---
language:
- en
license: apache-2.0
library_name: atommic
datasets:
- AHEAD
thumbnail: null
tags:
- quantitative-mri-mapping
- qVarNet
- ATOMMIC
- pytorch
model-index:
- name: QMRI_qVarNet_AHEAD_gaussian2d_12x
  results: []

---


## Model Overview

quantitative Variational Network (qVarNet) for 12x accelerated quantitative MRI mapping of R2*, S0, B0, phi maps on the AHEAD dataset.


## ATOMMIC: Training

To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version.
```
pip install atommic['all']
```

## How to Use this Model

The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/qMRI/AHEAD/conf).


### Automatically instantiate the model

```base
pretrained: true
checkpoint: https://huggingface.co/wdika/QMRI_qVarNet_AHEAD_gaussian2d_12x/blob/main/QMRI_qVarNet_AHEAD_gaussian2d_12x.atommic
mode: test
```

### Usage

You need to download the AHEAD dataset to effectively use this model. Check the [AHEAD](https://github.com/wdika/atommic/blob/main/projects/qMRI/AHEAD/README.md) page for more information.


## Model Architecture
```base
model:
  model_name: qVN
  use_reconstruction_module: false
  quantitative_module_num_cascades: 8
  quantitative_module_channels: 18
  quantitative_module_pooling_layers: 4
  quantitative_module_in_channels: 8
  quantitative_module_out_channels: 8
  quantitative_module_padding_size: 11
  quantitative_module_normalize: true
  quantitative_module_no_dc: false
  quantitative_module_signal_forward_model_sequence: MEGRE
  quantitative_module_dimensionality: 2
  quantitative_maps_scaling_factor: 1e-3
  quantitative_maps_regularization_factors:
    - 150.0
    - 150.0
    - 1000.0
    - 150.0
  quantitative_loss:
    ssim: 1.0
  kspace_quantitative_loss: false
  total_quantitative_loss_weight: 1.0  # balance between reconstruction and quantitative loss
  quantitative_parameters_regularization_factors:
    - R2star: 1.0
    - S0: 1.0
    - B0: 1.0
    - phi: 1.0
```

## Training
```base
  optim:
    name: adam
    lr: 1e-4
    betas:
      - 0.9
      - 0.98
    weight_decay: 0.0
    sched:
      name: InverseSquareRootAnnealing
      min_lr: 0.0
      last_epoch: -1
      warmup_ratio: 0.1

trainer:
  strategy: ddp_find_unused_parameters_false
  accelerator: gpu
  devices: 1
  num_nodes: 1
  max_epochs: 20
  precision: 16-mixed
  enable_checkpointing: false
  logger: false
  log_every_n_steps: 50
  check_val_every_n_epoch: -1
  max_steps: -1
```

## Performance

To compute the targets using the raw k-space and the chosen coil combination method, accompanied with the chosen coil sensitivity maps estimation method, you can use [targets](https://github.com/wdika/atommic/tree/main/projects/qMRI/AHEAD/conf/targets) configuration files.

Evaluation can be performed using the [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/qmapping.py) script for the qmri task, with --evaluation_type per_slice.

Results
-------

Evaluation against R2*, S0, B0, phi targets
-------------------------------------------
12x: MSE = 0.005571 +/- 0.02725 NMSE = 0.192 +/- 0.3344 PSNR = 24.36 +/- 7.791 SSIM = 0.7838 +/- 0.2059


## Limitations

This model was trained on very few subjects on the AHEAD dataset. It is not guaranteed to generalize to other datasets.


## References

[1] [ATOMMIC](https://github.com/wdika/atommic)

[2] Alkemade A, Mulder MJ, Groot JM, et al. The Amsterdam Ultra-high field adult lifespan database (AHEAD): A freely available multimodal 7 Tesla submillimeter magnetic resonance imaging database. NeuroImage 2020;221.