Field of View Normalization in Multi-Site Brain MRI

Yangming Ou, Lilla Zöllei, Xiao Da, Kallirroi Retzepi, Shawn N. Murphy, Elizabeth R. Gerstner, Bruce R. Rosen, P. Ellen Grant, Jayashree Kalpathy-Cramer, Randy L. Gollub

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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

Original languageEnglish (US)
Pages (from-to)431-444
Number of pages14
Issue number3-4
StatePublished - Oct 1 2018
Externally publishedYes


  • Field of view
  • Multi-site MRI
  • Normalization
  • Standardization

ASJC Scopus subject areas

  • Software
  • Neuroscience(all)
  • Information Systems


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