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Heinrich, M.P.* ; Jenkinson, M.* ; Brady, M.* ; Schnabel, J.A.*

MRF-Based deformable registration and ventilation estimation of lung CT.

IEEE Trans. Med. Imaging 32, 1239-1248 (2013)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
Deformable image registration is an important tool in medical image analysis. In the case of lung computed tomography (CT) registration there are three major challenges: large motion of small features, sliding motions between organs, and changing image contrast due to compression. Recently, Markov random field (MRF)-based discrete optimization strategies have been proposed to overcome problems involved with continuous optimization for registration, in particular its susceptibility to local minima. However, to date the simplifications made to obtain tractable computational complexity reduced the registration accuracy. We address these challenges and preserve the potentially higher quality of discrete approaches with three novel contributions. First, we use an image-derived minimum spanning tree as a simplified graph structure, which copes well with the complex sliding motion and allows us to find the global optimum very efficiently. Second, a stochastic sampling approach for the similarity cost between images is introduced within a symmetric, diffeomorphic B-spline transformation model with diffusion regularization. The complexity is reduced by orders of magnitude and enables the minimization of much larger label spaces. In addition to the geometric transform labels, hyper-labels are introduced, which represent local intensity variations in this task, and allow for the direct estimation of lung ventilation. We validate the improvements in accuracy and performance on exhale-inhale CT volume pairs using a large number of expert landmarks.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Discrete Optimization ; Lung Ventilation ; Markov Random Field (mrf) ; Minimum-spanning-tree ; Nonrigid Registration ; Sliding Motion ; Stochastic Optimization
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Journal IEEE Transactions on Medical Imaging
Quellenangaben Volume: 32, Issue: 7, Pages: 1239-1248 Article Number: , Supplement: ,
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Publishing Place New York, NY [u.a.]
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)