https://github.com/agahkarakuzu/qlayers

Quantitative layer based analysis for renal magnetic resonance imaging.

https://github.com/agahkarakuzu/qlayers

Repository

Quantitative layer based analysis for renal magnetic resonance imaging.

https://github.com/agahkarakuzu/qlayers/blob/main/

# QLayers

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Quantitative layer based analysis for renal magnetic resonance imaging.

## Installation

The easiest way to install `qlayers`is via `pip`:

```bash
pip install qlayers
```

or if you're a `conda` user:

```bash
conda install qlayers -c conda-forge
```

Alternatively, you can install `qlayers`from source in pips editable mode:

```bash
git clone https://github.com/alexdaniel654/qlayers.git
cd qlayers
pip install -e .
```

## Quick Start

For a more thorough example of how to use `qlayers` see the tutorials section of this reposetry/documentation, however if
you want to get started, the snippet of code below should get you going.

```python
import nibabel as nib
from qlayers import QLayers

mask_img = nib.load("kidney_mask.nii.gz")
t2star_img = nib.load("t2star_map.nii.gz")

qlayers = QLayers(mask_img, pelvis_dist=10)
qlayers.add_map(t2star_img, "t2star")

df = qlayers.get_df(format="wide")
df.groupby("layer").median().loc[:, "t2star"].plot(
    xlabel="Depth (mm)", ylabel="$T_2^*$ (ms)"
)
```





## Theory

### Background

The premise behind `qlayers` was first proposed by [Pruijm et al](https://doi.org/10.1007/s10334-019-00808-5) and is based
on the idea to segment the kidney into layers based on each voxels distance from the surface of the kidney. The average of
a quantitative parameter can be calculated for each layer producing profiles of, for example, T2* with
depth. The outer and inner layers are analogous to the cortex and medulla respectively while the gradient of the profile is
representative of the cortico-medullary difference. `qlayers` extends this idea by allowing the user to define layers based on
a 3D mask and apply the layer to any quantitative parameter.

### Generating Layers

Layers are generated via the process outlined in the figure below.




a i. Shows the mask thats input to the `QLayers` class. This mask then has any holes smaller than `fill_ml` filled as these are
most likely cysts
and therefore not cortical surfaces, a ii. The mask is then converted from a voxel representation to a mesh surface
representation, b i, this mesh is then smoothed because anatomical scans of the kidneys often have a low through-plane
resolution, b ii. The distance from the centre of each voxel in the kidneys to the closest surface on the mesh is then
calculated, b iii. As the tissue adjacent to the renal pelvis is not representative of the medulla, this is automatically
excluded from the resulting depth maps. Fist the pelvis is automatically segmented, c i, and the distance from each voxel
in the kidneys to the pelvis calculated as above, c ii. Voxels closer than a specified threshold `pelvis_dist` are then
excluded from the depth maps, c iii. Finally, a layer image is generated by quantising the depth map to a desired layer
thickness, typically 1 mm although shown with 5 mm layers here for illustrative purposes, d.

### Applying Layers to Quantitative Data

If the `space` parameter of the `QLayers` object is set to `layers`, when a quantitative map is added to the `QLayers`
object, it is resampled to the same resolution and orientation as the layers. If the `space` parameter is set to `map` then
the layers are resampled to the resolution and orientation of the quantitative map. In both cases, Pandas DataFrames can
be generated with the quantitative value, depth and layer each voxel is in. These DataFrames can then be used for further
calculations such as generating profiles or linear regressions to explore the cortico-medullary difference. Some example
voxels are shown in the table below.

| Depth | Layer | T2*  | R2*  |
|-------|-------|------|------|
| 0     | 0     | 57   | 17.6 |
| 13.2  | 14    | 35.5 | 28.2 |
| 10.2  | 11    | 60.9 | 16.4 |
| 3.05  | 4     | 51.6 | 19.4 |
| 9.33  | 10    | 42.8 | 23.3 |
| 10.4  | 11    | 29.6 | 33.8 |
| 8.63  | 9     | 37.5 | 26.7 |
| 6.66  | 7     | 49.2 | 20.3 |
| 19.8  | 20    | 42.8 | 23.3 |
| 12.1  | 13    | 39.4 | 25.4 |

## Citing 3DQLayers

If you have used 3DQLayers in your research, please cite the following conference abstract:

[Daniel AJ, Francis ST. Volumetric Layer Based Analysis for Quantitative Renal MRI. In: Proc. Intl. Soc. Mag. Reson. Med. 33. Singapore; 2024:2748.](https://www.researchgate.net/publication/380534128_Volumetric_Layer_Based_Analysis_for_Quantitative_Renal_MRI)

Alternatively, if you want to cite a specific version of this software, each release has an individual DOI on Zenodo, the
DOI for the latest release can be [found here](https://zenodo.org/doi/10.5281/zenodo.12707172).

## Contributing
We welcome contributions to QLayers, a full contributing guide can be found [here](.github/CONTRIBUTING.md).

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