<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heinrich, Mattias P.</style></author><author><style face="normal" font="default" size="100%">Jenkinson, Mark</style></author><author><style face="normal" font="default" size="100%">Papiez, Bartlomiej W.</style></author><author><style face="normal" font="default" size="100%">Brady, Sir Michael</style></author><author><style face="normal" font="default" size="100%">Julia A. Schnabel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards realtime multimodal fusion for image-guided interventions using self-similarities.</style></title><secondary-title><style face="normal" font="default" size="100%">Medical image computing and computer-assisted intervention : MICCAI 2013 International Conference on Medical Image Computing and Computer-Assisted Intervention</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Med Image Comput Comput Assist Interv</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Enhancement</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Interpretation, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Magnetic Resonance Imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">Multimodal Imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">Neurosurgical Procedures</style></keyword><keyword><style  face="normal" font="default" size="100%">Pattern Recognition, Automated</style></keyword><keyword><style  face="normal" font="default" size="100%">Subtraction Technique</style></keyword><keyword><style  face="normal" font="default" size="100%">Surgery, Computer-Assisted</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">187-94</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Image-guided interventions often rely on deformable multimodal registration to align pre-treatment and intra-operative scans. There are a number of requirements for automated image registration for this task, such as a robust similarity metric for scans of different modalities with different noise distributions and contrast, an efficient optimisation of the cost function to enable fast registration for this time-sensitive application, and an insensitive choice of registration parameters to avoid delays in practical clinical use. In this work, we build upon the concept of structural image representation for multi-modal similarity. Discriminative descriptors are densely extracted for the multi-modal scans based on the &quot;self-similarity context&quot;. An efficient quantised representation is derived that enables very fast computation of point-wise distances between descriptors. A symmetric multi-scale discrete optimisation with diffusion reguIarisation is used to find smooth transformations. The method is evaluated for the registration of 3D ultrasound and MRI brain scans for neurosurgery and demonstrates a significantly reduced registration error (on average 2.1 mm) compared to commonly used similarity metrics and computation times of less than 30 seconds per 3D registration.</style></abstract><issue><style face="normal" font="default" size="100%">Pt 1</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/24505665?dopt=Abstract</style></custom1></record></records></xml>