We aim to detect the target vessel and puncture needle and to derive the puncture angle by combining deep learning and conventional image processing methods such as the Hough transform. Median cubital vein US images were obtained from 20 healthy volunteers, and images of simulated blood vessels and needles were obtained during the puncture of a simulated blood vessel in four phantoms. The U-Net architecture was used to segment images of blood vessels and needles, and various image processing methods were employed to automatically measure angles. The experimental results indicated that the mean dice coefficients of median cubital veins, simulated blood vessels, and needles were 0.826, 0.931, and 0.773, respectively. The quantitative results of angular measurement showed good agreement between the expert and automatic measurements of the puncture angle with 0.847 correlations. Our findings indicate that the proposed method achieves extremely high segmentation accuracy and automated angular measurements. The proposed method reduces the variability and time required in manual angle measurements and presents the possibility where the operator can concentrate on delicate techniques related to the direction of the needle.

Ultrasound and Photoacoustic Imaging for the Guidance of Laser Ablation Procedures

The accuracy and efficacy of laser ablation procedures depend on the accurate placement of the laser applicator within the diseased tissue, monitoring the real-time temperature during the ablation procedure, and mapping the extent of the ablated region. Ultrasound (US) imaging has been widely used to guide ablation procedures. While US imaging offers significant advantages for guiding ablation procedures, its limitations include low imaging contrast, angular dependency, and limited ability to monitor the temperature. Photoacoustic (PA) imaging is a relatively new imaging modality that inherits the advantages of US imaging and offers enhanced capabilities for laser-guided ablations, such as accurate, angle-independent tracking of ablation catheters, the potential for quantitative thermometry, and monitoring thermal lesion formation. This work provides an overview of ultrasound-guided procedures and how different US-related artifacts limit their utility, followed by introducing PA as complementary to US as a solution to address the existing limitations and improve ablation outcomes. Furthermore, we highlight the integration of PA-driven features into existing US-guided laser ablation systems, along with their limitations and future outlooks. Integrated US/PA-guided laser ablation procedures can lead to safer and more precise treatment outcomes.

Mixed Reality Biopsy Navigation System Utilizing Markerless Needle Tracking and Imaging Data Superimposition

Exact biopsy planning and careful execution of needle injection is crucial to ensure successful procedure completion as initially intended while minimizing the risk of complications. This study introduces a solution aimed at helping the operator navigate to precisely position the needle in a previously planned trajectory utilizing a mixed reality headset. A markerless needle tracking method was developed by integrating deep learning and deterministic computer vision techniques. The system is based on superimposing imaging data onto the patient's body in order to directly perceive the anatomy and determine a path from the selected injection site to the target location. Four types of tests were conducted to assess the system's performance: measuring the accuracy of needle pose estimation, determining the distance between injection sites and designated targets, evaluating the efficiency of material collection, and comparing procedure time and number of punctures required with and without the system. These tests, involving both phantoms and physician participation in the latter two, demonstrated the accuracy and usability of the proposed solution. The results showcased a significant improvement, with a reduction in number of punctures needed to reach the target location. The test was successfully completed on the first attempt in 70% of cases, as opposed to only 20% without the system. Additionally, there was a 53% reduction in procedure time, validating the effectiveness of the system.

Ultrasound-guided needle tracking with deep learning: A novel approach with photoacoustic ground truth

Accurate needle guidance is crucial for safe and effective clinical diagnosis and treatment procedures. Conventional ultrasound (US)-guided needle insertion often encounters challenges in consistency and precisely visualizing the needle, necessitating the development of reliable methods to track the needle. As a powerful tool in image processing, deep learning has shown promise for enhancing needle visibility in US images, although its dependence on manual annotation or simulated data as ground truth can lead to potential bias or difficulties in generalizing to real US images. Photoacoustic (PA) imaging has demonstrated its capability for high-contrast needle visualization. In this study, we explore the potential of PA imaging as a reliable ground truth for deep learning network training without the need for expert annotation. Our network (UIU-Net), trained on ex vivo tissue image datasets, has shown remarkable precision in localizing needles within US images. The evaluation of needle segmentation performance extends across previously unseen ex vivo data and in vivo human data (collected from an open-source data repository). Specifically, for human data, the Modified Hausdorff Distance (MHD) value stands at approximately 3.73, and the targeting error value is around 2.03, indicating the strong similarity and small needle orientation deviation between the predicted needle and actual needle location. A key advantage of our method is its applicability beyond US images captured from specific imaging systems, extending to images from other US imaging systems.

