A normalisation framework for quantitative brain imaging; application to quantitative susceptibility mapping

Quantitative medical imaging often utilizes intensity normalization based on a signal from a neighboring region. The choice of this region can substantially affect the quantification, and is potentially confounded by the presence of pathology or other limitations such as partial volume effect. In this paper we outline the desirable list of criteria for selecting a normalization region of interest, and utilize this approach for quantitative susceptibility mapping (QSM) MRI in a study of neurodegeneration. The proposed criteria includes (i) association between reference region and demographics such as age, (ii) diagnostic group separation effect in the reference region, (iii) correlation between reference and target regions, (iv) local variance in the reference region, and (v) reduced cross-sectional variance within the diagnostic groups using the reference region. The intensity normalization was then evaluated using 119 subjects with normal cognition, mild cognitive impairment and Alzheimer's disease. For QSM applications in ageing we found that normalizing by the white matter regions not only satisfies the criteria but it also provides the best separation between clinical groups in the brain nuclei regions.