title: Characterization of anatomic fiber bundles for diffusion tensor image analysis.
abstract: In this paper we deal with the problem of quantification of diffusion tensor (DT) data sets. A set of measures and a 2D tract mapping technique are proposed to analyze the fiber structures in brain white matter and to allow for comparisons between different subjects, either patients or controls. Features such as integrity, discontinuity and connectivity of the fiber bundles are proposed and analyzed, taking into account longitudinal and transverse information of the fiber bundle under study. The performance of the proposed characterization framework is shown analyzing the corticospinal tracts of control data sets and pathological cases, comparing the measures between controls and patients and also between the right and left hemispheres. A reproducibility study is also performed to show the robustness of the proposed measures.
authors.affiliation: Laboratory of Image Processing, University of Valladolid, Spain. ruben@lpi.tel.uva.es
authors.forename: Rubén
authors.initials: R
authors.lastname: Cárdenes
authors.forename: Daniel
authors.initials: D
authors.lastname: Argibay-Quiñones
authors.forename: Emma
authors.initials: E
authors.lastname: Muñoz-Moreno
authors.forename: Marcos
authors.initials: M
authors.lastname: Martin-Fernandez
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000166:cytology*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique