title: Differential MRI analysis for quantification of low grade glioma growth.
abstract: A differential analysis framework of longitudinal FLAIR MRI volumes is proposed, based on non-linear gray value mapping, to quantify low-grade glioma growth. First, MRI volumes were mapped to a common range of gray levels via a midway-based histogram mapping. This mapping enabled direct comparison of MRI data and computation of difference maps. A statistical analysis framework of intensity distributions in midway-mapped MRI volumes as well as in their difference maps was designed to identify significant difference values, enabling quantification of low-grade glioma growth, around the borders of an initial segmentation. Two sets of parameters, corresponding to optimistic and pessimistic growth estimations, were proposed. The influence and modeling of MRI inhomogeneity field on a novel midway-mapping framework using image models with multiplicative contrast changes was studied. Clinical evaluation was performed on 32 longitudinal clinical cases from 13 patients. Several growth indices were measured and evaluated in terms of accuracy, compared to manual tracing. Results from the clinical evaluation showed that millimetric precision on a specific volumetric radius growth index measurement can be obtained automatically with the proposed differential analysis. The automated optimistic and pessimistic growth estimates behaved as expected, providing upper and lower bounds around the manual growth estimations.
authors.affiliation: Institut Telecom, Telecom ParisTech, CNRS LTCI, Department of Signal and Image Processing, 46 rue Barrault, 75013 Paris, France. elsa.angelini@telecom-paristech.fr
authors.forename: Elsa D
authors.initials: ED
authors.lastname: Angelini
authors.forename: Julie
authors.initials: J
authors.lastname: Delon
authors.forename: Alpha Boubacar
authors.initials: AB
authors.lastname: Bah
authors.forename: Laurent
authors.initials: L
authors.lastname: Capelle
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Mandonnet
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000473:pathology*; D005910:Glioma / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009367:Neoplasm Staging; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique