A joint space‐angle regularization approach for single 4D diffusion image super‐resolution
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Summary
A joint space‐angle regularization approach is proposed to reconstruct HSAR diffusion signals from a single 4D low resolution (LR) dMRI, which is down‐sampled in both 3D‐space and q‐space.
- Type
- article
- Published
- 2018-11-01
- Cited by
- 5
- References
- 58
- OpenAlex
- https://openalex.org/W2801184066
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:4998473
Keywords
Regularization (linguistics), Computer science, Image resolution, Voxel, Upsampling
References
- An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images
- A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging
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- Designing Single- and Multiple-Shell Sampling Schemes for Diffusion MRI Using Spherical Code
- Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes
- Fast and accurate reconstruction of HARDI data using compressed sensing
- Accelerated Diffusion Spectrum Imaging with Compressed Sensing using Adaptive Dictionaries
- General and Efficient Super-Resolution Method for Multi-slice MRI
- The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
- Rician Noise Removal in Diffusion Tensor MRI
- Super‐resolution reconstruction of diffusion parameters from diffusion‐weighted images with different slice orientations
- MR diffusion tensor spectroscopy and imaging.
- Super‐resolution for multislice diffusion tensor imaging
- How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging
- Diffusion Tensor Imaging and Beyond
- Q‐ball imaging
- Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression
- Non-local MRI upsampling
- Tikhonov Regularization and Total Least Squares
- Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency
Cited by
- Fast and accurate reconstruction of HARDI using a 1D encoder-decoder convolutional network
- Deep learning prediction of diffusion MRI data with microstructure-sensitive loss functions
- Super-resolution of diffusion-weighted images using space-customized learning model
- Spherical Harmonics Representation Learning for High-Fidelity and Generalizable Super-Resolution in Diffusion MRI
- Spatial-Angular Representation Learning for High-Fidelity Continuous Super-Resolution in Diffusion MRI
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