Literature DB >> 22281731

Comparison between intensity normalization techniques for dynamic susceptibility contrast (DSC)-MRI estimates of cerebral blood volume (CBV) in human gliomas.

Benjamin M Ellingson1, Taryar Zaw, Timothy F Cloughesy, Kourosh M Naeini, Shadi Lalezari, Sandy Mong, Albert Lai, Phioanh L Nghiemphu, Whitney B Pope.   

Abstract

PURPOSE: To compare "standardization," "Gaussian normalization," and "Z-score normalization" intensity transformation techniques in dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) estimates of cerebral blood volume (CBV) in human gliomas. DSC-MRI is a well-established biomarker for CBV in brain tumors; however, DSC-MRI estimates of CBV are semiquantitative. The use of image intensity transformation algorithms provides a mechanism for obtaining quantitatively similar CBV maps with the same intensity scaling.
MATERIALS AND METHODS: The coefficient of variance (CV) in normal-appearing white matter and relative contrast between tumor regions and normal tissue was compared between the three CBV transformations across five different MR scanners in 96 patients with gliomas.
RESULTS: The results suggest all normalization techniques improved variability and relative tumor contrast of CBV measurements compared with nonnormalized CBV maps. The results suggest Gaussian normalization of CBV maps provided slightly lower CV in normal white matter and provided slightly higher tumor contrast for glioblastomas (WHO grade IV) compared with other techniques.
CONCLUSION: The results suggest Gaussian normalization of leakage-corrected CBV maps may be the best choice for image intensity correction for use in large-scale, multicenter clinical trials where MR scanners and protocols vary widely due to ease of implementation, lowest variability, and highest tumor to normal tissue contrast.
Copyright © 2012 Wiley Periodicals, Inc.

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Year:  2012        PMID: 22281731     DOI: 10.1002/jmri.23600

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  30 in total

1.  Perfusion imaging of brain gliomas using arterial spin labeling: correlation with histopathological vascular density in MRI-guided biopsies.

Authors:  Di Ningning; Pang Haopeng; Dang Xuefei; Cheng Wenna; Ren Yan; Wu Jingsong; Yao Chengjun; Yao Zhenwei; Feng Xiaoyuan
Journal:  Neuroradiology       Date:  2016-12-06       Impact factor: 2.804

2.  Emerging techniques and technologies in brain tumor imaging.

Authors:  Benjamin M Ellingson; Martin Bendszus; A Gregory Sorensen; Whitney B Pope
Journal:  Neuro Oncol       Date:  2014-10       Impact factor: 12.300

3.  Clinical parameters outweigh diffusion- and perfusion-derived MRI parameters in predicting survival in newly diagnosed glioblastoma.

Authors:  Sina Burth; Philipp Kickingereder; Oliver Eidel; Diana Tichy; David Bonekamp; Lukas Weberling; Antje Wick; Sarah Löw; Anne Hertenstein; Martha Nowosielski; Heinz-Peter Schlemmer; Wolfgang Wick; Martin Bendszus; Alexander Radbruch
Journal:  Neuro Oncol       Date:  2016-06-13       Impact factor: 12.300

4.  A novel voxel-wise lesion segmentation technique on 3.0-T diffusion MRI of hyperacute focal cerebral ischemia at 1 h after permanent MCAO in rats.

Authors:  Chi-Hoon Choi; Kyung Sik Yi; Sang-Rae Lee; Youngjeon Lee; Chang-Yeop Jeon; Jinwoo Hwang; Chulhyun Lee; Sung Sik Choi; Hong Jun Lee; Sang-Hoon Cha
Journal:  J Cereb Blood Flow Metab       Date:  2017-06-09       Impact factor: 6.200

5.  Assessment of tumor oxygenation and its impact on treatment response in bevacizumab-treated recurrent glioblastoma.

Authors:  David Bonekamp; Kim Mouridsen; Alexander Radbruch; Felix T Kurz; Oliver Eidel; Antje Wick; Heinz-Peter Schlemmer; Wolfgang Wick; Martin Bendszus; Leif Østergaard; Philipp Kickingereder
Journal:  J Cereb Blood Flow Metab       Date:  2016-07-21       Impact factor: 6.200

6.  Optimization of DSC MRI Echo Times for CBV Measurements Using Error Analysis in a Pilot Study of High-Grade Gliomas.

Authors:  L C Bell; M D Does; A M Stokes; L C Baxter; K M Schmainda; A C Dueck; C C Quarles
Journal:  AJNR Am J Neuroradiol       Date:  2017-07-06       Impact factor: 3.825

7.  Relative cerebral blood volume is a potential predictive imaging biomarker of bevacizumab efficacy in recurrent glioblastoma.

Authors:  Philipp Kickingereder; Benedikt Wiestler; Sina Burth; Antje Wick; Martha Nowosielski; Sabine Heiland; Heinz-Peter Schlemmer; Wolfgang Wick; Martin Bendszus; Alexander Radbruch
Journal:  Neuro Oncol       Date:  2015-03-09       Impact factor: 12.300

Review 8.  Emerging MRI Techniques to Redefine Treatment Response in Patients With Glioblastoma.

Authors:  Fabrício Guimarães Gonçalves; Sanjeev Chawla; Suyash Mohan
Journal:  J Magn Reson Imaging       Date:  2020-03-19       Impact factor: 4.813

9.  Recurrent glioblastoma treated with bevacizumab: contrast-enhanced T1-weighted subtraction maps improve tumor delineation and aid prediction of survival in a multicenter clinical trial.

Authors:  Benjamin M Ellingson; Hyun J Kim; Davis C Woodworth; Whitney B Pope; Jonathan N Cloughesy; Robert J Harris; Albert Lai; Phioanh L Nghiemphu; Timothy F Cloughesy
Journal:  Radiology       Date:  2013-11-27       Impact factor: 11.105

10.  Dynamic Contrast-Enhanced MRI in Low-Grade Versus Anaplastic Oligodendrogliomas.

Authors:  Julio Arevalo-Perez; Amanuel A Kebede; Kyung K Peck; Eli Diamond; Andrei I Holodny; Marc Rosenblum; Jennifer Rubel; Joshua Gaal; Vaios Hatzoglou
Journal:  J Neuroimaging       Date:  2015-12-26       Impact factor: 2.486

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