Edge- and detail-preserving sparse image representations for deformable registration of chest MRI and CT volumes.

TitelEdge- and detail-preserving sparse image representations for deformable registration of chest MRI and CT volumes.
Publication TypeJournal Article
Year of Publication2013
AuthorsHeinrich M.P., Jenkinson M., Papiez B.W., Glesson F.V., Brady S.M., Schnabel J.A.
JournalInformation processing in medical imaging
Volume23
Pages463-74
Date Published2013
Publication Languageeng
ISSN1011-2499
SchlüsselwörterAlgorithms, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Lung, Magnetic Resonance Imaging, Multimodal Imaging, Pattern Recognition, Automated, Radiography, Thoracic, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique, Tomography, X-Ray Computed
Abstract

Deformable medical image registration requires the optimisation of a function with a large number of degrees of freedom. Commonly-used approaches to reduce the computational complexity, such as uniform B-splines and Gaussian image pyramids, introduce translation-invariant homogeneous smoothing, and may lead to less accurate registration in particular for motion fields with discontinuities. This paper introduces the concept of sparse image representation based on supervoxels, which are edge-preserving and therefore enable accurate modelling of sliding organ motions frequently seen in respiratory and cardiac scans. Previous shortcomings of using supervoxels in motion estimation, in particular inconsistent clustering in ambiguous regions, are overcome by employing multiple layers of supervoxels. Furthermore, we propose a new similarity criterion based on a binary shape representation of supervoxels, which improves the accuracy of single-modal registration and enables multimodal registration. We validate our findings based on the registration of two challenging clinical applications of volumetric deformable registration: motion estimation between inhale and exhale phase of CT scans for radiotherapy planning, and deformable multi-modal registration of diagnostic MRI and CT chest scans. The experiments demonstrate state-of-the-art registration accuracy, and require no additional anatomical knowledge with greatly reduced computational complexity.

PubMed Link

http://www.ncbi.nlm.nih.gov/pubmed/24683991?dopt=Abstract

Alternate JournalInf Process Med Imaging
Erstellt am 26. Oktober 2015 - 11:31.

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