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Technical Note: Retrospective reduction in systematic differences across scanner changes by accounting for noise floor effects in diffusion tensor imaging
Author(s) -
Sakaie Ken,
Zhou Xiaopeng,
Lin Jian,
Debbins Josef,
Lowe Mark,
Fox Robert J.
Publication year - 2018
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13088
Subject(s) - reproducibility , scanner , noise (video) , diffusion mri , noise floor , signal to noise ratio (imaging) , robustness (evolution) , noise reduction , computer science , accounting , noise measurement , statistics , mathematics , medicine , artificial intelligence , radiology , magnetic resonance imaging , biology , business , image (mathematics) , biochemistry , gene
Purpose The purpose of this study was to determine if retrospective correction for noise floor effects can reduce systematic differences and improve reproducibility across major scanner changes. Changes in scanner configuration can negatively impact quantitative MRI studies by introducing systematic differences between measurements that are due to the instrument, not biology. Noise floor rectification is a potential source of systematic differences in diffusion tensor imaging ( DTI ). Methods Healthy volunteers were scanned before and after a major scanner change at four sites. DTI ‐based measures of tissue microstructure were calculated using a standard approach that ignores noise floor effects and using a maximum likelihood estimation ( MLE ) approach that accounts for the noise floor. Voxelwise estimates of systematic differences and reproducibility were evaluated. Results Accounting for noise floor effects can reduce the extent of systematic differences and can improve reproducibility. However, when signal levels are high, accounting for the noise floor can have a deleterious effect. An empirical metric constructed to reflect the magnitude of noise floor effects signal‐to‐noise‐floor ratio ( SNFR ). The MLE approach improves reproducibility for SNFR < 3. Conclusions Accounting for noise floor effects can boost the robustness of DTI measurements in the presence of scanner changes, potentially improving the reliability of DTI for studies of neurological disease.