Ultrasound Assisted Guidance with Force Cues for Intravascular Interventions

Project Goals


Image guidance during minimally invasive cardiovascular interventions is primarily achieved based on X-ray fluoroscopy, which has several limitations including limited 3D imaging, significant doses of radiation to operators, and lack of contact force measurement between the cardiovascular anatomy and interventional tools. Ultrasound imaging may complement or possibly replace 2D fluoroscopy for intravascular interventions due to its portability, safety, and the ability of providing depth information. However, it is a challenging work to perfectly visualize catheters and guidewires in the ultrasound images. In this paper, we developed a novel method to locate the position and orientation of the catheter tip in 2D ultrasound images in real time by detecting and tracking a passive marker attached to the catheter tip. Moreover, the contact force can also be measured due to the length variation of the marker in real time. An active geometrical structure model based method was proposed to detect the initial position of the marker, and a KLT (Kanade-Lucas-Tomasi) based algorithm was developed to track the position, orientation, and the length of the marker. The ex vivo experimental results indicate that the proposed method is able to automatically locate the catheter tip in the ultrasound images and sense the contact force, so as to facilitate the operators’ work during intravascular interventions.

Approaches/Results/Video

People Involved

Research Fellow: Jin Guo
Project Investigator: Hongliang Ren

Related Publications

TBA

Tracking Magnetic Particles under Ultrasound Imaging using Contrast-Enhancing Microbubbles

Abstract

Magnetic microbubbles which can be controlled by an external magnetic field have been explored as a method for precise and efficient drug delivery. In this paper, a technique for the fabrication of microbubble encapsulated magnetic spheres is presented. The resultant magnetic spheres were subsequently imaged using ultrasound and the encapsulated microbubbles proved to appear as bright spots and resulted in enhanced ultrasound image contrast, as compared to the solid magnetic spheres which appeared dull. A tracking algorithm was then developed for the tracking of the magnetic microbubbles based on optical flow tracking. Further development of the magnetic microbubbles and tracking algorithm can lead to future use of the tracking algorithm in the case of in vivo injection of the magnetic microbubbles.

Publications

1. Loh Kai Ting, Ren Hongliang and Li Jun, Tracking Magnetic Particles under Ultrasound Imaging using Contrast-Enhancing Microbubbles, The 11th Asian Conference on Computer Aided Surgery, 2015.

Poster in BME Showcase 2015

KT Poster Final Printed

Surgical Tracking Based on Stereo Vision and Depth Sensing

Project Goals:

The objective of this research is to incorporate multiple sensors at broad spectrum, including stereo infrared (IR) cameras, color (or RGB) cameras and depth sensors to perceive the surgical environment. Features extracted from each modality can contribute to the cognition of complex surgical environment or procedures. Additionally, their combination can provide higher robustness and accuracy beyond what is obtained from single sensing modality. As a preliminary study, we propose a multi-sensor fusion approach for localizing surgical instruments. We developed an integrated dual Kinect tracking system to validate the proposed hierarchical tracking approach.

Approaches:

This project considers the problem of improving the surgical instrument tracking accuracy by multi-sensor fusion technique in computer vision. We proposed a hierarchical fusion algorithm for integrating the tracking results from depth sensor, IR camera pair and RGB camera pair. Fig. 1 summarized the algorithm involved in this project. It can be divided into the “low-level” and the “high-level” fusion.

Fig. 1 Block diagram of hierarchical fusion algorithm.


The low-level fusion is to improve the speed and robustness of marker feature extraction before triangulating the tool tip position in IR and RGB camera pair. The IR and RGB camera are modeled as pin-hole cameras.  The depth data of the tool can be used as a priori for marker detection. The working area of the tracking tool is supposed to be limited in a reasonable volume v(x, y, z) that can be used to refine the search area for feature extraction, which could reduce the computational cost for real-time applications.
The high-level fusion is to reach a highly accurate tracking result by fusing two measurements. We employ the covariance intersection (CI) algorithm to estimate a new tracking result with less covariance.

