Literature DB >> 21911309

Differential MRI analysis for quantification of low grade glioma growth.

Elsa D Angelini1, Julie Delon, Alpha Boubacar Bah, Laurent Capelle, Emmanuel Mandonnet.   

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.
Copyright © 2011. Published by Elsevier B.V.

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Year:  2011        PMID: 21911309     DOI: 10.1016/j.media.2011.05.014

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  6 in total

1.  Adaptive quantification and longitudinal analysis of pulmonary emphysema with a hidden Markov measure field model.

Authors:  Yrjo Hame; Elsa D Angelini; Eric A Hoffman; R Graham Barr; Andrew F Laine
Journal:  IEEE Trans Med Imaging       Date:  2014-04-15       Impact factor: 10.048

2.  Imaging growth as a predictor of grade of malignancy and aggressiveness of IDH-mutant and 1p/19q-codeleted oligodendrogliomas in adults.

Authors:  Alexandre Roux; Arnault Tauziede-Espariat; Marc Zanello; Sophie Peeters; Gilles Zah-Bi; Eduardo Parraga; Myriam Edjlali; Emmanuèle Lechapt; Natalia Shor; Luisa Bellu; Giulia Berzero; Didier Dormont; Edouard Dezamis; Fabrice Chretien; Catherine Oppenheim; Marc Sanson; Pascale Varlet; Laurent Capelle; Frédéric Dhermain; Johan Pallud
Journal:  Neuro Oncol       Date:  2020-07-07       Impact factor: 12.300

3.  Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies.

Authors:  Lior Weizman; Liat Ben Sira; Leo Joskowicz; Daniel L Rubin; Kristen W Yeom; Shlomi Constantini; Ben Shofty; Dafna Ben Bashat
Journal:  Med Phys       Date:  2014-05       Impact factor: 4.071

4.  Intra-rater variability in low-grade glioma segmentation.

Authors:  Hans Kristian Bø; Ole Solheim; Asgeir Store Jakola; Kjell-Arne Kvistad; Ingerid Reinertsen; Erik Magnus Berntsen
Journal:  J Neurooncol       Date:  2016-11-11       Impact factor: 4.130

Review 5.  Current standards and new concepts in MRI and PET response assessment of antiangiogenic therapies in high-grade glioma patients.

Authors:  Markus Hutterer; Elke Hattingen; Christoph Palm; Martin Andreas Proescholdt; Peter Hau
Journal:  Neuro Oncol       Date:  2014-12-27       Impact factor: 12.300

6.  Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning.

Authors:  Yudong Zhang; Zhengchao Dong; Preetha Phillips; Shuihua Wang; Genlin Ji; Jiquan Yang; Ti-Fei Yuan
Journal:  Front Comput Neurosci       Date:  2015-06-02       Impact factor: 2.380

  6 in total

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