# Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional Autoencoder
[](https://badge.fury.io/py/dmri-rcnn)
This project enhances the angular resolution of dMRI data through the use of a Recurrent CNN. This codebase is associated with the
following paper. Please cite the paper if you use this model:
[Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional Autoencoder](https://arxiv.org/abs/2203.15598) [MIDL 2022]
## Table of contents
* [Installation](#installation)
* [Inference](#inference)
* [Training](#training)
* [Docker](#docker)
* [Spherical Harmonic Baseline](#spherical-harmonic-baseline)
## Installation
`dMRI-RCNN` can be installed by via pip:
```bash
pip install dmri-rcnn
```
### Requirements
`dMRI-RCNN` uses [TensorFlow](https://www.tensorflow.org/) as the deep learning architecture. To enable [GPU usage within TensorFlow](https://www.tensorflow.org/install/gpu), you should ensure the appropriate prerequisites are installed.
Listed below are the requirements for this package.
* `tensorflow>=2.6.0`
* `numpy`
* `einops`
* `nibabel`
* `tqdm`
## Inference
Once installed, use `run_dmri_rcnn.py` to perform inference of new dMRI volumes. Below lists the data requirements to use the script, and the command-line arguments available for inference.
### Data
To run this script, dMRI data is required in the following format:
* Context dMRI file. The dMRI data used as context within the model to infer other volumes
* File format: `NIfTI`
* Single-shell: containing only one b-value.
* Dimensions: `(i, j, k, q_in)`.
* `(i, j, k)` are the spatial dimensions of the data
* `q_in` number of samples within the q-space dimension. This can either be `6`, `10`, or `30` and will affect which of the trained models is used.
* Context b-vector file. The corresponding b-vectors for the context dMRI file.
* File format: text file, whitespace delimited.
* `3` rows corresponding to the `x, y, z` co-ordinates of q-space
* `q_in` columns corresponding to the q-space directions sampled. `q_in` must either be `6`, `10`, or `30`.
* Target b-vector file. The corresponding b-vectors for the inferred dMRI data.
* File format: text file, whitespace delimited.
* `3` rows corresponding to the `x, y, z` co-ordinates of q-space
* `q_out` columns corresponding to the q-space directions sampled.
* Brain mask file. Binary brain mask file for dMRI data.
* File format: `NIfTI`
* Dimensions: `(i, j, k)`. Same spatial dimensions as used in the dMRI data.
The script will create the following data:
* Inferred dMRI file. dMRI volumes inferred from the model as defined by the target b-vectors.
* File format: `NIfTI`
* Dimensions: `(i, j, k, q_out)`.
* `q_out` number of samples within the q-space dimension. This can any number, though using higher numbers will require more GPU memory if using.
### Command-line
Bring up the following help message via `run_dmri_rcnn.py -h`:
```
usage: `run_dmri_rcnn.py` [-h] -dmri_in DMRI_IN -bvec_in BVEC_IN -bvec_out BVEC_OUT -mask MASK -dmri_out DMRI_OUT -s {1000,2000,3000} [-m {1,3}] [-c] [-b BATCH_SIZE]
optional arguments:
-h, --help show this help message and exit
-dmri_in DMRI_IN Context dMRI NIfTI volume. Must be single-shell and contain q_in 3D volumes
-bvec_in BVEC_IN Context b-vector text file. Whitespace delimited with 3 rows and q_in columns
-bvec_out BVEC_OUT Target b-vector text file. Whitespace delimited with 3 rows and q_out columns
-mask MASK Brain mask NIfTI volume. Must have space spatial dimensions as dmri_in.
-dmri_out DMRI_OUT Inferred dMRI NIfTI volume. This will contain q_out inferred volumes.
-s {1000,2000,3000}, --shell {1000,2000,3000}
Shell to perform inference with. Must be same shell as context/target dMRI and b-vectors
-m {1,3}, --model-dim {1,3}
Model dimensionality, choose either 1 or 3. Default: 3.
-c, --combined Use combined shell model. Currently only applicable with 3D model and 10 q_in.
-n, --norm Perform normalisation using 99 percentile of data. Only implemented with --combined flag, and only for q_in = 10
-b BATCH_SIZE, --batch-size BATCH_SIZE
Batch size to run model inference with.
```
***N.B.** Weights are downloaded and stored within `~/.dmri_rcnn` by default. To store weights in a different directory, set environment variable `DMRI_RCNN_DIR="/your/custom/directory"`*
#### Example
The following example performs `b = 1000` inference with the 3D dMRI RCNN on **HCP data**.
```bash
run_dmri_rcnn.py -dmri_in context_dmri.nii.gz -bvec_in context_bvecs -bvec_out target_bvecs -mask brain_mask.nii.gz -dmri_out inferred_dmri.nii.gz -s 1000 -m 3
```
This example would take ~2 minutes to infer 80 volumes on an `NVIDIA RTX 3080`.
To perform inference on data outside of the HCP dataset, use the flags `-c` and `-n`. This is currently only implemented for $q_{in} = 10$.
## Training
Below are details on how to train a given model, and the preprocessing steps involved.
### Data Pre-Processing
A training dataset is typically too large to fit into memory all at once. To overcome this, this project uses TensorFlow's `.tfrecord` file format and the
[tf.data.Dataset](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) API. Therefore training data should be saved in this format before starting. Below is an example on how to do this using the `dMRI-RCNN` project.
```python
import numpy as np
from dmri_rcnn.core import io
from dmri_rcnn.core.processing import save_tfrecord_data
# First load a subject into memory
dmri, _ = io.load_nifti('/path/to/dmri/data.nii.gz')
mask, _ = io.load_nifti('/path/to/brain/mask.nii.gz', dtype=np.int8)
bvecs = io.load_bvec('/path/to/dmri/bvecs') # bvecs & bvals should be in FSL format
bvals = io.load_bval('/path/to/dmri/bvals')
# Optionally crop image data
dmri, mask = io.autocrop_dmri(dmri, mask)
# .tfrecord format uses a maximum filesize of 2 GiB, therefore for high
# resolution dMRI data, the image may need to be split into smaller parts
# to do this use the function below. It is recommended to first try to save
# each subject as a whole before splitting the image into separate files.
dmri_list = io.split_image_to_octants(dmri)
mask_list = io.split_image_to_octants(mask)
# Now save data in .tfrecord format
save_tfrecord_data(dmri, bvecs, bvals, mask, '/path/to/saved/data.tfrecord')
# Alternatively save the list of image parts if dmri is too large
for i in range(len(dmri_list)):
save_tfrecord_data(dmri_list[i], bvecs, bvals, mask_list[i], '/path/to/saved/data' + str(i) + '.tfrecord')
```
### Training a Model
Once pre-processing is complete, you can then train a model.
```python
from dmri_rcnn.core.weights import get_weights
from dmri_rcnn.core.model import get_1d_autoencoder, get_3d_autoencoder
from dmri_rcnn.core.processing import TrainingProcessor, TrainingProcessorNorm
# If we want to fine-tune the model we can load the previously obtained weights.
# In this example we'll load the weights for the 3D RCNN trained on the b = 1000
# shell and 6 q-space samples per input.
weights = get_weights(model_dim=3, shell=1000, q_in=6)
# Now we can instantiate the pre-compiled 3D model
model = get_3d_autoencoder(weights) # Omit the weights argument to load without pre-trained weights
# Instantiate the training processor
processor = TrainingProcessor(shells=[1000], q_in=6)
# If using non-HCP data, the TrainingProcessorNorm should be used instead.
processor = TrainingProcessorNorm(shells=[1000], q_in=6)
# Important: Here our q_in = 6, and the processor uses a default q_out = 10, therefore our dmri data must
# contain at least 16 volumes.
# Load dataset mapping
train_data = processor.load_data(['/path/to/train_data0.tfrecord', '/path/to/train_data1.tfrecord'])
validation_data = processor.load_data(['/path/to/val_data0.tfrecord'], validation=True)
# Begin training
model.fit(train_data, epochs=10, validation_data=validation_data)
```
## Docker
You can also use `dMRI-RCNN` directly via [Docker](https://www.docker.com/). Both a CPU and GPU version of the project are available.
### CPU
To use `dMRI-RCNN` with the CPU only, use:
```bash
sudo docker run -v /absolute/path/to/my/data/directory:/data -it -t mlyon93/dmri-rcnn-cpu:latest
```
### GPU
To use `dMRI-RCNN` with the GPU, first ensure the [appropriate NVIDIA prerequisites](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) have been installed. Then use:
```bash
sudo docker run --gpus all -v /absolute/path/to/my/data/directory:/data -it -t mlyon93/dmri-rcnn-gpu:latest
```
## Spherical Harmonic Baseline
To run the Spherical Harmonic baseline model used in the paper, first ensure `dipy` is installed. You can install `dipy` directly via `pip` or by installing this project using the following prompt.
```bash
pip install dmri-rcnn[sh]
```
### Command-line
Bring up the following help message via `dmri_sh_baseline.py -h`:
```
usage: dMRI Spherical Harmonic Baseline Inference [-h] -dmri_in DMRI_IN -bvec_in BVEC_IN -bvec_out BVEC_OUT -dmri_out DMRI_OUT -s SHELL
optional arguments:
-h, --help show this help message and exit
-dmri_in DMRI_IN Context dMRI NIfTI volume. Must be single-shell and contain q_in 3D volumes
-bvec_in BVEC_IN Context b-vector text file. Whitespace delimited with 3 rows and q_in columns
-bvec_out BVEC_OUT Target b-vector text file. Whitespace delimited with 3 rows and q_out columns
-dmri_out DMRI_OUT Inferred dMRI NIfTI volume. This will contain q_out inferred volumes.
-s SHELL, --shell SHELL
Shell to perform inference on. Must be same shell as context/target dMRI and b-vecs
```
#### Example
The following example performs `b = 1000` spherical harmonic inference.
```bash
dmri_sh_baseline.py -dmri_in context_dmri.nii.gz -bvec_in context_bvecs -bvec_out target_bvecs -dmri_out inferred_dmri.nii.gz -s 1000
```
The use or inspect the spherical harmonic model, the code can be found within `dmri_rcnn.core.processing.sph_harmonic`.
## Roadmap
Future Additions & Improvements:
* Plot functionality
Raw data
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"description": "# Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional Autoencoder\n\n\n\n[](https://badge.fury.io/py/dmri-rcnn)\n\nThis project enhances the angular resolution of dMRI data through the use of a Recurrent CNN. This codebase is associated with the\nfollowing paper. Please cite the paper if you use this model:\n\n[Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional Autoencoder](https://arxiv.org/abs/2203.15598) [MIDL 2022]\n\n## Table of contents\n\n* [Installation](#installation)\n* [Inference](#inference)\n* [Training](#training)\n* [Docker](#docker)\n* [Spherical Harmonic Baseline](#spherical-harmonic-baseline)\n\n## Installation\n\n`dMRI-RCNN` can be installed by via pip:\n\n```bash\npip install dmri-rcnn\n```\n\n### Requirements\n\n`dMRI-RCNN` uses [TensorFlow](https://www.tensorflow.org/) as the deep learning architecture. To enable [GPU usage within TensorFlow](https://www.tensorflow.org/install/gpu), you should ensure the appropriate prerequisites are installed.\n\nListed below are the requirements for this package.\n\n* `tensorflow>=2.6.0`\n* `numpy`\n* `einops`\n* `nibabel`\n* `tqdm`\n\n## Inference\n\nOnce installed, use `run_dmri_rcnn.py` to perform inference of new dMRI volumes. Below lists the data requirements to use the script, and the command-line arguments available for inference.\n\n### Data\n\nTo run this script, dMRI data is required in the following format:\n\n* Context dMRI file. The dMRI data used as context within the model to infer other volumes\n * File format: `NIfTI`\n * Single-shell: containing only one b-value.\n * Dimensions: `(i, j, k, q_in)`.\n * `(i, j, k)` are the spatial dimensions of the data\n * `q_in` number of samples within the q-space dimension. This can either be `6`, `10`, or `30` and will affect which of the trained models is used.\n* Context b-vector file. The corresponding b-vectors for the context dMRI file.\n * File format: text file, whitespace delimited.\n * `3` rows corresponding to the `x, y, z` co-ordinates of q-space\n * `q_in` columns corresponding to the q-space directions sampled. `q_in` must either be `6`, `10`, or `30`.\n* Target b-vector file. The corresponding b-vectors for the inferred dMRI data.\n * File format: text file, whitespace delimited.\n * `3` rows corresponding to the `x, y, z` co-ordinates of q-space\n * `q_out` columns corresponding to the q-space directions sampled.\n* Brain mask file. Binary brain mask file for dMRI data.\n * File format: `NIfTI`\n * Dimensions: `(i, j, k)`. Same spatial dimensions as used in the dMRI data.