Experiments

To demonstrate the proposed algorithm, we designed a hybrid marker-based tracking tool (Fig. 2) that incorporates the cross-based feature in visible modality and retro-reflective marker based feature in infra-red modality to get a fused tracking of the customized tool tip. To evaluate the performance of the proposed method, we employ two Kinects to build the experimental setup. Fig. 3 shows the prototype of multi-sensor fusion tracker for the experiment, which indicates that the CI-based fusion approaches obviously tend to be better than the separate IR tracker or RGB tracker.  The mean error and deviation of the fusion algorithm are all improved.
Hybrid marker

Fig. 3 Dual Kinect tracking system

People Involved

Staffs: Wei LIU, Shuang SONG, Andy Lim
Advisor: Dr. Hongliang Ren
Collaborator: Wei ZHANG

Publications

[1] Ren, H.; LIU, W. & LIM, A. Marker-Based Instrument Tracking Using Dual Kinect Sensors for Navigated Surgery IEEE Transactions on Automation Science and Engineering, 2013
[2] Liu, W.; Ren, H.; Zhang, W. & Song, S. Cognitive Tracking of Surgical Instruments Based on Stereo Vision and Depth Sensing, ROBIO 2013, IEEE International Conference on Robotics and Biomimetics, 2013

Related FYP Project

Andy Lim: Marker-Based Surgical Tracking With Multiple Modalities Using Microsoft Kinect

References

[1] H. Ren, D. Rank, M. Merdes, J. Stallkamp, and P. Kazanzides, “Multi-sensor data fusion in an integrated tracking system for endoscopic surgery,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 1, pp. 106 – 111, 2012.
[2] W. Liu, C. Hu, Q. He, and M.-H. Meng, “A three-dimensional visual localization system based on four inexpensive video cameras,” in Information and Automation (ICIA), 2010 IEEE International Conference on. IEEE, 2010, pp. 1065–1070.
[3] F. Faion, S. Friedberger, A. Zea, and U. D. Hanebeck, “Intelligent sensor-scheduling for multi-kinect-tracking,” in Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on. IEEE, 2012, pp. 3993–3999.

FYP: Surgical Tracking With Multiple Microsoft Kinects

FYP Project Goals

The aim of this project is to perform tracking of surgical instruments utilizing the Kinect sensors. With the advances in computing and imaging technologies in the recent years, visual limitations during surgery such as those due to poor depth perception and limited field of view, can be overcome by using computer-assisted systems. 3D models of the patient’s anatomy (obtained during pre-operative planning via Computed Tomography scans or Magnetic Resonance Imaging) can be combined with intraoperative information such as the 3D pose and orientation of surgical instruments. Such a computer-assisted system will reduce surgical mistakes and help identify unnecessary or imperfect surgical movements, effectively increasing the success rate of the surgeries.
For computer-assisted systems to work, accurate spatial information of surgical instruments is required. Most surgical tools are capable of 6 degrees of freedom (6DoF) movement, which includes the translation in the x, y, z- axes as well as the rotation about these axes. The introduction of Microsoft Kinect sensor raises the possibility of an alternative optical tracking system for surgical instruments.
This project’s objective would be the development of an optical tracking system for surgical instruments utilising the capabilities of the Kinect sensor. In this part of the project, the focus will be on marker-based tracking using the Kinect sensor.

Approach

  • The setup for the tracking of surgical instruments consists of two Kinects placed side by side with overlapping field of views.
  • The calibration board used to find out the intrinsic camera parameters as well as the relative position of the cameras. This allows us to calculate the fundamental matrix, which is essential for epipolar geometry calculations used in 3D point reconstruction. (a) without external LED illumination (b) with LED illumination. The same board is used for RGB camera calibration.
  • Seeded region growing allows the segmentation of retro-reflective markers from the duller background. The algorithm is implemented through OpenCV.
  • Corner detection algorithm: the cornerSubPix algorithm from OpenCV is used to refine the position of the corners. This results in sub-pixel accuracy of the corner position.

Current Results

  • The RMS error for IRR and checkerboard tracking ranges from 0.37 to 0.68 mm and 0.18 to 0.37 mm respectively over a range of 1.2 m. Checkerboard tracking is found to be more accurate. Error increases with distance from camera.
  • The jitter for the checkerboard tracking system was investigated and it was found to range from 0.071 mm to 0.29 mm over the range of 1.2 m.
  • (dots) Measurement of jitter plotted against the distance from the left camera. (line) the data is fitted to a polynomial of order 2 to analyze how jitter varies with depth.