Enhanced Needle Visualization With Reflection Tuned Apodization Based on the Radon Transform for Ultrasound Imaging

In ultrasound (US)-guided interventions, accurately tracking and visualizing needles during in-plane insertions are significant challenges due to strong directional specular reflections. These reflections violate the geometrical delay and apodization estimations in the conventional delay and sum beamforming (DASB) degrading the visualization of needles. This study proposes a novel reflection tuned apodization (RTA) to address this issue and facilitate needle enhancement through DASB. The method leverages both temporal and angular information derived from the Radon transforms of the radio frequency (RF) data from plane-wave imaging to filter the specular reflections from the needle and their directivity. The directivity information is translated into apodization center maps through time-to-space mapping in the Radon domain, which is subsequently integrated into DASB. We assess the influence of needle angulations, projection angles in the Radon transform, needle gauge sizes, and the presence of multiple specular interfaces on the approach. The analysis shows that the method surpasses conventional DASB in enhancing the image quality of needle interfaces while preserving the diffuse scattering from the surrounding tissues without significant computational overhead. The work offers promising prospects for improved outcomes in US-guided interventions and better insights into characterizing US reflections with Radon transforms.

Handheld interventional ultrasound/photoacoustic puncture needle navigation based on deep learning segmentation

Interventional ultrasound (US) has challenges in accurate localization of the puncture needle due to intrinsic acoustic interferences, which lead to blurred, indistinct, and even invisible needles in handheld linear array transducer-based US navigation, especially the incorrect needle tip positioning. Photoacoustic (PA) imaging can provide complementary image contrast, without additional data acquisition. Herein, we proposed an internal illumination to solely light up the needle tip in PA imaging. Then deep-learning-based feature segmentation alleviates acoustic interferences, enhancing the needle shaft-tip visibility. Further, needle shaft-tip compensation aligned the needle shaft in US image and the needle tip in the PA image. The experiments on phantom, ex vivo chicken breast, preclinical radiofrequency ablation and in vivo biopsy of sentinel lymph nodes were piloted. The target registration error can reach the submillimeter level, achieving precise puncture needle tracking ability with in-plane US/PA navigation.

Model-Based Needle Identification Using Image Analysis and Needle Library Matching for Ultrasound-Guided Kidney Biopsy: A Feasibility Study

OBJECTIVE

The aim of the work described here was to determine the feasibility of using a novel biopsy needle detection technique that achieves high sensitivity and specificity in a trade-off of resolution, detectability and depth of imaging.

METHODS

The proposed needle detection method consists of a model-based image analysis, temporal needle projection and needle library matching: (i) Image analysis was formulated under the signal decomposition framework; (ii) temporal projection converted the time-resolved needle dynamics into a single image of the desired needle; and (iii) the enhanced needle structure was spatially refined by matching a long, straight linear object in the needle library. The efficacy was examined with respect to different needle visibility.

RESULTS

Our method effectively eliminated confounding effects of the background tissue artifacts more robustly than conventional methods, thus improving needle visibility even with the low contrast between the needle and tissue. The improvement in needle structure further resulted in an improvement in estimation performance for the trajectory angle and tip position.

CONCLUSION

Our three-step needle detection method can reliably detect needle position without the need for external devices, increasing the needle conspicuity and reducing motion sensitivity.

Bend-insensitive fiber optic ultrasonic tracking probe for cardiovascular interventions

BACKGROUND

Transesophageal echocardiography (TEE) is widely used to guide medical device placement in minimally invasive cardiovascular procedures. However, visualization of the device tip with TEE can be challenging. Ultrasonic tracking, enabled by an integrated fiber optic ultrasound sensor (FOUS) that receives transmissions from the TEE probe, is very well suited to improving device localization in this context. The problem addressed in this study is that tight deflections of devices such as a steerable guide catheter can result in bending of the FOUS beyond its specifications and a corresponding loss of ultrasound sensitivity.

PURPOSE

A bend-insensitive FOUS was developed, and its utility with ultrasonic tracking of a steerable tip during TEE-based image guidance was demonstrated.

