Towards realtime multimodal fusion for image-guided interventions using self-similarities.

TitelTowards realtime multimodal fusion for image-guided interventions using self-similarities.
Publication TypeJournal Article
Year of Publication2013
AuthorsHeinrich M.P., Jenkinson M., Papiez B.W., Brady S.M., Schnabel J.A.
JournalMedical image computing and computer-assisted intervention : MICCAI 2013 International Conference on Medical Image Computing and Computer-Assisted Intervention
Volume16
IssuePt 1
Pages187-94
Date Published2013
Publication Languageeng
SchlüsselwörterAlgorithms, Brain Neoplasms, Computer Systems, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Multimodal Imaging, Neurosurgical Procedures, Pattern Recognition, Automated, Subtraction Technique, Surgery, Computer-Assisted
Abstract

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 "self-similarity context". 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.

PubMed Link

http://www.ncbi.nlm.nih.gov/pubmed/24505665?dopt=Abstract

Alternate JournalMed Image Comput Comput Assist Interv
Erstellt am 26. Oktober 2015 - 10:28.

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