People Involved

FYP Student: Andy Lim Yong Mong
Research Engineer: Liu Wei
Advisor: Dr. Ren Hongliang

Related Project

Surgical Tracking Based on Stereo Vision and Depth Sensing

References

[1] Sun, W., Yang, X., Xiao, S., & Hu, W. (2008). Robust Checkerboard Recognition for Efficient Nonplanar Geometry Registration in Projector-camera Systems. Proceedings of the 5th ACM/IEEE International Workshop on Projector camera systems. ACM.
[2] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2 ed., Cambridge: Cambridge University Press, 2003.
[3] Q. He, C. Hu, W. Liu, N. Wei, M. Q.-H. Meng, L. Liu and C. Wang, “Simple 3-D Point Reconstruction Methods With Accuracy Prediction for Multiocular System, “IEEE/ASME Transactions on Mechatronics, vol. 18, no. 1, pp. 366-375, 2013

3D Ultrasound Tracking and Servoing of Tubular Surgical Robots

Collaborators:

[Pediatric Cardiac Bioengineering Lab of Children’s Hospital Boston, Harvard Medical School, USA]
[Philips Research]

Abstract

Ultrasound imaging is a useful modality for guiding minimally invasive interventions due to its portability and safety. In cardiac surgery, for example, real-time 3D ultrasound imaging is being investigated for guiding repairs of complex defects inside the beating heart. Substantial difficulty can arise, however, when surgical instruments and tissue structures are imaged simultaneously to achieve precise manipulations. This research project includes: (1) the development of echogenic instrument coatings, (2) the design of passive instrument markers, and (3) the development of algorithms for instrument tracking and servoing. For example, a family of passive markers has been developed by which the position and orientation of a surgical instrument can be determined from a single 3D ultrasound volume using simple image processing. Marker-based estimates of instrument pose can be used in augmented reality displays or for image-based servoing.
For example, a family of passive markers has been developed by which the position and orientation of a surgical instrument can be determined from a single 3D ultrasound volume using simple image processing. Marker-based estimates of instrument pose can be used in augmented reality displays or for image-based servoing. The design principles for marker shapes ensure imaging system and measurement uniqueness constraints are met. Error analysis is used to guide marker design and to establish a lower bound on measurement uncertaintanty. Experimental evaluation of marker designs and tracking algorithms demonstrate a tracking accuracy of 0.7 mm in position and 0.075 rad in orientation.
Another example is to investigate the problem of automatic curve pattern detection from 3D ultrasound images, because many surgical instruments are curved along the distal end during operation, such as continuum tube robot, and catheter insertion etc. We propose a two-stage approach to decompose the six parameter constant-curvature curve estimation problem into a two stage parameter estimation problems: 3D spatial plane detection and 2D circular pattern detection. The algorithm includes an image-preprocessing pipeline, including thresholding, denoising, connected component analysis and skeletonization, for automatically extracting the curved robot from ultrasound volumetric images. The proposed method can also be used for spatial circular or arc pattern recognition from other volumetric images such as CT and MRI.
Additional related information at [Pediatric Cardiac Bioengineering Lab of Children’s Hospital Boston, Harvard Medical School]

Surgical Tracking System for Laparoscopic Surgery

Collaborators:
ERC-CISST, LCSR Lab of Johns Hopkins University, USA
Fraunhofer Germany (FhG)

Laparoscopic surgery poses a challenging problem for a real-time intra-body navigation system: how to keep tracking the surgical instruments inside the human body intra-operatively. This project aims to develop surgical tracking technology that is accurate, robust against environmental disturbances, and does not require line-of-sight. The current approach is to combine electromagnetic and inertial sensing. Sensor fusion methods are proposed for a hybrid tracking system that incorporates a miniature inertial measurement unit and an electromagnetic navigation system, in order to obtain continuous position and orientation information, even in the presence of metal objects.
Additional information at [SMARTS Lab of Johns Hopkins University]

Automated tracking of pallets in warehouses based on asymmetric ultrasound observation models

We consider the use of wireless sensor networks to automatically track “perceptive pallets” of materials in ware-houses for the purpose of monitoring volumetric and spatial constraints. A combination of radio frequency and ultrasound chirping produces position estimates that are noisy and prone to error. To address this, we measure and characterize the ultrasound response from standard “Cricket” wireless sensor motes and beacons. We develop a non-parametric particle filtering approach to estimate trajectories of moving motes and introduce two asymmetric observation models that incorporate measured cardioid-shaped response patterns of ultrasound.

Collaborator: Automation Lab of Professor Ken Goldberg, EECS UC Berkeley