ArticleIEEE transactions on medical imaging2020
Joint RElaxation-Diffusion Imaging Moments to Probe Neurite Microstructure.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed.
- Informed Dictionary-Guided Monte Carlo Inversion for Robust and Reproducible Multidimensional MRI.Magnetic resonance in medicine · 2026Article
- A relaxation-diffusion MRI dataset of aging mouse brains at 9.4 Tesla.Scientific data · 2025Article
- A deep learning approach to multi-fiber parameter estimation and uncertainty quantification in diffusion MRI.Medical image analysis · 2025Article
- Romer-EPTI: Rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale diffusion MRI and microstructure imaging.Magnetic resonance in medicine · 2025Article
- Single-shot echo planar time-resolved imaging for multi-echo functional MRI and distortion-free diffusion imaging.Magnetic resonance in medicine · 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
- 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
- Romer-EPTI: rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale dMRI and microstructure imaging.bioRxiv : the preprint server for biology · 2024Article
- Article
- An open relaxation-diffusion MRI dataset in neurosurgical studies.Scientific data · 2024Article
- Relaxation-Diffusion Spectrum Imaging for Probing Tissue Microarchitecture.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Multi-readout DWI with a reduced FOV for studying the coupling between diffusion andMagnetic resonance in medicine · 2023Article
- Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact.NeuroImage · 2022Review
- Accelerating joint relaxation-diffusion MRI by integrating time division multiplexing and simultaneous multi-slice (TDM-SMS) strategies.Magnetic resonance in medicine · 2022Article
- Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review.NeuroImage · 2022Review
- Combined diffusion-relaxometry microstructure imaging: Current status and future prospects.Magnetic resonance in medicine · 2021Review
- SNR-enhanced diffusion MRI with structure-preserving low-rank denoising in reproducing kernel Hilbert spaces.Magnetic resonance in medicine · 2021Article
- Toward nonparametric diffusion-Magnetic resonance in medicine · 2021Article
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5 authors.
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Abstract
Joint relaxation-diffusion measurements can provide new insight about the tissue microstructural properties. Most recent methods have focused on inverting the Laplace transform to recover the joint distribution of relaxation-diffusion. However, as is well-known, this problem is notoriously ill-posed and numerically unstable. In this work, we address this issue by directly computing the joint moments of transverse relaxation rate and diffusivity, which can be robustly estimated. To zoom into different parts of the joint distribution, we further enhance our method by applying multiplicative filters to the joint probability density function of relaxation and diffusion and compute the corresponding moments. We propose an approach to use these moments to compute several novel scalar indices to characterize specific properties of the underlying tissue microstructure. Furthermore, for the first time, we propose an algorithm to estimate diffusion signals that are independent of echo time based on the moments of the marginal probability density function of diffusion. We demonstrate its utility in extracting tissue information not contaminated with multiple intra-voxel relaxation rates. We compare the performance of four types of filters that zoom into tissue components with different relaxation and diffusion properties and demonstrate it on an in-vivo human dataset. Experimental results show that these filters are able to characterize heterogeneous tissue microstructure. Moreover, the filtered diffusion signals are also able to distinguish fiber bundles with similar orientations but different relaxation rates. The proposed method thus allows to characterize the neural microstructure information in a robust and unique manner not possible using existing techniques.
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