Cargando…

MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal

OBJECTIVE: Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site (“target”) to the second (“reference”) to reduce confounding s...

Descripción completa

Detalles Bibliográficos
Autores principales: Newlin, Nancy R., Kim, Michael E., Kanakaraj, Praitayini, Yao, Tianyuan, Hohman, Timothy, Pechman, Kimberly R., Beason-Held, Lori L., Resnick, Susan M., Archer, Derek, Jefferson, Angela, Landman, Bennett A., Moyer, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10462069/
https://www.ncbi.nlm.nih.gov/pubmed/37645973
http://dx.doi.org/10.1101/2023.08.12.553099
_version_ 1785097984206176256
author Newlin, Nancy R.
Kim, Michael E.
Kanakaraj, Praitayini
Yao, Tianyuan
Hohman, Timothy
Pechman, Kimberly R.
Beason-Held, Lori L.
Resnick, Susan M.
Archer, Derek
Jefferson, Angela
Landman, Bennett A.
Moyer, Daniel
author_facet Newlin, Nancy R.
Kim, Michael E.
Kanakaraj, Praitayini
Yao, Tianyuan
Hohman, Timothy
Pechman, Kimberly R.
Beason-Held, Lori L.
Resnick, Susan M.
Archer, Derek
Jefferson, Angela
Landman, Bennett A.
Moyer, Daniel
author_sort Newlin, Nancy R.
collection PubMed
description OBJECTIVE: Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site (“target”) to the second (“reference”) to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. METHODS: We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. CONCLUSION: MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. SIGNIFICANCE: Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.
format Online
Article
Text
id pubmed-10462069
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Cold Spring Harbor Laboratory
record_format MEDLINE/PubMed
spelling pubmed-104620692023-08-29 MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal Newlin, Nancy R. Kim, Michael E. Kanakaraj, Praitayini Yao, Tianyuan Hohman, Timothy Pechman, Kimberly R. Beason-Held, Lori L. Resnick, Susan M. Archer, Derek Jefferson, Angela Landman, Bennett A. Moyer, Daniel bioRxiv Article OBJECTIVE: Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site (“target”) to the second (“reference”) to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. METHODS: We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. CONCLUSION: MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. SIGNIFICANCE: Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step. Cold Spring Harbor Laboratory 2023-08-15 /pmc/articles/PMC10462069/ /pubmed/37645973 http://dx.doi.org/10.1101/2023.08.12.553099 Text en https://creativecommons.org/licenses/by-nd/4.0/This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Newlin, Nancy R.
Kim, Michael E.
Kanakaraj, Praitayini
Yao, Tianyuan
Hohman, Timothy
Pechman, Kimberly R.
Beason-Held, Lori L.
Resnick, Susan M.
Archer, Derek
Jefferson, Angela
Landman, Bennett A.
Moyer, Daniel
MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal
title MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal
title_full MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal
title_fullStr MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal
title_full_unstemmed MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal
title_short MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal
title_sort midrish: unbiased harmonization of rotationally invariant harmonics of the diffusion signal
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10462069/
https://www.ncbi.nlm.nih.gov/pubmed/37645973
http://dx.doi.org/10.1101/2023.08.12.553099
work_keys_str_mv AT newlinnancyr midrishunbiasedharmonizationofrotationallyinvariantharmonicsofthediffusionsignal
AT kimmichaele midrishunbiasedharmonizationofrotationallyinvariantharmonicsofthediffusionsignal
AT kanakarajpraitayini midrishunbiasedharmonizationofrotationallyinvariantharmonicsofthediffusionsignal
AT yaotianyuan midrishunbiasedharmonizationofrotationallyinvariantharmonicsofthediffusionsignal
AT hohmantimothy midrishunbiasedharmonizationofrotationallyinvariantharmonicsofthediffusionsignal
AT pechmankimberlyr midrishunbiasedharmonizationofrotationallyinvariantharmonicsofthediffusionsignal
AT beasonheldloril midrishunbiasedharmonizationofrotationallyinvariantharmonicsofthediffusionsignal
AT resnicksusanm midrishunbiasedharmonizationofrotationallyinvariantharmonicsofthediffusionsignal
AT archerderek midrishunbiasedharmonizationofrotationallyinvariantharmonicsofthediffusionsignal
AT jeffersonangela midrishunbiasedharmonizationofrotationallyinvariantharmonicsofthediffusionsignal
AT landmanbennetta midrishunbiasedharmonizationofrotationallyinvariantharmonicsofthediffusionsignal
AT moyerdaniel midrishunbiasedharmonizationofrotationallyinvariantharmonicsofthediffusionsignal