Evidence map›Paper›PMID 34506916›Full record

ArticleNeuroImage2021

Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory.

Lianrui Zuo, Blake E Dewey, Yihao Liu, Yufan He, Scott D Newsome, Ellen M Mowry, Susan M Resnick, Jerry L Prince, Aaron Carass

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

47 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Unpaired Multi-Site Brain MRI Harmonization with Image Style-Guided Latent Diffusion.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026
    Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Review
  12. Review
  13. Review
  14. Disentangled latent energy-based style translation: An image-level structural MRI harmonization framework.Neural networks : the official journal of the International Neural Network Society · 2025
    Article
  15. Article
  16. Learning disentangled representations to harmonize connectome network measures.Journal of medical imaging (Bellingham, Wash.) · 2025
    Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Lianrui ZuoDepartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institute of Health, Baltimore, MD 20892, USA. Electronic address: lr_zuo@jhu.edu.
Blake E DeweyDepartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA.
Yihao LiuDepartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA.
Yufan HeDepartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA.
Scott D NewsomeDepartment of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
Ellen M MowryDepartment of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
Susan M ResnickLaboratory of Behavioral Neuroscience, National Institute on Aging, National Institute of Health, Baltimore, MD 20892, USA.
Jerry L PrinceDepartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA.
Aaron CarassDepartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA.

Funding

Neuroimaging Predictors of Cognitive Decline, Impairment, and ResilienceZIAAG000191 · NIA · NATIONAL INSTITUTE ON AGING · PI FERRUCCI, LUIGI · 2009 to 2025
$26.4M
Intramural NIH HHS ZIA AG000191
6 · The paper itself

Abstract

In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to site, which impedes consistent measurements in automatic analyses. In this paper, we propose an unsupervised MR image harmonization approach, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), which aims to alleviate contrast variations in multi-site MR imaging. Designed using information bottleneck theory, CALAMITI learns a globally disentangled latent space containing both anatomical and contrast information, which permits harmonization. In contrast to supervised harmonization methods, our approach does not need a sample population to be imaged across sites. Unlike traditional unsupervised harmonization approaches which often suffer from geometry shifts, CALAMITI better preserves anatomy by design. The proposed method is also able to adapt to a new testing site with a straightforward fine-tuning process. Experiments on MR images acquired from ten sites show that CALAMITI achieves superior performance compared with other harmonization approaches.

Indexed as

HumansImage Processing, Computer-AssistedInformation TheoryMagnetic Resonance ImagingDisentangleHarmonizationImage synthesisImage-to-image translationMagnetic resonance imaging

Identifiers

PMID34506916
PMCPMC10473284

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.