Utils for gathering, aggregation and handling metadata from DICOM files.
# Installation
From pip
```
pip install dicom-csv
```
or from GitHub
```bash
git clone https://github.com/neuro-ml/dicom-csv
cd dicom-csv
pip install -e .
```
# Example `join_tree`
```python
>>> from dicom_csv import join_tree
>>> folder = '/path/to/folder/'
>>> meta = join_tree(folder, verbose=2)
>>> meta.head(3)
```
| AccessionNumber | AcquisitionDate | ... | WindowCenter | WindowWidth |
| -------------: | -------------: | :---: | --------: | :---------: |
|000002621237 |20200922 |... |-500.0 |1500.0 |
|000002621237 |20200922 |... |-40.0 |400.0 |
|000002621237 |20200922 |... |-500.0 |1500.0 |
3 rows x 155 columns
# Example load 3D image
from a series of dicom files (each containing 2D image)
```python
from dicom_csv import join_tree, order_series, stack_images
from pydicom import dcmread
from pathlib import Path
# 1. Collect metadata from all dicom files
folder = Path('/path/to/folder/')
meta = join_tree(folder, verbose=2)
# 2. Select series to load
uid = '...' # unique identifier of a series you want to load,
# you could list them by `meta.SeriesInstanceUID.unique()`
series = meta.query("SeriesInstanceUID==@uid")
# 3. Read files & combine them into a single volume
images2d = [dcmread(folder / row[1].PathToFolder / row[1].FileName) for row in series.iterrows()]
image3d = stack_images(order_series(images2d))
```
# Documentation
You can find the documentation [here](https://dicom-csv.readthedocs.io/en/latest/index.html).
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