We have investigated improvements to PET-MR image registration offered by PET-CT scanning. Ten subjects with suspected soft-tissue sarcomas were scanned with an in-line PET-CT and a clinical MR scanner. PET to CT, CT to MR and PET to MR image registrations were performed using a rigid-body external marker technique and rigid and non-rigid voxel-similarity algorithms. PET-MR registration was als...

Robots are usually equipped with 3D range sensors such as laser line scanners (LLSs) or lidars. These acquire a full scan in by manner while the robot is motion. All lines can be referred to common coordinate frame using data from inertial sensors. However, errors noisy measurements and inaccuracies extrinsic parameters between scanner also projected onto shared frame. This causes deformation f...

Rigid registration is usually performed as an optimization procedure that searches for an image transformation that gives best similarity between the registered images. Similarity is used as a measure of image correspondence. In this work we present an implementation of rigid multimodality registration based on point similarity measures, which were developed for non-rigid registration tasks. Th...

Image registration is finding increased clinical use both in aiding diagnosis and guiding therapy. There are numerous algorithms for registration, which all involve maximizing a measure of similarity between a transformed floating image and a fixed reference image. The choice of the similarity measure depends, to some extent, on the application. Methods based on the use of the joint intensity h...

This paper describes a framework for learning a statistical model of non-rigid deformations induced by interventional procedures. We make use of this learned model to perform constrained non-rigid registration of pre-procedural and post-procedural imaging. We demonstrate results applying this framework to non-rigidly register post-surgical computed tomography (CT) brain images to pre-surgical m...