METHODS

Fiberoptic ultrasound sensors were fabricated using both standard and bend insensitive single mode fibers and subjected to static bending at the distal end. The interference transfer function and ultrasound sensitivities were compared for both types of FOUS. The bend-insensitive FOUS was integrated within a steerable guide catheter, which served as an exemplar device; the signal-to-noise ratio (SNR) of tracking signals from the catheter tip with a straight and a fully deflected distal end were measured in a cardiac ultrasound phantom for over 100 frames.

RESULTS

With tight bending at the distal end (bend radius < 10 mm), the standard FOUS experienced a complete loss of US sensitivity due to high attenuation in the fiber, whereas the bend-insensitive FOUS had largely unchanged performance, with a SNR of 47.7 for straight fiber and a SNR of 36.8 at a bend radius of 3.0 mm. When integrated into the steerable guide catheter, the mean SNRs of the ultrasonic tracking signals recorded with the catheter in a cardiac phantom were similar for straight and fully deflected distal ends: 195 and 163.

CONCLUSION

The FOUS fabricated from bend-insensitive fiber overcomes the bend restrictions associated with the FOUS fabricated from standard single mode fiber, thereby enabling its use in ultrasonic tracking in a wide range of cardiovascular devices.

On the physics of ultrasound transmission for in-plane needle tracking in guided interventions

Objective.In ultrasound (US) guided interventions, the accurate visualization and tracking of needles is a critical challenge, particularly during in-plane insertions. An inaccurate identification and localization of needles lead to severe inadvertent complications and increased procedure times. This is due to the inherent specular reflections from the needle with directivity depending on the angle of incidence of the US beam, and the needle inclination.Approach.Though several methods have been proposed for improved needle visualization, a detailed study emphasizing the physics of specular reflections resulting from the interaction of transmitted US beam with the needle remains to be explored. In this work, we discuss the properties of specular reflections from planar and spherical wave US transmissions respectively through multi-angle plane wave (PW) and synthetic transmit aperture (STA) techniques for in-plane needle insertion angles between 15°-50°.Main Results.The qualitative and quantitative results from simulations and experiments reveal that the spherical waves enable better visualization and characterization of needles than planar wavefronts. The needle visibility in PW transmissions is severely degraded by the receive aperture weighting during image reconstruction than STA due to greater deviation in reflection directivity. It is also observed that the spherical wave characteristics starts to alter to planar characteristics due to wave divergence at large needle insertion depths.Significance.The study highlights that synergistic transmit-receive imaging schemes addressing the physical properties of reflections from the transmit wavefronts are imperative for the precise imaging of needle interfaces and hence have strong potential in elevating the quality of outcomes from US guided interventional practices.

Enhancement of instrumented ultrasonic tracking images using deep learning

PURPOSE

Instrumented ultrasonic tracking provides needle localisation during ultrasound-guided minimally invasive percutaneous procedures. Here, a post-processing framework based on a convolutional neural network (CNN) is proposed to improve the spatial resolution of ultrasonic tracking images.

METHODS

The custom ultrasonic tracking system comprised a needle with an integrated fibre-optic ultrasound (US) transmitter and a clinical US probe for receiving those transmissions and for acquiring B-mode US images. For post-processing of tracking images reconstructed from the received fibre-optic US transmissions, a recently-developed framework based on ResNet architecture, trained with a purely synthetic dataset, was employed. A preliminary evaluation of this framework was performed with data acquired from needle insertions in the heart of a fetal sheep in vivo. The axial and lateral spatial resolution of the tracking images were used as performance metrics of the trained network.

RESULTS

Application of the CNN yielded improvements in the spatial resolution of the tracking images. In three needle insertions, in which the tip depth ranged from 23.9 to 38.4 mm, the lateral resolution improved from 2.11 to 1.58 mm, and the axial resolution improved from 1.29 to 0.46 mm.

CONCLUSION

The results provide strong indications of the potential of CNNs to improve the spatial resolution of ultrasonic tracking images and thereby to increase the accuracy of needle tip localisation. These improvements could have broad applicability and impact across multiple clinical fields, which could lead to improvements in procedural efficiency and reductions in risk of complications.

