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2018 | OriginalPaper | Chapter

A Framework to Objectively Identify Reference Regions for Normalizing Quantitative Imaging

Authors : Amir Fazlollahi, Scott Ayton, Pierrick Bourgeat, Ibrahima Diouf, Parnesh Raniga, Jurgen Fripp, James Doecke, David Ames, Colin L. Masters, Christopher C. Rowe, Victor L. Villemagne, Ashley I. Bush, Olivier Salvado

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Publisher: Springer International Publishing

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Abstract

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.

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Metadata
Title
A Framework to Objectively Identify Reference Regions for Normalizing Quantitative Imaging
Authors
Amir Fazlollahi
Scott Ayton
Pierrick Bourgeat
Ibrahima Diouf
Parnesh Raniga
Jurgen Fripp
James Doecke
David Ames
Colin L. Masters
Christopher C. Rowe
Victor L. Villemagne
Ashley I. Bush
Olivier Salvado
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-030-00928-1_8

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