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Cattell, L.* ; Schnabel, J.A.* ; Declerck, J.* ; Hutton, C.*

Combined PET-MR brain registration to discriminate between Alzheimer's disease and healthy controls.

In: (International Workshop on Biomedical Image Registration). Berlin [u.a.]: Springer, 2014. 134-143 (Lect. Notes Comput. Sc. ; 8545 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Previous amyloid positron emission tomography (PET) imaging studies have shown that Alzheimer's disease (AD) patients exhibit higher standardised uptake value ratios (SUVRs) than healthy controls. Automatic methods for SUVR calculation in brain images are typically based on registration of PET brain data to a template, followed by computation of the mean uptake ratio in a set of regions in the template space. Resulting SUVRs will therefore have some dependence on the registration method. It is widely accepted that registration based on anatomical information provides optimal results. However, in clinical practice, good quality anatomical data may not be available and registration is often based on PET data alone. We investigate the effect of using functional and structural image information during the registration of PET volumes to a template, by comparing six registration methods: affine registration, non-linear registration using PET-driven demons, non-linear registration using magnetic resonance (MR) driven demons, and our novel joint PET-MR registration technique with three different combination weightings. Our registration method jointly registers PET-MR brain volume pairs, by combining the incremental updates computed in single-modality local correlation coefficient demons registrations. All six registration methods resulted in significantly higher mean SUVRs for diseased subjects compared to healthy subjects. Furthermore, the combined PET-MR registration method resulted in a small, but significant, increase in the mean Dice overlaps between cortical regions in the MR brain volumes and the MR template, compared with the single-modality registration methods. These results suggest that a non-linear, combined PET-MR registration method can perform at least as well as the single-modality registration methods in terms of the separation between SUVRs and Dice overlaps, and may be well suited to discriminate between populations of AD patients and healthy controls.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel International Workshop on Biomedical Image Registration
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 8545 LNCS, Heft: , Seiten: 134-143 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)