wehub-resource-sync
64 lines
3.1 KiB
Markdown
64 lines
3.1 KiB
Markdown
<!--[metadata]
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title = "Dicom MRI"
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tags = ["Tensor", "MRI", "DICOM"]
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thumbnail = "https://static.rerun.io/dicom-mri/d5a434f92504e8dda8af6c7f4eded2a9d662c991/480w.png"
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thumbnail_dimensions = [480, 480]
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channel = "main"
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include_in_manifest = true
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-->
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Visualize a [DICOM](https://en.wikipedia.org/wiki/DICOM) MRI scan. This demonstrates the flexible tensor slicing capabilities of the Rerun viewer.
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<picture data-inline-viewer="examples/dicom_mri">
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<source media="(max-width: 480px)" srcset="https://static.rerun.io/dicom_mri/e39f34a1b1ddd101545007f43a61783e1d2e5f8e/480w.png">
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<source media="(max-width: 768px)" srcset="https://static.rerun.io/dicom_mri/e39f34a1b1ddd101545007f43a61783e1d2e5f8e/768w.png">
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<source media="(max-width: 1024px)" srcset="https://static.rerun.io/dicom_mri/e39f34a1b1ddd101545007f43a61783e1d2e5f8e/1024w.png">
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<source media="(max-width: 1200px)" srcset="https://static.rerun.io/dicom_mri/e39f34a1b1ddd101545007f43a61783e1d2e5f8e/1200w.png">
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<img src="https://static.rerun.io/dicom_mri/e39f34a1b1ddd101545007f43a61783e1d2e5f8e/full.png" alt="">
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</picture>
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## Used Rerun types
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[`Tensor`](https://www.rerun.io/docs/reference/types/archetypes/tensor), [`TextDocument`](https://www.rerun.io/docs/reference/types/archetypes/text_document)
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## Background
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Digital Imaging and Communications in Medicine (DICOM) serves as a technical standard for the digital storage and transmission of medical images. In this instance, an MRI scan is visualized using Rerun.
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## Logging and visualizing with Rerun
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The visualizations in this example were created with just the following line.
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```python
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rr.log("tensor", rr.Tensor(voxels_volume_u16, dim_names=["right", "back", "up"]))
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```
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A `numpy.array` named `voxels_volume_u16` representing volumetric MRI intensities with a shape of `(512, 512, 512)`.
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To visualize this data effectively in Rerun, we can log the `numpy.array` as [`Tensor`](https://www.rerun.io/docs/reference/types/archetypes/tensor) to the `tensor` entity.
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In the Rerun Viewer you can also inspect the data in detail. The `dim_names` provided in the above call to `rr.log` help to
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give semantic meaning to each axis. After selecting the tensor view, you can adjust various settings in the Blueprint
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settings on the right-hand side. For example, you can adjust the color map, the brightness, which dimensions to show as
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an image and which to select from, and more.
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## Run the code
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To run this example, make sure you have the Rerun repository checked out and the latest SDK installed:
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```bash
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pip install --upgrade rerun-sdk # install the latest Rerun SDK
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git clone git@github.com:rerun-io/rerun.git # Clone the repository
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cd rerun
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git checkout latest # Check out the commit matching the latest SDK release
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```
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Install the necessary libraries specified in the requirements file:
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```bash
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pip install -e examples/python/dicom_mri
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```
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To experiment with the provided example, simply execute the main Python script:
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```bash
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python -m dicom_mri # run the example
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```
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If you wish to customize it, explore additional features, or save it, use the CLI with the `--help` option for guidance:
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```bash
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python -m dicom_mri --help
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```
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