ArticleMedical image analysis2023
MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data.
Article in Medical image analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.Frontiers in neuroscience · 2024Pooled it
- Imaging the hallmarks of cancer.Nature reviews. Cancer · 2026Review
- Advances in artificial intelligence for neuroimaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
- Evaluation of Image-Level Harmonization Methods for Multi-Center MR Neuroimaging.Journal of magnetic resonance imaging : JMRI · 2026Article
- Article
- Rethinking MRI as a measurement device through modular and portable pipelines.Magma (New York, N.Y.) · 2025Review
- Enhancing consistency in brain imaging research for population neuroimaging.Nature protocols · 2025Article
- WMH Contributions to Cognitive Impairment: Rationale and Design of the Diverse VCID Study.Stroke · 2025Review
- HeteroMRI: Robust white matter abnormality classification across multi-scanner MRI data.GigaScience · 2025Article
- Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.La Radiologia medica · 2025Article
- Superpixel-ComBat modeling: A joint approach for harmonization and characterization of inter-scanner variability in T1-weighted images.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Multi-site harmonization for magnetoencephalography spectral power data.Imaging neuroscience (Cambridge, Mass.)Article
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10 authors.
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Abstract
Large-scale data obtained from aggregation of already collected multi-site neuroimaging datasets has brought benefits such as higher statistical power, reliability, and robustness to the studies. Despite these promises from growth in sample size, substantial technical variability stemming from differences in scanner specifications exists in the aggregated data and could inadvertently bias any downstream analyses on it. Such a challenge calls for data normalization and/or harmonization frameworks, in addition to comprehensive criteria to estimate the scanner-related variability and evaluate the harmonization frameworks. In this study, we propose MISPEL (Multi-scanner Image harmonization via Structure Preserving Embedding Learning), a supervised multi-scanner harmonization method that is naturally extendable to more than two scanners. We also designed a set of criteria to investigate the scanner-related technical variability and evaluate the harmonization techniques. As an essential requirement of our criteria, we introduced a multi-scanner matched dataset of 3T T1 images across four scanners, which, to the best of our knowledge is one of the few datasets of this kind. We also investigated our evaluations using two popular segmentation frameworks: FSL and segmentation in statistical parametric mapping (SPM). Lastly, we compared MISPEL to popular methods of normalization and harmonization, namely White Stripe, RAVEL, and CALAMITI. MISPEL outperformed these methods and is promising for many other neuroimaging modalities.
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