Paper Info
Title
Correction of inter-scanner and within-subject variance in structural MRI based automated diagnosing.
Abstract
Automated analysis of structural magnetic resonance images is a promising way to improve early detection of neurodegenerative brain diseases. Clinical applications of such methods involve multiple scanners with potentially different hardware and/or acquisition sequences and demographically heterogeneous groups. To improve classification performance, we propose to correct effects of subject-specific covariates (such as age, total intracranial volume, and sex) as well as effects of scanner by using a non-linear Gaussian process model. To test the efficacy of the correction, we performed classification of carriers of the genetic mutation leading to Huntington's disease (HD) versus healthy controls. Half of the HD carriers were free of typical HD symptoms and had an estimated 5 to 20years before onset of clinical symptoms, thus providing a model for preclinical diagnosis of a neurodegenerative disease.
Year
DOI
Venue
2014
10.1016/j.neuroimage.2014.04.057
NeuroImage
Keywords
Field
DocType
Between-scanner variability,Structural MRI,Classification,Neuro-degeneration,Huntington's disease,Support vector machines
Voxel,Covariate,Confounding,Pattern recognition,Computer science,Support vector machine,Cognitive psychology,Gaussian process,Artificial intelligence,Scanner,Neuroimaging,Magnetic resonance imaging
Journal
Volume
ISSN
Citations 
98
1053-8119
8
PageRank 
References 
Authors
0.59
11
11
Name
Order
Citations
PageRank
Daniel Kostro180.59
Ahmed Abdulkadir230912.38
Alexandra Durr381.61
Raymund A. C. Roos4100.97
Blair R. Leavitt5331.33
Hans J. Johnson662752.13
David M. Cash735928.35
Sarah J Tabrizi8343.55
Rachael I Scahill9998.90
Olaf Ronneberger102726107.29
Stefan Klöppel1128419.77