[Pediatric Cardiac Bioengineering Lab of Children’s Hospital Boston, Harvard Medical School, USA]
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]
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]
We study a typical heterogeneous network, a Wireless Biomedical Sensor Network (WBSN), as it consists of various types of biosensors to monitor different physiological parameters. WBSN will help to enhance medical services with its unique advantages in long-term monitoring, easy network deployment, wireless connections, and ambulatory capabilities. The network protocol plays an important role in carrying out the medical and healthcare services. Many unique challenges exist in WBSN design for medical and healthcare services, including extensive optimization problems in network protocol design to deal with power scheduling and radiation absorption concerns. Concerning these issues, we present a systematic solution to the wireless biomedical sensor network in our project named MediMesh. We develop a prototypical test-bed for medical and healthcare applications and evaluate the radiation absorption effects and efficiency. A lightweight network protocol is proposed, taking into consideration of the radiation absorption effects and communication overhead. After data acquisition from the sink stations, a data publishing system based on web service technology is implemented for typical medical and healthcare monitoring services in a hospital or home environment.
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