Measuring brain atrophy with a generalized formulation of the boundary shift integral

Ferran Prados; Manuel Jorge Cardoso; Kelvin K Leung; David M Cash; Marc Modat; Nick C Fox; Claudia A M Wheeler-Kingshott; Sebastien Ourselin; Alzheimer's Disease Neuroimaging Initiative

doi:10.1016/j.neurobiolaging.2014.04.035

Measuring brain atrophy with a generalized formulation of the boundary shift integral

Neurobiol Aging. 2015 Jan;36 Suppl 1(Suppl 1):S81-90. doi: 10.1016/j.neurobiolaging.2014.04.035. Epub 2014 Aug 29.

Authors

Ferran Prados¹, Manuel Jorge Cardoso², Kelvin K Leung³, David M Cash⁴, Marc Modat⁴, Nick C Fox³, Claudia A M Wheeler-Kingshott⁵, Sebastien Ourselin⁴; Alzheimer's Disease Neuroimaging Initiative

Affiliations

¹ Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK. Electronic address: f.carrasco@ucl.ac.uk.
² Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
³ Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.
⁴ Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.
⁵ NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.

PMID: 25264346
PMCID: PMC4288791
DOI: 10.1016/j.neurobiolaging.2014.04.035

Abstract

Brain atrophy measured using structural magnetic resonance imaging (MRI) has been widely used as an imaging biomarker for disease diagnosis and tracking of pathologic progression in neurodegenerative diseases. In this work, we present a generalized and extended formulation of the boundary shift integral (gBSI) using probabilistic segmentations to estimate anatomic changes between 2 time points. This method adaptively estimates a non-binary exclusive OR region of interest from probabilistic brain segmentations of the baseline and repeat scans to better localize and capture the brain atrophy. We evaluate the proposed method by comparing the sample size requirements for a hypothetical clinical trial of Alzheimer's disease to that needed for the current implementation of BSI as well as a fuzzy implementation of BSI. The gBSI method results in a modest but reduced sample size, providing increased sensitivity to disease changes through the use of the probabilistic exclusive OR region.

Keywords: Alzheimer's disease; Biomarker; Clinical trials; MRI; boundary shift integral.

Publication types

Comparative Study
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Aged
Aged, 80 and over
Alzheimer Disease / diagnosis*
Alzheimer Disease / pathology*
Atrophy
Brain / pathology*
Diffusion Magnetic Resonance Imaging / methods*
Female
Humans
Male
Middle Aged
Neuroimaging / methods*

Abstract

Publication types

MeSH terms

Grants and funding