title: Using curve-fitting of curvilinear features for assessing registration of clinical neuropathology with in vivo MRI.
abstract: Traditional neuropathological examination provides information about neurological disease or injury of a patient at a high-resolution level. Correlating this type of post mortem diagnosis with in vivo image data of the same patient acquired by non-invasive tomographic scans greatly complements the interpretation of any disease or injury. We present the validation of a registration method for correlating macroscopic pathological images with MR images of the same patient. This also allows for 3-D mapping of the distribution of pathological changes throughout the brain. As the validation deals with datasets of widely differing sampling, we propose a method using smooth curvilinear anatomical features in the brain which allows interpolation between wide-spaced samples. Curvilinear features are common anatomically, and if selected carefully have the potential to allow determination of the accuracy of co-registration across large areas of a volume of interest.
authors.affiliation: Medical Image Computing, University of Kent, Canterbury CT2 7PD, United Kingdom.
authors.forename: P P
authors.initials: PP
authors.lastname: Laissue
authors.forename: C
authors.initials: C
authors.lastname: Kenwright
authors.forename: S A
authors.initials: SA
authors.lastname: Hojjat
authors.forename: A C F
authors.initials: AC
authors.lastname: Colchester
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001927:Brain Diseases / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique