ArticleJournal of magnetic resonance (San Diego, Calif. : 1997)2016
Use of marginal distributions constrained optimization (MADCO) for accelerated 2D MRI relaxometry and diffusometry.
Article in Journal of magnetic resonance (San Diego, Calif. : 1997), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed.
- MultidimensionalbioRxiv : the preprint server for biology · 2026Article
- Diffusion-relaxation MRI as virtual histology: separable microstructural signatures of AD pathology in ex vivo human brain.Research square · 2026Article
- Separable multidimensional MRI signatures of cellular and structural pathology in Alzheimer's disease.bioRxiv : the preprint server for biology · 2026Article
- Quantifying Transmembrane Water Exchange by Diffusion NMR Methods: From Yeast Cells to Optic Nerve Ex Vivo.NMR in biomedicine · 2026Article
- Multi-TE Diffusion MRI Dataset for Exploring Combined Diffusion-Relaxometry Methods in Microstructure Imaging.Scientific data · 2025Article
- Data Processing in Multidimensional MRI For Biomarker Identification: Is It Necessary?bioRxiv : the preprint server for biology · 2025Article
- Multidimensional MRI reveals cortical astrogliosis linked to dementia in Alzheimer's disease.Brain communications · 2025Article
- Joint estimation of compartment-specific TMagnetic resonance in medicine · 2025Article
- An Efficient Algorithm for Spatial-Spectral Partial Volume Compartment Mapping with Applications to Multicomponent Diffusion and Relaxation MRI.IEEE transactions on computational imaging · 2025Article
- Maximum-entropy and subspace methods for high-resolution relaxation-diffusion distribution estimation.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Time-division multiplexing (TDM) sequence removes bias in TMagnetic resonance in medicine · 2024Article
- The Diffusion Exchange Ratio (DEXR): A minimal sampling of diffusion exchange spectroscopy to probe exchange, restriction, and time-dependence.bioRxiv : the preprint server for biology · 2024Article
- Assessment of precision and accuracy of brain white matter microstructure using combined diffusion MRI and relaxometry.Human brain mapping · 2024Article
- Time-division multiplexing (TDM) sequence removes bias in T2 estimation and relaxation-diffusion measurements.bioRxiv : the preprint server for biology · 2024Article
- Novel pore size-controlled, susceptibility matched, 3D-printed MRI phantoms.Magnetic resonance in medicine · 2024Article
- Article
- Variability of multidimensional diffusion-relaxation MRI estimates in the human brain.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Data inversion of multi-dimensional magnetic resonance in porous media.Magnetic resonance letters · 2023Article
- Mapping astrogliosis in the individual human brain using multidimensional MRI.Brain : a journal of neurology · 2023Article
- The distortions of the free water model for diffusion MRI data when assuming single compartment relaxometry and proton density.Physics in medicine and biology · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
Measuring multidimensional (e.g., 2D) relaxation spectra in NMR and MRI clinical applications is a holy grail of the porous media and biomedical MR communities. The main bottleneck is the inversion of Fredholm integrals of the first kind, an ill-conditioned problem requiring large amounts of data to stabilize a solution. We suggest a novel experimental design and processing framework to accelerate and improve the reconstruction of such 2D spectra that uses a priori information from the 1D projections of spectra, or marginal distributions. These 1D marginal distributions provide powerful constraints when 2D spectra are reconstructed, and their estimation requires an order of magnitude less data than a conventional 2D approach. This marginal distributions constrained optimization (MADCO) methodology is demonstrated here with a polyvinylpyrrolidone-water phantom that has 3 distinct peaks in the 2D D-T1 space. The stability, sensitivity to experimental parameters, and accuracy of this new approach are compared with conventional methods by serially subsampling the full data set. While the conventional, unconstrained approach performed poorly, the new method had proven to be highly accurate and robust, only requiring a fraction of the data. Additionally, synthetic T1-T2 data are presented to explore the effects of noise on the estimations, and the performance of the proposed method with a smooth and realistic 2D spectrum. The proposed framework is quite general and can also be used with a variety of 2D MRI experiments (D-T2,T1-T2,D-D, etc.), making these potentially feasible for preclinical and even clinical applications for the first time.
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