Literature DB >> 31209528

Brain tissue and myelin volumetric analysis in multiple sclerosis at 3T MRI with various in-plane resolutions using synthetic MRI.

Laetitia Saccenti1,2, Christina Andica1, Akifumi Hagiwara3,4, Kazumasa Yokoyama5, Mariko Yoshida Takemura1, Shohei Fujita1,6, Tomoko Maekawa1,6, Koji Kamagata1, Alice Le Berre1,2, Masaaki Hori1, Nobutaka Hattori5, Shigeki Aoki1.   

Abstract

PURPOSE: Synthetic MRI (SyMRI) enables automatic brain tissue and myelin volumetry based on the quantification of R1 and R2 relaxation rates and proton density. This study aimed to determine the validity of SyMRI brain tissue and myelin volumetry using various in-plane resolutions at 3T in patients with multiple sclerosis (MS).
METHODS: We scanned 19 MS patients and 10 healthy age- and gender-matched controls using a 3T MR scanner with in-plane resolutions of 0.8, 1.8, and 3.6 mm. The acquisition times were 5 min 8 s, 2 min 52 s, and 2 min 1 s, respectively. White matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and myelin and non-WM/GM/CSF (NoN) volumes; brain parenchymal volume (BPV); and intracranial volume (ICV) were compared between different in-plane resolutions. These parameters were also compared between both groups, after ICV normalization.
RESULTS: No significant differences in measured volumes were noted between the 0.8 and 1.8 mm in-plane resolutions, except in NoN and CSF for healthy controls and NoN for MS patients. Meanwhile, significant volumetric differences were noted in most brain tissues when compared between the 3.6 and 0.8 or 1.8 mm resolution for both healthy controls and MS patients. The normalized WM volume, myelin volume, and BPV showed significant differences between controls and MS patients at in-plane resolutions of 0.8 and 1.8 mm.
CONCLUSIONS: SyMRI brain tissue and myelin volumetry with in-plane resolution as low as 1.8 mm can be useful in the evaluation of MS with a short acquisition time of < 3 min.

Entities:  

Keywords:  Automated brain tissue volumetry; In-plane resolution; Multiple sclerosis; Myelin measurement; Quantitative MRI; Synthetic MRI

Year:  2019        PMID: 31209528     DOI: 10.1007/s00234-019-02241-w

Source DB:  PubMed          Journal:  Neuroradiology        ISSN: 0028-3940            Impact factor:   2.804


  26 in total

1.  Myelin Detection Using Rapid Quantitative MR Imaging Correlated to Macroscopically Registered Luxol Fast Blue-Stained Brain Specimens.

Authors:  J B M Warntjes; A Persson; J Berge; W Zech
Journal:  AJNR Am J Neuroradiol       Date:  2017-04-20       Impact factor: 3.825

2.  Linearity, Bias, Intrascanner Repeatability, and Interscanner Reproducibility of Quantitative Multidynamic Multiecho Sequence for Rapid Simultaneous Relaxometry at 3 T: A Validation Study With a Standardized Phantom and Healthy Controls.

Authors:  Akifumi Hagiwara; Masaaki Hori; Julien Cohen-Adad; Misaki Nakazawa; Yuichi Suzuki; Akihiro Kasahara; Moeko Horita; Takuya Haruyama; Christina Andica; Tomoko Maekawa; Koji Kamagata; Kanako Kunishima Kumamaru; Osamu Abe; Shigeki Aoki
Journal:  Invest Radiol       Date:  2019-01       Impact factor: 6.016

Review 3.  Brain MRI atrophy quantification in MS: From methods to clinical application.