\n\nThe script will create the following data:\n\n* Inferred dMRI file. dMRI volumes inferred from the model as defined by the target b-vectors.\n * File format: `NIfTI`\n * Dimensions: `(i, j, k, q_out)`.\n * `q_out` number of samples within the q-space dimension. This can any number, though using higher numbers will require more GPU memory if using.\n\n### Command-line\n\nBring up the following help message via `run_dmri_rcnn.py -h`:\n\n```\nusage: `run_dmri_rcnn.py` [-h] -dmri_in DMRI_IN -bvec_in BVEC_IN -bvec_out BVEC_OUT -mask MASK -dmri_out DMRI_OUT -s {1000,2000,3000} [-m {1,3}] [-c] [-b BATCH_SIZE]\n\noptional arguments:\n -h, --help show this help message and exit\n -dmri_in DMRI_IN Context dMRI NIfTI volume. Must be single-shell and contain q_in 3D volumes\n -bvec_in BVEC_IN Context b-vector text file. Whitespace delimited with 3 rows and q_in columns\n -bvec_out BVEC_OUT Target b-vector text file. Whitespace delimited with 3 rows and q_out columns\n -mask MASK Brain mask NIfTI volume. Must have space spatial dimensions as dmri_in.\n -dmri_out DMRI_OUT Inferred dMRI NIfTI volume. This will contain q_out inferred volumes.\n -s {1000,2000,3000}, --shell {1000,2000,3000}\n Shell to perform inference with. Must be same shell as context/target dMRI and b-vectors\n -m {1,3}, --model-dim {1,3}\n Model dimensionality, choose either 1 or 3. Default: 3.\n -c, --combined Use combined shell model. Currently only applicable with 3D model and 10 q_in.\n -n, --norm Perform normalisation using 99 percentile of data. Only implemented with --combined flag, and only for q_in = 10\n -b BATCH_SIZE, --batch-size BATCH_SIZE\n Batch size to run model inference with.\n```\n\n***N.B.** Weights are downloaded and stored within `~/.dmri_rcnn` by default. To store weights in a different directory, set environment variable `DMRI_RCNN_DIR=\"/your/custom/directory\"`*\n\n#### Example\n\nThe following example performs `b = 1000` inference with the 3D dMRI RCNN on **HCP data**.\n\n```bash\nrun_dmri_rcnn.py -dmri_in context_dmri.nii.gz -bvec_in context_bvecs -bvec_out target_bvecs -mask brain_mask.nii.gz -dmri_out inferred_dmri.nii.gz -s 1000 -m 3\n```\n\nThis example would take ~2 minutes to infer 80 volumes on an `NVIDIA RTX 3080`.\n\nTo perform inference on data outside of the HCP dataset, use the flags `-c` and `-n`. This is currently only implemented for $q_{in} = 10$.\n\n## Training\n\nBelow are details on how to train a given model, and the preprocessing steps involved.\n\n### Data Pre-Processing\n\nA training dataset is typically too large to fit into memory all at once. To overcome this, this project uses TensorFlow's `.tfrecord` file format and the\n[tf.data.Dataset](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) API. Therefore training data should be saved in this format before starting. Below is an example on how to do this using the `dMRI-RCNN` project.\n\n```python\nimport numpy as np\n\nfrom dmri_rcnn.core import io\nfrom dmri_rcnn.core.processing import save_tfrecord_data\n\n# First load a subject into memory\ndmri, _ = io.load_nifti('/path/to/dmri/data.nii.gz')\nmask, _ = io.load_nifti('/path/to/brain/mask.nii.gz', dtype=np.int8)\nbvecs = io.load_bvec('/path/to/dmri/bvecs') # bvecs & bvals should be in FSL format\nbvals = io.load_bval('/path/to/dmri/bvals')\n\n# Optionally crop image data\ndmri, mask = io.autocrop_dmri(dmri, mask)\n\n# .tfrecord format uses a maximum filesize of 2 GiB, therefore for high\n# resolution dMRI data, the image may need to be split into smaller parts\n# to do this use the function below. It is recommended to first try to save\n# each subject as a whole before splitting the image into separate files.