A survey of catheter tracking concepts and methodologies

Ramadani, Ardit; Bui, Mai; Wendler, Thomas; Schunkert, Heribert; Ewert, Peter; Navab, Nassir

doi:10.1016/j.media.2022.102584

Cited by 26 publications

(14 citation statements)

References 116 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy requirements vary depending on the specific procedure. However, it is generally agreed that tracking errors at millimetre and degree levels are sufficient for most endoscopic and surgical navigation tasks [ 13 – 15 ].…”

Section: Discussionmentioning

confidence: 99%

Enhancing electromagnetic tracking accuracy in medical applications using pre-trained witness sensor distortion models

Cavaliere

Cantillon-Murphy

2023

Int J CARS

View full text Add to dashboard Cite

Purpose Electromagnetic tracking (EMT) accuracy is affected by the presence of surrounding metallic materials. In this work, we propose measuring the magnetic field's variation due to distortion at a witness position to localise the instrument causing distortion based on a pre-trained model and without additional sensors attached to it. Methods Two experiments were performed to demonstrate possible applications of the technique proposed. In the first case, the distortion introduced by an ultrasound (US) probe was characterised and subsequently used to track the probe position on a line. In the second application, the measurement was used to estimate the distance of an interventional fluoroscopy C-arm machine and apply the correct compensation model. Results Tracking of the US probe using the proposed method was demonstrated with millimetric accuracy. The distortion created by the C-arm caused errors in the order of centimetres, which were reduced to 1.52 mm RMS after compensation. Conclusions The distortion profile associated with medical equipment was pre-characterised and used in applications such as object tracking and error compensation map selection. In the current study, the movement was limited to one degree of freedom (1 DOF) and simple analytical functions were used to model the magnetic distortion. Future work will explore advanced AI models to extend the method to 6 DOF tracking using multiple witness sensors.

show abstract

Section: Discussionmentioning

confidence: 99%

Enhancing electromagnetic tracking accuracy in medical applications using pre-trained witness sensor distortion models

Cavaliere

Cantillon-Murphy

2023

Int J CARS

View full text Add to dashboard Cite

show abstract

“…Their main technologies can be divided into three parts: environmental perception [ 1 ], path planning [ 2 ], and decision-making control [ 3 ]. As a basic work in the environment perception of intelligent vehicles, multitarget tracking [ 4 ] is of great significance for ensuring the safety of autonomous vehicles and improving the ability of intelligent vehicles to understand the environment.…”

Section: Introductionmentioning

confidence: 99%

Multitarget-Tracking Method Based on the Fusion of Millimeter-Wave Radar and LiDAR Sensor Information for Autonomous Vehicles

Shi

Tang

Gao

et al. 2023

Sensors

View full text Add to dashboard Cite

Multitarget tracking based on multisensor fusion perception is one of the key technologies to realize the intelligent driving of automobiles and has become a research hotspot in the field of intelligent driving. However, most current autonomous-vehicle target-tracking methods based on the fusion of millimeter-wave radar and lidar information struggle to guarantee accuracy and reliability in the measured data, and cannot effectively solve the multitarget-tracking problem in complex scenes. In view of this, based on the distributed multisensor multitarget tracking (DMMT) system, this paper proposes a multitarget-tracking method for autonomous vehicles that comprehensively considers key technologies such as target tracking, sensor registration, track association, and data fusion based on millimeter-wave radar and lidar. First, a single-sensor multitarget-tracking method suitable for millimeter-wave radar and lidar is proposed to form the respective target tracks; second, the Kalman filter temporal registration method and the residual bias estimation spatial registration method are used to realize the temporal and spatial registration of millimeter-wave radar and lidar data; third, use the sequential m-best method based on the new target density to find the track the correlation of different sensors; and finally, the IF heterogeneous sensor fusion algorithm is used to optimally combine the track information provided by millimeter-wave radar and lidar, and finally form a stable and high-precision global track. In order to verify the proposed method, a multitarget-tracking simulation verification in a high-speed scene is carried out. The results show that the multitarget-tracking method proposed in this paper can realize the track tracking of multiple target vehicles in high-speed driving scenarios. Compared with a single-radar tracker, the position, velocity, size, and direction estimation errors of the track fusion tracker are reduced by 85.5%, 64.6%, 75.3%, and 9.5% respectively, and the average value of GOSPA indicators is reduced by 19.8%; more accurate target state information can be obtained than a single-radar tracker.

show abstract

“…There has been work involving guidewire tracking, usually used in combination with catheter tracking in related studies. Researchers indicate that many frameworks used for catheter tracking can also be applied to guidewire tracking [58]. Traditional methods in endovascular and neurosurgery fields have guided researchers to use either intensityand learning-based models or the segmentation and movement of the guidewire [58][59][60][61][62].…”

Section: Introductionmentioning

confidence: 99%

“…Researchers indicate that many frameworks used for catheter tracking can also be applied to guidewire tracking [58]. Traditional methods in endovascular and neurosurgery fields have guided researchers to use either intensityand learning-based models or the segmentation and movement of the guidewire [58][59][60][61][62]. However, with emergent segmentation and deep learning techniques, new approaches have taken a turn in this direction.…”

Section: Introductionmentioning

confidence: 99%

A Coordinate-Regression-Based Deep Learning Model for Catheter Detection during Structural Heart Interventions

Aghasizade,

Kiyoumarsioskouei,

Hashemi

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

With a growing geriatric population estimated to triple by 2050, minimally invasive procedures that are image-guided are becoming both more popular and necessary for treating a variety of diseases. To lower the learning curve for new procedures, it is necessary to develop better guidance systems and methods to analyze procedure performance. Since fluoroscopy remains the primary mode of visualizations, the ability to perform catheter tracking from fluoroscopic images is an important part of this endeavor. This paper explores the use of deep learning to perform the landmark detection of a catheter from fluoroscopic images in 3D-printed heart models. We show that a two-stage deep-convolutional-neural-network-based model architecture can provide improved performance by initially locating a region of interest before determining the coordinates of the catheter tip within the image. This model has an average error of less than 2% of the image resolution and can be performed within 4 milliseconds, allowing for its potential use for real-time intraprocedural tracking. Coordinate regression models have the advantage of directly outputting values that can be used for quantitative tracking in future applications and are easier to create ground truth values (~50× faster), as compared to semantic segmentation models that require entire masks to be made. Therefore, we believe this work has better long-term potential to be used for a broader class of cardiac devices, catheters, and guidewires.

show abstract

A survey of catheter tracking concepts and methodologies

Cited by 26 publications

References 116 publications

Enhancing electromagnetic tracking accuracy in medical applications using pre-trained witness sensor distortion models

Enhancing electromagnetic tracking accuracy in medical applications using pre-trained witness sensor distortion models

Multitarget-Tracking Method Based on the Fusion of Millimeter-Wave Radar and LiDAR Sensor Information for Autonomous Vehicles

A Coordinate-Regression-Based Deep Learning Model for Catheter Detection during Structural Heart Interventions

Contact Info

Product

Resources

About