Authors:  Maria A Rocca; Marco Battaglini; Ralph H B Benedict; Nicola De Stefano; Jeroen J G Geurts; Roland G Henry; Mark A Horsfield; Mark Jenkinson; Elisabetta Pagani; Massimo Filippi
Journal:  Neurology       Date:  2016-12-16       Impact factor: 9.910

4.  Automated determination of brain parenchymal fraction in multiple sclerosis.

Authors:  M Vågberg; T Lindqvist; K Ambarki; J B M Warntjes; P Sundström; R Birgander; A Svenningsson
Journal:  AJNR Am J Neuroradiol       Date:  2012-09-13       Impact factor: 3.825

5.  Accuracy and reproducibility of a quantitative magnetic resonance imaging method for concurrent measurements of tissue relaxation times and proton density.

Authors:  Wolfgang Krauss; Martin Gunnarsson; Torbjörn Andersson; Per Thunberg
Journal:  Magn Reson Imaging       Date:  2015-02-20       Impact factor: 2.546

6.  Brain parenchymal fraction in an age-stratified healthy population - determined by MRI using manual segmentation and three automated segmentation methods.

Authors:  Mattias Vågberg; Khalid Ambarki; Thomas Lindqvist; Richard Birgander; Anders Svenningsson
Journal:  J Neuroradiol       Date:  2016-10-05       Impact factor: 3.447

7.  Investigation of the freely available easy-to-use software 'EZR' for medical statistics.

Authors:  Y Kanda
Journal:  Bone Marrow Transplant       Date:  2012-12-03       Impact factor: 5.483

8.  Revised Recommendations of the Consortium of MS Centers Task Force for a Standardized MRI Protocol and Clinical Guidelines for the Diagnosis and Follow-Up of Multiple Sclerosis.

Authors:  A Traboulsee; J H Simon; L Stone; E Fisher; D E Jones; A Malhotra; S D Newsome; J Oh; D S Reich; N Richert; K Rammohan; O Khan; E-W Radue; C Ford; J Halper; D Li
Journal:  AJNR Am J Neuroradiol       Date:  2015-11-12       Impact factor: 3.825

9.  3D quantitative synthetic MRI-derived cortical thickness and subcortical brain volumes: Scan-rescan repeatability and comparison with conventional T1 -weighted images.

Authors:  Shohei Fujita; Akifumi Hagiwara; Masaaki Hori; Marcel Warntjes; Koji Kamagata; Issei Fukunaga; Masami Goto; Haruyama Takuya; Kohei Takasu; Christina Andica; Tomoko Maekawa; Mariko Yoshida Takemura; Ryusuke Irie; Akihiko Wada; Michimasa Suzuki; Shigeki Aoki
Journal:  J Magn Reson Imaging       Date:  2019-04-10       Impact factor: 4.813

10.  Application of quantitative MRI for brain tissue segmentation at 1.5 T and 3.0 T field strengths.

Authors:  Janne West; Ida Blystad; Maria Engström; Jan B M Warntjes; Peter Lundberg
Journal:  PLoS One       Date:  2013-09-16       Impact factor: 3.240

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  3 in total

1.  The effect of scan parameters on T1, T2 relaxation times measured with multi-dynamic multi-echo sequence: a phantom study.

Authors:  Zuofeng Zheng; Jiafei Yang; Dongpo Zhang; Jun Ma; Hongxia Yin; Yawen Liu; Zhenchang Wang
Journal:  Phys Eng Sci Med       Date:  2022-05-13

2.  Myelin Measurement Using Quantitative Magnetic Resonance Imaging: A Correlation Study Comparing Various Imaging Techniques in Patients with Multiple Sclerosis.

Authors:  Laetitia Saccenti; Akifumi Hagiwara; Christina Andica; Kazumasa Yokoyama; Shohei Fujita; Shimpei Kato; Tomoko Maekawa; Koji Kamagata; Alice Le Berre; Masaaki Hori; Akihiko Wada; Ukihide Tateishi; Nobutaka Hattori; Shigeki Aoki
Journal:  Cells       Date:  2020-02-08       Impact factor: 6.600

Review 3.  Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence.

Authors:  Akifumi Hagiwara; Shohei Fujita; Yoshiharu Ohno; Shigeki Aoki
Journal:  Invest Radiol       Date:  2020-09       Impact factor: 10.065

  3 in total

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