Intraoperative Needle Tip Tracking with an Integrated Fibre-Optic Ultrasound Sensor

Ultrasound is an essential tool for guidance of many minimally-invasive surgical and interventional procedures, where accurate placement of the interventional device is critical to avoid adverse events. Needle insertion procedures for anaesthesia, fetal medicine and tumour biopsy are commonly ultrasound-guided, and misplacement of the needle may lead to complications such as nerve damage, organ injury or pregnancy loss. Clear visibility of the needle tip is therefore critical, but visibility is often precluded by tissue heterogeneities or specular reflections from the needle shaft. This paper presents the in vitro and ex vivo accuracy of a new, real-time, ultrasound needle tip tracking system for guidance of fetal interventions. A fibre-optic, Fabry-Pérot interferometer hydrophone is integrated into an intraoperative needle and used to localise the needle tip within a handheld ultrasound field. While previous, related work has been based on research ultrasound systems with bespoke transmission sequences, the new system-developed under the ISO 13485 Medical Devices quality standard-operates as an adjunct to a commercial ultrasound imaging system and therefore provides the image quality expected in the clinic, superimposing a cross-hair onto the ultrasound image at the needle tip position. Tracking accuracy was determined by translating the needle tip to 356 known positions in the ultrasound field of view in a tank of water, and by comparison to manual labelling of the the position of the needle in B-mode US images during an insertion into an ex vivo phantom. In water, the mean distance between tracked and true positions was 0.7 ± 0.4 mm with a mean repeatability of 0.3 ± 0.2 mm. In the tissue phantom, the mean distance between tracked and labelled positions was 1.1 ± 0.7 mm. Tracking performance was found to be independent of needle angle. The study demonstrates the performance and clinical compatibility of ultrasound needle tracking, an essential step towards a first-in-human study.

Recent Advances in Tracking Devices for Biomedical Ultrasound Imaging Applications

With the rapid advancement of tracking technologies, the applications of tracking systems in ultrasound imaging have expanded across a wide range of fields. In this review article, we discuss the basic tracking principles, system components, performance analyses, as well as the main sources of error for popular tracking technologies that are utilized in ultrasound imaging. In light of the growing demand for object tracking, this article explores both the potential and challenges associated with different tracking technologies applied to various ultrasound imaging applications, including freehand 3D ultrasound imaging, ultrasound image fusion, ultrasound-guided intervention and treatment. Recent development in tracking technology has led to increased accuracy and intuitiveness of ultrasound imaging and navigation with less reliance on operator skills, thereby benefiting the medical diagnosis and treatment. Although commercially available tracking systems are capable of achieving sub-millimeter resolution for positional tracking and sub-degree resolution for orientational tracking, such systems are subject to a number of disadvantages, including high costs and time-consuming calibration procedures. While some emerging tracking technologies are still in the research stage, their potentials have been demonstrated in terms of the compactness, light weight, and easy integration with existing standard or portable ultrasound machines.

A sensorized needle guide for ultrasound assisted breast biopsy

One in every eight women will get breast cancer during their lifetime. Therefore, the early diagnosis of the lesions is fundamental to improve the chances of recovery. To find breast cancers, breast screening using techniques such as mammography and ultrasound (US) imaging scans are often used. When a lesion is found, a breast biopsy is performed to extract a tissue sample for analysis. The breast biopsy is usually assisted by an US to help find the lesion and guide the needle to its location. However, the identification of the needle tip in US image is challenging, possibly resulting in puncture failures. In this paper, we intend to study the potential of a sensorized needle guide system that provides information about the needle angle and displacement in respect to the US probe. Laboratory tests were initially conducted to evaluate the accuracy of each sensor in controlled conditions. After, a practical experiment with the US probe, working as a proof of concept, was performed. The angle sensor showed a root mean square error (RMSE) of 0.48 degrees and the displacement sensor showed a RMSE of 0.26mm after being calibrated. For the US probe tests, the displacement sensor shows high errors in the range of 1.19mm to 2.05mm due to mechanical reasons. Overall, the proposed system showed its potential to be used to accurately estimate needle tip localization throughout breast biopsies guided by US, corroborating its potential clinical application. Clinical relevance - Potential for clinical application where precise needle localization in ultrasound image is required.

Needle detection using ultrasound B-mode and power Doppler analyses

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 are 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. This article is protected by copyright. All rights reserved.

