[HTML][HTML] Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization

F Hu, AA Chen, H Horng, V Bashyam, C Davatzikos… - NeuroImage, 2023 - Elsevier
Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …

[HTML][HTML] Deep learning in large and multi-site structural brain MR imaging datasets

M Bento, I Fantini, J Park, L Rittner… - Frontiers in …, 2022 - frontiersin.org
Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the
training, validation and testing of advanced deep learning (DL)-based automated tools …

Harmonization of cortical thickness measurements across scanners and sites

JP Fortin, N Cullen, YI Sheline, WD Taylor, I Aselcioglu… - Neuroimage, 2018 - Elsevier
With the proliferation of multi-site neuroimaging studies, there is a greater need for handling
non-biological variance introduced by differences in MRI scanners and acquisition …

[HTML][HTML] Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan

R Pomponio, G Erus, M Habes, J Doshi, D Srinivasan… - NeuroImage, 2020 - Elsevier
As medical imaging enters its information era and presents rapidly increasing needs for big
data analytics, robust pooling and harmonization of imaging data across diverse cohorts …

Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data

M Yu, KA Linn, PA Cook, ML Phillips… - Human brain …, 2018 - Wiley Online Library
Acquiring resting‐state functional magnetic resonance imaging (fMRI) datasets at multiple
MRI scanners and clinical sites can improve statistical power and generalizability of results …

DeepHarmony: A deep learning approach to contrast harmonization across scanner changes

BE Dewey, C Zhao, JC Reinhold, A Carass… - Magnetic resonance …, 2019 - Elsevier
Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks
reproducibility between protocols and scanners. It has been shown that even when care is …

[HTML][HTML] Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data

JC Beer, NJ Tustison, PA Cook, C Davatzikos… - Neuroimage, 2020 - Elsevier
While aggregation of neuroimaging datasets from multiple sites and scanners can yield
increased statistical power, it also presents challenges due to systematic scanner effects …

[HTML][HTML] On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations

M Helmer, S Warrington… - Communications …, 2024 - nature.com
Associations between datasets can be discovered through multivariate methods like
Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property …

[HTML][HTML] A multi-scanner neuroimaging data harmonization using RAVEL and ComBat

ME Torbati, DS Minhas, G Ahmad, EE O'Connor… - Neuroimage, 2021 - Elsevier
Modern neuroimaging studies frequently combine data collected from multiple scanners and
experimental conditions. Such data often contain substantial technical variability associated …

A disentangled latent space for cross-site MRI harmonization

BE Dewey, L Zuo, A Carass, Y He, Y Liu… - … conference on medical …, 2020 - Springer
Accurate interpretation and quantification of magnetic resonance imaging (MRI) is vital to
medical research and clinical practice. However, lack of MRI standardization and differences …