ArticleMedical image analysis2011
DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting.
Article in Medical image analysis, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 199 papers.
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Who cites it
199 citing papers in PubMed.
- Prediction of Treatment Response to Neoadjuvant Chemotherapy for Breast Cancer via Early Changes in Tumor Heterogeneity Captured by DCE-MRI Registration.Scientific reports · 2019Trial
- Feasibility of multi-parametric PET and MRI for prediction of tumour recurrence in patients with glioblastoma.European journal of nuclear medicine and molecular imaging · 2019Trial
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- Batch Effect Correction for Neuroimaging Data with Heterogeneous Spatial Correlations.bioRxiv : the preprint server for biology · 2026Article
- A feasibility study on multimodal CT-MRI registration using segmentation aid and CoLlAGe feature extraction approach.Abdominal radiology (New York) · 2026Article
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- Taming dimensionality in big neuroimaging data: Efficient orthonormal projective NMF via stochastic learning and data compression.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- A genetically informed brain atlas for enhancing brain imaging genomics.Nature communications · 2025Article
- GBM-Reservoir: Brain tumor (Glioblastoma Multiforme) MRI dataset collection with ground truth segmentation masks.Data in brief · 2025Article
- BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy (BONBID-HIE): I. MRI and Lesion Labeling.Scientific data · 2025Article
- Mapping interictal discharges using intracranial EEG-fMRI to predict postsurgical outcomes.Brain : a journal of neurology · 2024Article
- Deep learning of structural MRI predicts fluid, crystallized, and general intelligence.Scientific reports · 2024Article
- Longitudinal registration of TMagnetic resonance imaging · 2024Article
- Brain aging patterns in a large and diverse cohort of 49,482 individuals.Nature medicine · 2024Article
- Deep learning-based deformable image registration with bilateral pyramid to align pre-operative and follow-up magnetic resonance imaging (MRI) scans.Quantitative imaging in medicine and surgery · 2024Article
- Anatomy-aware and acquisition-agnostic joint registration with SynthMorph.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- MULTISCALE ESTIMATION OF MORPHOMETRICITY FOR REVEALING NEUROANATOMICAL BASIS OF COGNITIVE TRAITS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Autism-associated brain differences can be observed in utero using MRI.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Optimal approaches to analyzing functional MRI data in glioma patients.Journal of neuroscience methods · 2024Article
- Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.Nature communications · 2024Article
139 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
Abstract
A general-purpose deformable registration algorithm referred to as "DRAMMS" is presented in this paper. DRAMMS bridges the gap between the traditional voxel-wise methods and landmark/feature-based methods with primarily two contributions. First, DRAMMS renders each voxel relatively distinctively identifiable by a rich set of attributes, therefore largely reducing matching ambiguities. In particular, a set of multi-scale and multi-orientation Gabor attributes are extracted and the optimal components are selected, so that they form a highly distinctive morphological signature reflecting the anatomical and geometric context around each voxel. Moreover, the way in which the optimal Gabor attributes are constructed is independent of the underlying image modalities or contents, which renders DRAMMS generally applicable to diverse registration tasks. A second contribution of DRAMMS is that it modulates the registration by assigning higher weights to those voxels having higher ability to establish unique (hence reliable) correspondences across images, therefore reducing the negative impact of those regions that are less capable of finding correspondences (such as outlier regions). A continuously-valued weighting function named "mutual-saliency" is developed to reflect the matching uniqueness between a pair of voxels implied by the tentative transformation. As a result, voxels do not contribute equally as in most voxel-wise methods, nor in isolation as in landmark/feature-based methods. Instead, they contribute according to the continuously-valued mutual-saliency map, which dynamically evolves during the registration process. Experiments in simulated images, inter-subject images, single-/multi-modality images, from brain, heart, and prostate have demonstrated the general applicability and the accuracy of DRAMMS.
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