Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/20680
Title: A Framework to Objectively Identify Reference Regions for Normalizing Quantitative Imaging
Authors: Fazlollahi, Amir;Ayton, Scott;Bourgeat, Pierrick;Diouf, Ibrahima;Raniga, Parnesh;Fripp, Jurgen;Doecke, James;Ames, David;Masters, Colin L;Rowe, Christopher C
Affiliation: CSIRO Health and Biosecurity, Brisbane, Australia
Cooperative Research Centre for Mental Health, Parkville, Australia
Florey Institute of Neuroscience and Mental Health, Parkville, Australia
The University of Melbourne, Parkville, Australia
Austin Health, Heidelberg, Victoria, Australia
Issue Date: Sep-2018
EDate: 2018-09-26
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.
Conference Name: MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention
Conference Number/Code: 21
Conference Location: Granada, Spain
URI: http://ahro.austin.org.au/austinjspui/handle/1/20680
DOI: 10.1007/978-3-030-00928-1_8
ORCID: 0000-0003-3910-2453
Type: Conference Presentation
Subjects: Quantification
Reference region
Normalization
QSM
Appears in Collections:Journal articles

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