@inproceedings{PalmGraemeCrumetal., author = {Palm, Christoph and Graeme, Penny P. and Crum, William R. and Schnabel, Julia A. and Pietrzyk, Uwe and Hawkes, David J.}, title = {Fusion of Rat Brain Histology and MRI using Weighted Multi-Image Mutual Information}, series = {Proceedings of the SPIE Medical Imaging 6914: Image Processing 69140M}, booktitle = {Proceedings of the SPIE Medical Imaging 6914: Image Processing 69140M}, number = {6914}, doi = {10.1117/12.770605}, pages = {69140M-1 -- 69140M-9}, abstract = {Fusion of histology and MRI is frequently demanded in biomedical research to study in vitro tissue properties in an in vivo reference space. Distortions and artifacts caused by cutting and staining of histological slices as well as differences in spatial resolution make even the rigid fusion a difficult task. State-of- the-art methods start with a mono-modal restacking yielding a histological pseudo-3D volume. The 3D information of the MRI reference is considered subsequently. However, consistency of the histology volume and consistency due to the corresponding MRI seem to be diametral goals. Therefore, we propose a novel fusion framework optimizing histology/histology and histology/MRI consistency at the same time finding a balance between both goals. Method - Direct slice-to-slice correspondence even in irregularly-spaced cutting sequences is achieved by registration-based interpolation of the MRI. Introducing a weighted multi-image mutual information metric (WI), adjacent histology and corresponding MRI are taken into account at the same time. Therefore, the reconstruction of the histological volume as well as the fusion with the MRI is done in a single step. Results - Based on two data sets with more than 110 single registrations in all, the results are evaluated quantitatively based on Tanimoto overlap measures and qualitatively showing the fused volumes. In comparison to other multi-image metrics, the reconstruction based on WI is significantly improved. We evaluated different parameter settings with emphasis on the weighting term steering the balance between intra- and inter-modality consistency.}, subject = {Kernspintomografie}, language = {en} } @misc{PalmCrumPietrzyketal., author = {Palm, Christoph and Crum, William R. and Pietrzyk, Uwe and Hawkes, David J.}, title = {Application of Fluid and Elastic Registration Methods to Histological Rat Brain Sections}, series = {Biomedizinische Technik}, volume = {52}, journal = {Biomedizinische Technik}, number = {Suppl.}, pages = {1569048-859}, subject = {Registrierung }, language = {en} } @article{MangSchnabelCrumetal., author = {Mang, Andreas and Schnabel, Julia A. and Crum, William R. and Modat, Marc and Camara-Rey, Oscar and Palm, Christoph and Caseiras, Gisele Brasil and J{\"a}ger, H. Rolf and Ourselin, S{\´e}bastien and Buzug, Thorsten M. and Hawkes, David J.}, title = {Consistency of parametric registration in serial MRI studies of brain tumor progression}, series = {International Journal of Computer Assisted Radiology and Surgery}, volume = {3}, journal = {International Journal of Computer Assisted Radiology and Surgery}, number = {3-4}, doi = {10.1007/s11548-008-0234-5}, pages = {201 -- 211}, abstract = {Object The consistency of parametric registration in multi-temporal magnetic resonance (MR) imaging studies was evaluated. Materials and methods Serial MRI scans of adult patients with a brain tumor (glioma) were aligned by parametric registration. The performance of low-order spatial alignment (6/9/12 degrees of freedom) of different 3D serial MR-weighted images is evaluated. A registration protocol for the alignment of all images to one reference coordinate system at baseline is presented. Registration results were evaluated for both, multimodal intra-timepoint and mono-modal multi-temporal registration. The latter case might present a challenge to automatic intensity-based registration algorithms due to ill-defined correspondences. The performance of our algorithm was assessed by testing the inverse registration consistency. Four different similarity measures were evaluated to assess consistency. Results Careful visual inspection suggests that images are well aligned, but their consistency may be imperfect. Sub-voxel inconsistency within the brain was found for allsimilarity measures used for parametric multi-temporal registration. T1-weighted images were most reliable for establishing spatial correspondence between different timepoints. Conclusions The parametric registration algorithm is feasible for use in this application. The sub-voxel resolution mean displacement error of registration transformations demonstrates that the algorithm converges to an almost identical solution for forward and reverse registration.}, subject = {Kernspintomografie}, language = {en} }