CONTRAST MAPPING AND STATISTICAL TESTING FOR LOW-GRADE GLIOMA GROWTH QUANTIFICATION ON BRAIN MRI

被引:1
作者
Angelini, Elsa D. [1 ]
Delon, Julie [1 ]
Capelle, Laurent [2 ]
Mandonnet, Emmanuel [3 ]
机构
[1] Telecom ParisTech, CNRS, LTCI, Inst Telecom, Paris, France
[2] Hop La Pitie Salpetriere, Dept Neurosurg, Paris, France
[3] Hop Lariboisiere, Dept Neurosurg, Paris, France
来源
2010 7TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO | 2010年
关键词
SEGMENTATION; TUMORS;
D O I
10.1109/ISBI.2010.5490125
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
A statistical differential analysis framework of longitudinal MRI volumes is proposed, based on difference maps after non-linear contrast midway mapping, to quantify tumor growth. This mapping was used to normalize MRI scans to a common range of values, and was adapted in this work to handle multiplicative MRI inhomogeneity fields. This lead to two direct applications: (1) change detection from a statistical test on differences in midway-mapped MRI data, and (2) tumoral growth quantification. A clinical evaluation was performed on 32 clinical cases with low-grade glioma, screened with two FLAIR MRI scans, several months apart. Three growth indices (volume, maximum radius and spherical radius) were measured and evaluated in terms of accuracy, comparing to manual tracing. Millimetric growth estimation precision was achieved with the proposed method for the spherical radius growth index.
引用
收藏
页码:872 / 875
页数:4
相关论文
共 9 条
[1]   Detection of glioma evolution on longitudinal MRI studies [J].
Angelini, E. D. ;
Atif, J. ;
Delon, J. ;
Mandonnet, E. ;
Duffau, H. ;
Capelle, L. .
2007 4TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING : MACRO TO NANO, VOLS 1-3, 2007, :49-+
[2]   Glioma dynamics and computational models:: a review of segmentation, registration, and in silico growth algorithms and their clinical applications [J].
Angelini, Elsa D. ;
Clatz, Olivier ;
Mandonnet, Emmanuel ;
Konukoglu, Ender ;
Capelle, Laurent ;
Duffau, Hugues .
CURRENT MEDICAL IMAGING REVIEWS, 2007, 3 (04) :262-276
[3]   Automatic tumor segmentation using knowledge-based techniques [J].
Clark, MC ;
Hall, LO ;
Goldgof, DB ;
Velthuizen, R ;
Murtagh, FR ;
Silbiger, MS .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 1998, 17 (02) :187-201
[4]   Midway image equalization [J].
Delon, J .
JOURNAL OF MATHEMATICAL IMAGING AND VISION, 2004, 21 (02) :119-134
[5]   Automated segmentation of MR images of brain tumors [J].
Kaus, MR ;
Warfield, SK ;
Nabavi, A ;
Black, PM ;
Jolesz, FA ;
Kikinis, R .
RADIOLOGY, 2001, 218 (02) :586-591
[6]   Monitoring slowly evolving tumors [J].
Konukoglu, E. ;
Wells, W. M. ;
Novellas, S. ;
Ayachel, N. ;
Kikinis, R. ;
Black, P. M. ;
Pohl, K. M. .
2008 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1-4, 2008, :812-+
[7]   A longitudinal study of brain morphometrics using quantitative magnetic resonance imaging and difference image analysis [J].
Liu, RSN ;
Lemieux, L ;
Bell, GS ;
Sisodiya, SM ;
Shorvon, SD ;
Sander, JWAS ;
Duncan, JS .
NEUROIMAGE, 2003, 20 (01) :22-33
[8]   Continuous growth of mean tumor diameter in a subset of grade II gliomas [J].
Mandonnet, E ;
Delattre, JY ;
Tanguy, ML ;
Swanson, KR ;
Carpentier, AF ;
Duffau, H ;
Cornu, P ;
Van Effenterre, R ;
Alvord, EC ;
Capelle, L .
ANNALS OF NEUROLOGY, 2003, 53 (04) :524-528
[9]   A review of methods for correction of intensity inhomogeneity in MRI [J].
Vovk, Uros ;
Pernus, Franjo ;
Likar, Bostjan .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2007, 26 (03) :405-421