title: Enhancing accuracy of magnetic resonance image fusion by defining a volume of interest.
abstract: We compared the registration accuracy for corresponding anatomical landmarks in two MR images after fusing the complete volume (CV) and a defined volume of interest (VOI) of both MRI data sets. We carried out contrast-enhanced T1-weighted gradient-echo and T2-weighted fast spin-echo MRI (matrix 256 x 256) in 39 cases. The CV and a defined VOI data set were each fused using prototype software. We measured and analysed the distance between 25 anatomical landmarks in predefined areas identified at levels L(1)-L(5) corresponding to defined axial sections. Fusion technique, landmark areas and level of fusion were further processed using a feed-forward neural network to calculate the difference which can be expected based on the measurements. We identified 975 landmarks for both T1- and T2-weighted images and found a significant difference in registration accuracy ( P<0.01) for all landmarks between CV (1.6+/-1.2 mm) and VOI (0.7+/-1.0 mm). From cranial (L(1)) to caudal (L(5)), mean deviations were: L(1) CV 1.5 mm, VOI 0.5 mm; L(2) CV 1.8 mm, VOI 0.4 mm; L(3) CV 1.7 mm, VOI 0.4 mm; L(4) CV 1.6 mm, VOI 0.6 mm; and L(5) CV 1.6 mm, VOI 1.6 mm. Neural network analysis predicted a higher accuracy for VOI (0.05-0.15 mm) than for CV fusion (0.9-1.6 mm). Deviations due to magnetic susceptibility changes between air and tissue seen on gradient-echo images can decrease fusion accuracy. Our VOI fusion technique improves image fusion accuracy to <0.5 mm by excluding areas with marked susceptibility changes.
authors.affiliation: Department of Neurosurgery, Klinikum Fulda, Academic Hospital, Philips University Marburg, Pacelliallee 4, 36043 Fulda, Germany. hoelper@online.de
authors.forename: B M
authors.initials: BM
authors.lastname: Hoelper
authors.forename: F
authors.initials: F
authors.lastname: Soldner
authors.forename: R
authors.initials: R
authors.lastname: Lachner
authors.forename: R
authors.initials: R
authors.lastname: Behr
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*