Display 3D trajectories, with a grid interface #193
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This add "get_interpolators_from_fieldmap", supporting both real (B0 map only) and complex (Zmap = R2* + 1j * B0map) fields in arbitrary dimension (2D and 3D). The routine supports both numpy / cupy arrays and torch tensors on CPU and GPU, the latter requiring cupy due to limitations in torch.histogram / torch.histogramdd (pytorch/pytorch#69519). Calculation uses time segmentation with uniform time samples and LS coefficients (using histogram). Based on MIRT (https://github.com/JeffFessler/mirt/blob/main/mri/mri_exp_approx.m) and its Python porting from MIRTORCH (https://github.com/guanhuaw/MIRTorch/blob/master/mirtorch/linear/mri.py) and SigPy (https://github.com/mikgroup/sigpy/blob/main/sigpy/mri/util.py).
In addition, add off-resonance example.
Avoid torch cuda test case if cupy is not available.
remove deprecated get_grad Co-authored-by: Chaithya G R <[email protected]>
Co-authored-by: Guillaume Daval-Frérot <[email protected]>
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Please base yourself from master :) |
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Naa, ill wait for #180 to go in :P |
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I will get a CLI also :P |
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I seemed to have forgotten to add the example. Added it now. @Daval-G you are good to go for review whenever you get time. |
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@chaithyagr I will have a look over the week-end |
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| """ | |||
| ====================== | |||
| 3D Trajectory Display | |||
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| 3D Trajectory Display | |
| 3D Trajectory display |
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Also why is it a GPU example ?
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@chaithyagr Sorry I misplaced this comment, but could you answer it ?
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I think the reason why it's a GPU example is cause we use spread and interpolate kernels which currently is only supported on GPU backends. I'll add support for it in finufft soon
| def create_grid(grid_type, title="", **kwargs): | ||
| fig, axs = plt.subplots(3, 3, figsize=(10, 10)) | ||
| for i, (name, traj) in enumerate(trajectories.items()): | ||
| grid = get_gridded_trajectory( | ||
| traj, (64, 64, 64), grid_type=grid_type, traj_params=traj_params, **kwargs | ||
| ) | ||
| plot_slices(axs[:, i], grid, title=name) | ||
| plt.tight_layout() | ||
| plt.suptitle(title) | ||
| plt.show() |
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Why not provide trajectories and traj_params as arguments but keep grid_type and title ? It makes it a bit of a pain to read here and below
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I moved title below. But I dont know if I understand you?
| data = grid_op.raw_op.adj_op( | ||
| np.tile(np.linspace(1, 10, shots.shape[1]), (shots.shape[0],)), | ||
| None, | ||
| True, | ||
| ) |
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Don't we want to provide actual time values ? It should be more explicit in the documentation
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Also the output shape doesn't correspond to shape as expected when reading the documentation. I don't understand why this is part of that function since it doesn't relate to a grid
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It is just relative to get an understanding. Given that adjoint is not inverse, even if we give exact values, we cant expect it to be reflected the same way on image domain.
The shape would still be same as expected as we use grid_op which is set based on shape, right? Am I missing something?
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| Parameters | ||
| ---------- | ||
| shots : ndarray |
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The shots name isn't conventional in that part of the package
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What would you recommend? traj ?
| def get_gridded_trajectory( | ||
| shots: np.ndarray, | ||
| shape: Tuple, | ||
| grid_type: str = "density", | ||
| osf: int = 1, | ||
| backend: str = "gpunufft", | ||
| traj_params: dict = None, | ||
| turbo_factor: int = 176, | ||
| elliptical_samp: bool = True, | ||
| threshold: float = 1e-3, | ||
| ): |
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Overall, why have all of those features gathered into only one function ? Some of them are not related to grids.
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Also please add more comments to your code
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I think, all the features are related to gridding and how you want to "color" the trajectories on the grid.
| data = grid_op.raw_op.adj_op( | ||
| np.repeat( | ||
| np.linspace(1, 10, turbo_factor), samples.shape[0] // turbo_factor + 1 | ||
| )[: samples.shape[0]], | ||
| None, | ||
| True, | ||
| ) |
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Same problem here, the output shape doesn't seem to match the documented output
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Same as above, isnt data.shape same as shape we give as input?
| if elliptical_samp: | ||
| data[ | ||
| np.linalg.norm( | ||
| np.meshgrid( | ||
| *[np.linspace(-1, 1, sh) for sh in shape], indexing="ij" | ||
| ), | ||
| axis=0, | ||
| ) | ||
| > 1 | ||
| ] = 0 |
| @@ -0,0 +1,99 @@ | |||
| """ | |||
| ====================== | |||
| 3D Trajectory Display | |||
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Also why is it a GPU example ?
Co-authored-by: Guillaume Daval-Frérot <[email protected]>
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