ArticleIEEE transactions on medical imaging2019
Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 29 citations in OpenAlex.
- Towards Accurate Microstructure Estimation via 3D Hybrid Graph Transformer.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- [Diffusion tensor field estimation based on 3D U-Net and diffusion tensor imaging model constraint].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- Deep learning prediction of diffusion MRI data with microstructure-sensitive loss functions.Medical image analysis · 2023Article
- Hybrid Graph Transformer for Tissue Microstructure Estimation with Undersampled Diffusion MRI Data.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Article
- [A diffusion-weighted image denoising algorithm using HOSVD combined with Rician noise corrected model].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
- Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System.Sensors (Basel, Switzerland) · 2021Article
- Gaussianization of Diffusion MRI Data Using Spatially Adaptive Filtering.Medical image analysis · 2021Article
- Denoising Diffusion MRI via Graph Total Variance in Spatioangular Domain.Computational and mathematical methods in medicine · 2021Article
- The sensitivity of diffusion MRI to microstructural properties and experimental factors.Journal of neuroscience methods · 2021Review
- Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- The impact of position-orientation adaptive smoothing in diffusion weighted imaging-From diffusion metrics to fiber tractography.PloS one · 2020Article
- Context-Aware Superpixel and Bilateral Entropy-Image Coherence Induces Less Entropy.Entropy (Basel, Switzerland) · 2019Article
- XQ-SR: Joint x-q space super-resolution with application to infant diffusion MRI.Medical image analysis · 2019Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 4 countries.
Funding
Abstract
Diffusion magnetic resonance imaging (DMRI) suffers from lower signal-to-noise-ratio (SNR) due to MR signal attenuation associated with the motion of water molecules. To improve SNR, the non-local means (NLM) algorithm has demonstrated state-of-the-art performance in noise reduction. However, existing NLM algorithms do not take into account explicitly the fact that DMRI signal can vary significantly with local fiber orientations. Applying NLM naïvely can hence blur subtle structures and aggravate partial volume effects. To overcome this limitation, we improve NLM by performing neighborhood matching in non-flat domains and removing noise with information from both x -space (spatial domain) and q -space (wavevector domain). Specifically, we first encode the q -space sampling domain using a graph. We then perform graph framelet transforms to extract robust rotation-invariant features for each sampling point in x-q space. The resulting features are employed for robust neighborhood matching to locate recurrent information. Finally, we remove noise via an NLM framework. To adapt to the various types of noise in multi-coil MR imaging, we transform the signal before denoising so that it is Gaussian-distributed, allowing noise removal to be carried out in an unbiased manner. Our method is able to more effectively locate recurrent information in white matter structures with different orientations, avoiding the blurring effects caused by naïvely applying NLM. Experiments on synthetic, repetitively-acquired, and infant DMRI data demonstrate that our method is able to preserve subtle structures while effectively removing noise.
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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.