Challenges and Opportunities in dMRI Data Harmonization

Zhu, Alyssa H.; Moyer, Daniel; Nir, Talia M.; Thompson, Paul M.; Jahanshad, Neda

doi:10.1007/978-3-030-05831-9_13

Cited by 28 publications

(32 citation statements)

References 60 publications

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“…Most multi-site neuroimaging studies are susceptible to variability across sites. Variability in dMRI studies is due in part to heterogeneity in acquisition protocols, scanning parameters, and scanner manufacturers (Zhu et al, 2009;Zhu et al, 2011;Zhu et al, 2018). Anisotropy and diffusivity maps are affected by angular and spatial resolution (Alexander et al, 2001;Kim et al, 2006;Zhan et al, 2010), the number of DWI directions (Giannelli et al, 2009), and the number of acquired b-values (Correia et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

“…In addition to ComBat, a number of harmonization approaches have recently been proposed at various stages of analysis (Tax et al, 2018;Zhu et al, 2018). Site differences can be accounted for at the time of overall group inference, such as with the random-effects regression level correction used here, or by using a meta-analysis approach in lieu of pooling data (Thompson et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

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Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

Initiative

2018

Preprint

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Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer's Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4±7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FA DTI ) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FA TDF ), in 24 bilaterally averaged white matter regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FA DTI . We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and Alzheimer's disease: the Alzheimer's Disease Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampalcingulum and fornix (crus) / stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum and uncinate fasciculus for associations between axial or mean diffusivity and CDR-sob. FA TDF detected robust widespread associations with clinical measures, while FA DTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

Initiative

2018

Preprint

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“…We demonstrate our proposed method on the MICCAI Computational Diffusion MRI challenge dataset [41,51,59], showing substantial improvement compared to a recently published baseline method. We also introduce technical improvements to the training of neural architectures on diffusion-weighted data, and discuss the limitations and error modes of our proposed method.…”

Section: Introductionmentioning

confidence: 87%

“…Harmonization has been an acknowledged problem in MR imaging and specifically diffusion imaging for some time [59]. Numerous studies have noted significant differences in diffusion summary measures (e.g., fractional anisotropy; FA) between scanners and sites [44,53,54].…”

Section: Relevant Prior Workmentioning

confidence: 99%

Scanner invariant representations for diffusion MRI harmonization

Moyer

Steeg

Tax

et al. 2020

Magnetic Resonance in Med

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114

106

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Purpose: In the present work we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation.Theory and Methods: Pooled imaging data from multiple sources are subject to variation between the sources. Correcting for these biases has become very important as imaging studies increase in size and multi-site cases become more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory-based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to underlying structures. To implement this, we use a deep learning method based on variational auto-encoders (VAE) to construct scanner invariant encodings of the imaging data.Results: To evaluate our method, we use training data from the 2018 MICCAI Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context. Conclusion:As imaging studies continue to grow, the use of pooled multi-site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data.

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“…Meta-analysis is a popular statistical analysis technique in biomedical research that combines results of independent multisite and/or longitudinal studies. The general concept is to perform a group-wise statistical analysis separately for each site, followed by a weighted combination of effect size over the different studies to strengthen conclusions about the research question (Zhu et al, 2019). Meta-analysis is useful to pool retrospective data with sample sizes that are too small to draw valid conclusions independently (Petitti, 1994).…”

Section: Meta-analysismentioning

confidence: 99%

Harmonization of Brain Diffusion MRI: Concepts and Methods

et al. 2020

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MRI diffusion data suffers from significant inter-and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and joint analysis of diffusion data across sites and over time, there is a clear need for robust data harmonization methods. This review article provides a comprehensive overview of diffusion data harmonization concepts and methods, and their limitations. Overall, the methods for the harmonization of multi-site diffusion images can be categorized in two main groups: diffusion parametric map harmonization (DPMH) and diffusion weighted image harmonization (DWIH). Whereas DPMH harmonizes the diffusion parametric maps (e.g., FA, MD, and MK), DWIH harmonizes the diffusionweighted images. Defining a gold standard harmonization technique for dMRI data is still an ongoing challenge. Nevertheless, in this paper we provide two classification tools, namely a feature table and a flowchart, which aim to guide the readers in selecting an appropriate harmonization method for their study.

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Challenges and Opportunities in dMRI Data Harmonization

Cited by 28 publications

References 60 publications

Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

Scanner invariant representations for diffusion MRI harmonization

Harmonization of Brain Diffusion MRI: Concepts and Methods

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