\ndmri_list = io.split_image_to_octants(dmri)\nmask_list = io.split_image_to_octants(mask)\n\n# Now save data in .tfrecord format\nsave_tfrecord_data(dmri, bvecs, bvals, mask, '/path/to/saved/data.tfrecord')\n\n# Alternatively save the list of image parts if dmri is too large\nfor i in range(len(dmri_list)):\n save_tfrecord_data(dmri_list[i], bvecs, bvals, mask_list[i], '/path/to/saved/data' + str(i) + '.tfrecord')\n```\n\n### Training a Model\n\nOnce pre-processing is complete, you can then train a model.\n\n```python\nfrom dmri_rcnn.core.weights import get_weights\nfrom dmri_rcnn.core.model import get_1d_autoencoder, get_3d_autoencoder\nfrom dmri_rcnn.core.processing import TrainingProcessor, TrainingProcessorNorm\n\n# If we want to fine-tune the model we can load the previously obtained weights.\n# In this example we'll load the weights for the 3D RCNN trained on the b = 1000\n# shell and 6 q-space samples per input.\nweights = get_weights(model_dim=3, shell=1000, q_in=6)\n\n# Now we can instantiate the pre-compiled 3D model\nmodel = get_3d_autoencoder(weights) # Omit the weights argument to load without pre-trained weights\n\n# Instantiate the training processor\nprocessor = TrainingProcessor(shells=[1000], q_in=6)\n\n# If using non-HCP data, the TrainingProcessorNorm should be used instead.\nprocessor = TrainingProcessorNorm(shells=[1000], q_in=6)\n\n# Important: Here our q_in = 6, and the processor uses a default q_out = 10, therefore our dmri data must\n# contain at least 16 volumes.\n\n# Load dataset mapping\ntrain_data = processor.load_data(['/path/to/train_data0.tfrecord', '/path/to/train_data1.tfrecord'])\nvalidation_data = processor.load_data(['/path/to/val_data0.tfrecord'], validation=True)\n\n# Begin training\nmodel.fit(train_data, epochs=10, validation_data=validation_data)\n```\n\n## Docker\n\nYou can also use `dMRI-RCNN` directly via [Docker](https://www.docker.com/). Both a CPU and GPU version of the project are available.\n\n### CPU\n\nTo use `dMRI-RCNN` with the CPU only, use:\n\n```bash\nsudo docker run -v /absolute/path/to/my/data/directory:/data -it -t mlyon93/dmri-rcnn-cpu:latest\n```\n\n### GPU\n\nTo use `dMRI-RCNN` with the GPU, first ensure the [appropriate NVIDIA prerequisites](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) have been installed. Then use:\n\n```bash\nsudo docker run --gpus all -v /absolute/path/to/my/data/directory:/data -it -t mlyon93/dmri-rcnn-gpu:latest\n```\n\n## Spherical Harmonic Baseline\n\nTo run the Spherical Harmonic baseline model used in the paper, first ensure `dipy` is installed. You can install `dipy` directly via `pip` or by installing this project using the following prompt.\n\n```bash\npip install dmri-rcnn[sh]\n```\n\n### Command-line\n\nBring up the following help message via `dmri_sh_baseline.py -h`:\n\n```\nusage: dMRI Spherical Harmonic Baseline Inference [-h] -dmri_in DMRI_IN -bvec_in BVEC_IN -bvec_out BVEC_OUT -dmri_out DMRI_OUT -s SHELL\n\noptional arguments:\n -h, --help show this help message and exit\n -dmri_in DMRI_IN Context dMRI NIfTI volume. Must be single-shell and contain q_in 3D volumes\n -bvec_in BVEC_IN Context b-vector text file. Whitespace delimited with 3 rows and q_in columns\n -bvec_out BVEC_OUT Target b-vector text file. Whitespace delimited with 3 rows and q_out columns\n -dmri_out DMRI_OUT Inferred dMRI NIfTI volume. This will contain q_out inferred volumes.\n -s SHELL, --shell SHELL\n Shell to perform inference on. Must be same shell as context/target dMRI and b-vecs\n```\n\n#### Example\n\nThe following example performs `b = 1000` spherical harmonic inference.\n\n```bash\ndmri_sh_baseline.py -dmri_in context_dmri.nii.gz -bvec_in context_bvecs -bvec_out target_bvecs -dmri_out inferred_dmri.nii.gz -s 1000\n```\n\nThe use or inspect the spherical harmonic model, the code can be found within `dmri_rcnn.core.processing.sph_harmonic`.\n\n## Roadmap\n\nFuture Additions & Improvements:\n\n* Plot functionality\n",
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