title: STEAM - Statistical Template Estimation for Abnormality Mapping: A personalized DTI analysis technique with applications to the screening of preterm infants.
abstract: We introduce the STEAM DTI analysis engine: a whole brain voxel-based analysis technique for the examination of diffusion tensor images (DTIs). Our STEAM analysis technique consists of two parts. First, we introduce a collection of statistical templates that represent the distribution of DTIs for a normative population. These templates include various diffusion measures from the full tensor, to fractional anisotropy, to 12 other tensor features. Second, we propose a voxel-based analysis (VBA) pipeline that is reliable enough to identify areas in individual DTI scans that differ significantly from the normative group represented in the STEAM statistical templates. We identify and justify choices in the VBA pipeline relating to multiple comparison correction, image smoothing, and dealing with non-normally distributed data. Finally, we provide a proof of concept for the utility of STEAM on a cohort of 134 very preterm infants. We generated templates from scans of 55 very preterm infants whose T1 MRI scans show no abnormalities and who have normal neurodevelopmental outcome. The remaining 79 infants were then compared to the templates using our VBA technique. We show: (a) that our statistical templates display the white matter development expected over the modeled time period, and (b) that our VBA results detect abnormalities in the diffusion measurements that relate significantly with both the presence of white matter lesions and with neurodevelopmental outcomes at 18months. Most notably, we show that STEAM produces personalized results while also being able to highlight abnormalities across the whole brain and at the scale of individual voxels. While we show the value of STEAM on DTI scans from a preterm infant cohort, STEAM can be equally applied to other cohorts as well. To facilitate this whole-brain personalized DTI analysis, we made STEAM publicly available at http://www.sfu.ca/bgb2/steam.
authors.affiliation: Simon Fraser University, Burnaby, Canada. Electronic address: bgb2@sfu.ca.
authors.forename: Brian G
authors.initials: BG
authors.lastname: Booth
authors.affiliation: The Hospital for Sick Children, Toronto, Canada; University of British Columbia, Vancouver, Canada; University of Toronto, Toronto, Canada.
authors.forename: Steven P
authors.initials: SP
authors.lastname: Miller
authors.affiliation: Simon Fraser University, Burnaby, Canada.
authors.forename: Colin J
authors.initials: CJ
authors.lastname: Brown
authors.affiliation: British Columbia's Children's and Women's Hospital, Vancouver, Canada; University of British Columbia, Vancouver, Canada.
authors.forename: Kenneth J
authors.initials: KJ
authors.lastname: Poskitt
authors.affiliation: The Hospital for Sick Children, Toronto, Canada; British Columbia's Children's and Women's Hospital, Vancouver, Canada; University of Toronto, Toronto, Canada.
authors.forename: Vann
authors.initials: V
authors.lastname: Chau
authors.affiliation: The Child & Family Research Institute, Vancouver, Canada; University of British Columbia, Vancouver, Canada.
authors.forename: Ruth E
authors.initials: RE
authors.lastname: Grunau
authors.affiliation: The Child & Family Research Institute, Vancouver, Canada; University of British Columbia, Vancouver, Canada.
authors.forename: Anne R
authors.initials: AR
authors.lastname: Synnes
authors.affiliation: Simon Fraser University, Burnaby, Canada.
authors.forename: Ghassan
authors.initials: G
authors.lastname: Hamarneh
mesh_terms: D001921:Brain / Q000002:abnormalities*; D001931:Brain Mapping / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007231:Infant, Newborn; D007234:Infant, Premature; D008297:Male; D015997:Neonatal Screening / Q000379:methods*; D066127:White Matter / Q000002:abnormalities*
keywords: Brain development; Diffusion tensor imaging; Outcome prediction; Preterm infants; Statistical modeling; Voxel-based analysis