Author(s): Valente, João ; Vieira, Pedro Miguel ; Couto, Carlos ; Lima, C. S.
Date: 2018
Persistent ID: http://hdl.handle.net/1822/52815
Origin: RepositóriUM - Universidade do Minho
Project/scholarship: info:eu-repo/grantAgreement/FCT/5876/147325/PT;
Subject(s): Brain extraction; Partial brain scanning; High resolution MRI; Multiple sclerosis; Science & Technology
Background Poor brain extraction in Magnetic Resonance Imaging (MRI) has negative consequences in several types of brain post-extraction such as tissue segmentation and related statistical measures or pattern recognition algorithms. Current state of the art algorithms for brain extraction work on weighted T1 and T2, being not adequate for non-whole brain images such as the case of T2*FLASH@7T partial volumes. New method This paper proposes two new methods that work directly in T2*FLASH@7T partial volumes. The first is an improvement of the semi-automatic threshold-with-morphology approach adapted to incomplete volumes. The second method uses an improved version of a current implementation of the fuzzy c-means algorithm with bias correction for brain segmentation. Results Under high inhomogeneity conditions the performance of the first method degrades, requiring user intervention which is unacceptable. The second method performed well for all volumes, being entirely automatic. Comparison with existing methods State of the art algorithms for brain extraction are mainly semi-automatic, requiring a correct initialization by the user and knowledge of the software. These methods can't deal with partial volumes and/or need information from atlas which is not available in T2*FLASH@7T. Also, combined volumes suffer from manipulations such as re-sampling which deteriorates significantly voxel intensity structures making segmentation tasks difficult. The proposed method can overcome all these difficulties, reaching good results for brain extraction using only T2*FLASH@7T volumes. Conclusions The development of this work will lead to an improvement of automatic brain lesions segmentation in T2*FLASH@7T volumes, becoming more important when lesions such as cortical Multiple-Sclerosis need to be detected.
This work is supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020–Programa Operacional Competitividade e Internacionalização (POCI) with the reference project POCI01-0145-FEDER-006941.
info:eu-repo/semantics/publishedVersion
