Comments (4)
Hello @bigponglee, this is entirely expected. By using the NUFFT you are applying a coordinate transformation to an integral without using the Jacobian to normalize the new unit of integration. There are methods such as density compensation to partially, but not completely compensate for the effect.
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Hi @mmuckley,
thanks for the explanation! I'm also encountering this problem and using an ugly operator normalization to imperfectly fix it. Would you share some papers you mentioned as density compensation? It would be very helpful! Many thanks!
Best,
Wenqi
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@wenqihuang several people use this paper as a reference: https://onlinelibrary.wiley.com/doi/abs/10.1002/(SICI)1522-2594(199901)41:1%3C179::AID-MRM25%3E3.0.CO;2-V
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Closing as this does not document a bug.
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Related Issues (20)
- Thread allocation issues with batched density compensation HOT 11
- Basic KB-NUFFT Example HOT 6
- Failed for 3D non-Cartesian trajectory
- MriSenseNufft, AdjMriSenseNufft, AdjKbNufft, ToepSenseNufft removed
- Deprecation warning and shutdown HOT 3
- Accuracy for numpoints = 2 - 7
- nonuniform discrete Fourier transform of type II HOT 4
- grid_size HOT 3
- Make compatible with latest PyTorch HOT 7
- KB-NUFFT hangs when used in PyTorch DataLoader with num_workers > 0 HOT 3
- Allow user control of threading
- Could you give an example of implementing a two-dimensional Fourier transform by use torchkbnufft?
- Performance vs FFT HOT 6
- Toeplitz Kernel has boundary artifacts HOT 9
- Support for NFFTs of real valued Arrays HOT 6
- Basic 1D case
- Batched input with varying size leads to TorchScript error HOT 2
- KbInterpAdjoint gives mismatch results? HOT 3
- Gradients w.r.t. coordinates in ktraj
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