Neural Network Kalman Filtering for 3-D Object Tracking From Linear Array Ultrasound Data

Many interventional surgical procedures rely on medical imaging to visualize and track instruments. Such imaging methods not only need to be real time capable but also provide accurate and robust positional information. In ultrasound (US) applications, typically, only 2-D data from a linear array are available, and as such, obtaining accurate positional estimation in three dimensions is nontrivial. In this work, we first train a neural network, using realistic synthetic training data, to estimate the out-of-plane offset of an object with the associated axial aberration in the reconstructed US image. The obtained estimate is then combined with a Kalman filtering approach that utilizes positioning estimates obtained in previous time frames to improve localization robustness and reduce the impact of measurement noise. The accuracy of the proposed method is evaluated using simulations, and its practical applicability is demonstrated on experimental data obtained using a novel optical US imaging setup. Accurate and robust positional information is provided in real time. Axial and lateral coordinates for out-of-plane objects are estimated with a mean error of 0.1 mm for simulated data and a mean error of 0.2 mm for experimental data. The 3-D localization is most accurate for elevational distances larger than 1 mm, with a maximum distance of 6 mm considered for a 25-mm aperture.

Ultrasonic Needle Tracking with Dynamic Electronic Focusing

Accurate identification of the needle tip is a key challenge with ultrasound-guided percutaneous interventions in regional anaesthesia, foetal surgery and cardiovascular medicine. In this study, we developed an ultrasonic needle tracking system in which the measured needle tip location was used to set the electronic focus of the external ultrasound imaging probe. In this system, needle tip tracking was enabled with a fibre-optic ultrasound sensor that was integrated into a needle stylet, and the A-lines recorded by the sensor were processed to generate tracking images of the needle tip. The needle tip position was estimated from the tracking images. The dependency of the tracking image on the electronic focal depth of the external ultrasound imaging probe was studied in a water bath and with needle insertions into a clinical training phantom. The variability in the estimated tracked position of the needle tip, with the needle tip at fixed depths in the imaging plane across a depth range from 0.5 to 7.5 cm, was studied. When the electronic focus was fixed, the variability of tracked position was found to increase with distance from that focus. The variability with the fixed focus was found to depend on the the relative distance between the needle tip and focal depth. It was found that with dynamic focusing, the maximum variability of tracked position was below 0.31 mm, as compared with 3.97 mm for a fixed focus.

Ultrasound-Guided Musculoskeletal Injections

Musculoskeletal injections serve a variety of diagnostic and therapeutic purposes, with ultrasonography (US) guidance having many advantages: no ionizing radiation, real-time guidance, high spatial resolution, excellent soft tissue contrast, and the ability to identify and avoid critical structures. Sonography can be cost effective and afford flexibility in resource-constrained settings. This article describes US-guided musculoskeletal injections relevant to many radiology practices and provides experience-based suggestions. Structures covered include multiple joints (shoulder, hip), bursae (iliopsoas, subacromial-subdeltoid, greater trochanteric), peripheral nerves (sciatic, radial), and tendon sheaths (posterior tibial, peroneal, flexor hallucis longus, Achilles, long head of the biceps). Trigger point and similar targeted steroid injections, as well as calcific tendinopathy barbotage, are also described.

Peripheral Neuropathies Seen by Ultrasound: A Literature Analysis through Lexical Evaluation, Geographical Assessment and Graph Theory

(1) Background: Ultrasound is a well-known tool used for the diagnosis and management of many diseases, including peripheral neuropathies. The main aim of this study was the lexical analysis of the literature on this topic considering the most cited words and the relationship between the words and the papers. Furthermore, a geographical analysis was performed to evaluate the worldwide prevalence. (2) Methods: We performed a literature search on PubMed, and we calculated the occurrence of the words indicating nerves and the body parts. Furthermore, we calculated the number of papers for each country, considering the affiliation of the first author. Finally, to describe the relationships between the words and the papers, we used the 30 most cited words, and we built a matrix describing in which papers a word was cited. This matrix was used to create a network based on the graph theory using Gephi 0.9.2 software. (3) Results: The most cited nerves were median and ulnar ones, and the most cited body parts were hand, wrist and elbow. The United States of America was the most productive country, with 80 papers. The graph of the network showed the importance of ultrasound as support for therapy. (4) Conclusions: The study represents a lexical analysis of the literature and shows information about subjects, authors and relationships of the papers. This may be helpful for better understanding and evaluation of the situation of the current literature.