Automatic Measurement of Extra-Axial CSF from Infant MRI Data.


Journal

Proceedings of SPIE--the International Society for Optical Engineering
ISSN: 0277-786X
Titre abrégé: Proc SPIE Int Soc Opt Eng
Pays: United States
ID NLM: 101524122

Informations de publication

Date de publication:
Feb 2020
Historique:
entrez: 31 7 2020
pubmed: 31 7 2020
medline: 31 7 2020
Statut: ppublish

Résumé

The quantification of cerebrospinal fluid (CSF) in the human brain has shown to play an important role in early postnatal brain development. Extra-axial fluid (EA-CSF), which is characterized by CSF in the subarachnoid space, is a promising marker for the early detection of children at risk for neurodevelopmental disorders, such as Autism Spectrum Disorder (ASD). Yet, non-ventricular CSF quantification, in particular extra-axial CSF quantification, is not supported in the major neuro-imaging software solutions, such as FreeSurfer. Most current structural image analysis packages mask out the extra-axial CSF space in one of the first pre-processing steps. A quantitative protocol was previously developed by our group to objectively measure the volume of total EA-CSF volume using a pipeline workflow implemented in a series of python scripts. While this solution worked for our specific lab, a graphical user interface-based tool is necessary to facilitate the computation of extra-axial CSF volume across a wide array of neuroimaging studies and research labs. This paper presents the development of a novel open-source, cross-platform, user-friendly software tool, called Auto-EACSF, for the automatic computation of such extra-axial CSF volume. Auto-EACSF allows neuroimaging labs to quantify extra-axial CSF in their neuroimaging studies in order to investigate its role in normal and atypical brain development.

Identifiants

pubmed: 32728309
doi: 10.1117/12.2550006
pmc: PMC7388182
mid: NIHMS1607136
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : NICHD NIH HHS
ID : U54 HD079124
Pays : United States
Organisme : NICHD NIH HHS
ID : K12 HD001441
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD055741
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB021391
Pays : United States
Organisme : NICHD NIH HHS
ID : P50 HD103573
Pays : United States

Références

Brain. 2013 Sep;136(Pt 9):2825-35
pubmed: 23838695
Lancet Neurol. 2018 Nov;17(11):1016-1024
pubmed: 30353860
Neuroradiol J. 2017 Oct;30(5):410-417
pubmed: 28691570
Biol Psychiatry. 2017 Aug 1;82(3):186-193
pubmed: 28392081
Med Image Anal. 2005 Oct;9(5):457-66
pubmed: 16019252
Neuroimage. 2011 Feb 1;54(3):2033-44
pubmed: 20851191
Proc SPIE Int Soc Opt Eng. 2018 Mar 2;10574:
pubmed: 30364673
Lancet Psychiatry. 2018 Nov;5(11):895-904
pubmed: 30270033

Auteurs

Arthur LeMaout (A)

Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, United States.

Han Bit Yoon (HB)

Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, United States.

Sun Hyung Kim (SH)

Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, United States.

Mahmoud Mostapha (M)

Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, United States.

Mark D Shen (MD)

Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, United States.

Juan Prieto (J)

Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, United States.

Martin Styner (M)

Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, United States.
Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, United States.

Classifications MeSH