Global registration of ultrasound to MRI using the LC2 metric for enabling neurosurgical guidance.

Automatic and robust registration of pre-operative magnetic resonance imaging (MRI) and intra-operative ultrasound (US) is essential to neurosurgery. We reformulate and extend an approach which uses a Linear Correlation of Linear Combination (LC2)-based similarity metric, yielding a novel algorithm which allows for fully automatic US-MRI registration in the matter of seconds. It is invariant with respect to the unknown and locally varying relationship between US image intensities and both MRI intensity and its gradient.

Image-based tracking of the teeth for orthodontic augmented reality.

We present image-based methods for tracking teeth in a video image with respect to a CT scan of the jaw, in order to enable a novel light-weight augmented reality (AR) system in orthodontistry. Its purpose is guided bracket placement in orthodontic correction. In this context, our goal is to determine the position of the patient maxilla and mandible in a video image solely based on a CT scan.

Detecting patient motion in projection space for cone-beam computed tomography.

Cone-beam X-Ray systems strictly depend on the imaged object being stationary over the entire acquisition process. Even slight patient motion can affect the quality of the final 3D reconstruction. It would be desirable to be able to discover and model patient motion right from the actual projection images, in order to take it into account during reconstruction.

Estimating radiation exposure in interventional environments.

In the last decade the use of interventional X-ray imaging, especially for fluoroscopy-guided procedures, has increased dramatically. Due to this the radiation exposure of the medical staff has also increased. Although radiation protection measures such as lead vests are used there are still unprotected regions, most notably the hands and the head.

Real-time 3D reconstruction for collision avoidance in interventional environments.

With the increased presence of automated devices such as C-arms and medical robots and the introduction of a multitude of surgical tools, navigation systems and patient monitoring devices, collision avoidance has become an issue of practical value in interventional environments. In this paper, we present a real-time 3D reconstruction system for interventional environments which aims at predicting collisions by building a 3D representation of all the objects in the room. The 3D reconstruction is used to determine whether other objects are in the working volume of the device and to alert the medical staff before a collision occurs.