TY - JOUR
T1 - Field of View Normalization in Multi-Site Brain MRI
AU - Ou, Yangming
AU - Zöllei, Lilla
AU - Da, Xiao
AU - Retzepi, Kallirroi
AU - Murphy, Shawn N.
AU - Gerstner, Elizabeth R.
AU - Rosen, Bruce R.
AU - Grant, P. Ellen
AU - Kalpathy-Cramer, Jayashree
AU - Gollub, Randy L.
N1 - Publisher Copyright:
© 2018, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - Multi-site brain MRI analysis is needed in big data neuroimaging studies, but challenging. The challenges lie in almost every analysis step including skull stripping. The diversities in multi-site brain MR images make it difficult to tune parameters specific to subjects or imaging protocols. Alternatively, using constant parameter settings often leads to inaccurate, inconsistent and even failed skull stripping results. One reason is that images scanned at different sites, under different scanners or protocols, and/or by different technicians often have very different fields of view (FOVs). Normalizing FOV is currently done manually or using ad hoc pre-processing steps, which do not always generalize well to multi-site diverse images. In this paper, we show that (a) a generic FOV normalization approach is possible in multi-site diverse images; we show experiments on images acquired from Philips, GE, Siemens scanners, from 1.0T, 1.5T, 3.0T field of strengths, and from subjects 0–90 years of ages; and (b) generic FOV normalization improves skull stripping accuracy and consistency for multiple skull stripping algorithms; we show this effect for 5 skull stripping algorithms including FSL’s BET, AFNI’s 3dSkullStrip, FreeSurfer’s HWA, BrainSuite’s BSE, and MASS. We have released our FOV normalization software at http://www.nitrc.org/projects/normalizefov.
AB - Multi-site brain MRI analysis is needed in big data neuroimaging studies, but challenging. The challenges lie in almost every analysis step including skull stripping. The diversities in multi-site brain MR images make it difficult to tune parameters specific to subjects or imaging protocols. Alternatively, using constant parameter settings often leads to inaccurate, inconsistent and even failed skull stripping results. One reason is that images scanned at different sites, under different scanners or protocols, and/or by different technicians often have very different fields of view (FOVs). Normalizing FOV is currently done manually or using ad hoc pre-processing steps, which do not always generalize well to multi-site diverse images. In this paper, we show that (a) a generic FOV normalization approach is possible in multi-site diverse images; we show experiments on images acquired from Philips, GE, Siemens scanners, from 1.0T, 1.5T, 3.0T field of strengths, and from subjects 0–90 years of ages; and (b) generic FOV normalization improves skull stripping accuracy and consistency for multiple skull stripping algorithms; we show this effect for 5 skull stripping algorithms including FSL’s BET, AFNI’s 3dSkullStrip, FreeSurfer’s HWA, BrainSuite’s BSE, and MASS. We have released our FOV normalization software at http://www.nitrc.org/projects/normalizefov.
KW - Field of view
KW - Multi-site MRI
KW - Normalization
KW - Standardization
UR - http://www.scopus.com/inward/record.url?scp=85040642090&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040642090&partnerID=8YFLogxK
U2 - 10.1007/s12021-018-9359-z
DO - 10.1007/s12021-018-9359-z
M3 - Article
C2 - 29353341
AN - SCOPUS:85040642090
SN - 1539-2791
VL - 16
SP - 431
EP - 444
JO - Neuroinformatics
JF - Neuroinformatics
IS - 3-4
ER -