# Examples

Executed when the documentation is built: every figure and number comes from
the code on the page. Each page has an *Open in
Colab* button, so you need nothing but a browser to run it.
The ideas behind them are in {doc}`/explanation/index`, and every function they
call is in {doc}`/api/index`.

## Course

Eleven lessons from a k-space tensor to a learned reconstruction, read in order
({doc}`course`).

| Lesson | Contents |
| --- | --- |
| {doc}`1. Your data as tensors </auto_examples/01-basics/01-tensors-and-commands>` | Tensors in BART's layout; BART commands on tensors |
| {doc}`2. From k-space to an image </auto_examples/01-basics/02-from-kspace-to-image>` | Undersampled Cartesian reconstruction, step by step |
| {doc}`3. Coil sensitivities </auto_examples/02-parallel-imaging/01-coil-calibration>` | Coil sensitivity estimation and its effect on the reconstruction |
| {doc}`4. Joint reconstruction of image and sensitivity maps </auto_examples/02-parallel-imaging/02-nonlinear-inversion>` | Joint estimation of image and coil sensitivities from a small calibration region |
| {doc}`5. Choosing a regularizer </auto_examples/03-regularization/01-regularized-reconstruction>` | Tikhonov, wavelet and total-variation regularization; regularization weight |
| {doc}`6. Custom encoding and solver </auto_examples/03-regularization/02-operators-and-solvers>` | Encoding operator and solver, identical to `pics` |
| {doc}`7. Basics of non-Cartesian imaging </auto_examples/04-non-cartesian/01-trajectories-and-transforms>` | Trajectories, NUFFT, density compensation, point spread function |
| {doc}`8. Radial SENSE </auto_examples/04-non-cartesian/02-radial-sense>` | Golden-angle radial SENSE |
| {doc}`9. Nonlinear inversion for T2 mapping </auto_examples/05-model-based/01-quantitative-models>` | Model-based $T_2$ mapping from raw data |
| {doc}`10. Plug-and-play reconstruction </auto_examples/06-learning/01-plug-and-play>` | Plug-and-play with a pretrained denoiser |
| {doc}`11. Training an unrolled network </auto_examples/06-learning/02-modl-with-admm>` | Unrolled network trained through ADMM |

## Tours

Standalone examples beyond the Course. Each names the lessons it assumes
({doc}`tours`).

| Tour | Contents |
| --- | --- |
| {doc}`/auto_examples/07-tours/01-readout-oversampling-and-apodization` | Readout oversampling removal; Fermi and Hann apodization |
| {doc}`/auto_examples/07-tours/02-epi-ghost-and-ramp-sampling` | EPI Nyquist ghost correction; ramp-sampling regridding |
| {doc}`/auto_examples/07-tours/03-bias-field` | Receive bias field estimation with N4 |
| {doc}`/auto_examples/07-tours/04-gradient-nonlinearity` | Gradient nonlinearity correction from spherical-harmonic coefficients |
| {doc}`/auto_examples/07-tours/05-spiral-deblurring` | Spiral off-resonance deblurring: multifrequency interpolation, time-segmented reconstruction |
| {doc}`/auto_examples/07-tours/06-navigator-motion` | Rigid head motion from orthogonal navigator planes |
| {doc}`/auto_examples/07-tours/07-epi-susceptibility-distortion` | EPI susceptibility distortion correction from reversed phase encoding |
| {doc}`/auto_examples/07-tours/08-noise-prewhitening` | Noise prewhitening and SNR |
| {doc}`/auto_examples/08-workflows/01-dynamic-golden-angle` | Dynamic golden-angle radial reconstruction with temporal total variation |
| {doc}`/auto_examples/08-workflows/02-subspace-t1-mapping` | Subspace $T_1$ mapping from a continuous inversion-recovery scan |
| {doc}`/auto_examples/08-workflows/03-maps-from-scanner-images` | $T_2$ mapping from DICOM magnitude images |
| {doc}`/auto_examples/09-learning-workflows/01-networks-for-complex-volumes` | Networks for complex multi-contrast volumes; patchwise execution |
| {doc}`/auto_examples/09-learning-workflows/02-staged-training` | Staged training of unrolled networks |
| {doc}`/auto_examples/09-learning-workflows/03-self-supervised-training` | Self-supervised training from undersampled data |
| {doc}`/auto_examples/09-learning-workflows/04-annealed-plug-and-play` | Plug-and-play with an annealed noise level |
| {doc}`/auto_examples/09-learning-workflows/05-uncertainty` | Calibrated voxel-wise uncertainty |

## Requirements

The scripts need a built `bartorch`, `brainweb-dl`, which
downloads the BrainWeb phantoms several examples build their images from, and
`matplotlib` and `cmap` for the figures:

```bash
pip install bartorch brainweb-dl matplotlib cmap
```

The learned-regularization lessons and tours additionally require `lightning`,
`torchio`, `monai` and `deepinv`, and the corrections tours
`PyHySCO`; the {doc}`course` and {doc}`tours` pages name what each needs.

```{toctree}
:hidden:

course
tours
```