The quantitative use of medical images often requires an intensity scaling with respect to the signal from a well-characterized anatomical region of interest. The choice of such a region often varies between studies which can substantially influence the quantification, resulting in study bias hampering objective findings which are detrimental to open science. This study outlines a list of criteria and a statistical ranking approach for identifying normalization region of interest. The proposed criteria include (i) associations between reference region and demographics such as age, (ii) diagnostic group differences in the reference region, (iii) correlation between reference and primary areas of interest, (iv) local variance in the reference region, and (v) longitudinal reproducibility of the target regions when normalized. The proposed approach has been used to establish an optimal normalization region of interest for the analysis of Quantitative Susceptibility Mapping (QSM) of Magnetic Resonance Imaging (MRI). This was achieved by using cross-sectional data from 119 subjects with normal cognition, mild cognitive impairment, and Alzheimer’s disease as well as and 19 healthy elderly individuals with longitudinal data. For the QSM application, we found that normalizing by the white matter regions not only satisfies the criteria but it also provides the best separation between clinical groups for deep brain nuclei target regions.
CITATION STYLE
Fazlollahi, A., Ayton, S., Bourgeat, P., Diouf, I., Raniga, P., Fripp, J., … Salvado, O. (2018). A framework to objectively identify reference regions for normalizing quantitative imaging. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11070 LNCS, pp. 65–72). Springer Verlag. https://doi.org/10.1007/978-3-030-00928-1_8
Mendeley helps you to discover research relevant for your work.