PP-MPI

Official repository for Plug-n-Play MPI Reconstruction (PP-MPI)

B. Askin, A. Güngör, D. A. Soydan, E. U. Saritas, C. B. Top and T. Çukur, "PP-MPI: A Deep Plug-and-Play Prior for Magnetic Particle Imaging Reconstruction," in MLMIR, 2022, pp. 105–114.

Demo

You can use the following links to download training, validation, test datasets.

Dataset

Download the folders datasets and OpenMPI in the same folder as the code:

https://drive.google.com/drive/folders/1tTRM-vhqONWCkZsW2Gxd5MZi8L-U13N6?usp=sharing

Pretrained Networks:

Pre-trained network can be found under training/denoiser/ folder.

Training

Generic training code code:

python train_ppmpi.py --useGPUno 0

wd: weight decay, default is 0
lr: learning rate
saveModelEpoch: save model every X epoch
valEpoch: Compute validation per X epoch
fixedNsStdFlag: 0: randomly generate noise std for each image, 1: fix noise std.
minNoiseStd: For non-fixed noise, minimum noise std.
maxNoiseStdList: For non-fixed noise: maximum noise std., For fixed noise: noise std. Multiple inputs are separated by comma, each input trains a different network consecutively.
batch_size_train: batch size
epoch_nb: number of epochs
wandbFlag: use Wandb for loss tracking (0 / 1)
wandbName: Experiment name for wandb
reScaleBetween: "x,y" rescale images in the dataset between x and y
dims: 2 / 3, number of dimensions of the image ("3" for using a 3D dataset, default 2)

nb_of_featuresList: Number of features of RDN, separate with comma for training of multiple different networks
nb_of_blocks: Number of blocks of RDN
layer_in_each_block: Layer in each block of RDN
growth_rate: growth rate of RDN

Inference for Open MPI dataset

python openMPItest.py

Settings should be changed from within the file. A jupyter notebook might be more helpful since it also helps better visualize and manipulate reconstructed images.

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Citation

You are encouraged to modify/distribute this code. However, please acknowledge this code and cite the paper appropriately.

@InProceedings{ppmpi,
author="Askin, Baris
and G{\"u}ng{\"o}r, Alper
and Alptekin Soydan, Damla
and Saritas, Emine Ulku
and Top, Can Bar{\i}{\c{s}}
and Cukur, Tolga",
editor="Haq, Nandinee
and Johnson, Patricia
and Maier, Andreas
and Qin, Chen
and W{\"u}rfl, Tobias
and Yoo, Jaejun",
title="PP-MPI: A Deep Plug-and-Play Prior for Magnetic Particle Imaging Reconstruction",
booktitle="Machine Learning for Medical Image Reconstruction",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="105--114",
isbn="978-3-031-17247-2"
}

@misc{deqmpi,
      title={DEQ-MPI: A Deep Equilibrium Reconstruction with Learned Consistency for Magnetic Particle Imaging}, 
      author={Alper Güngör and Baris Askin and Damla Alptekin Soydan and Can Barış Top and Emine Ulku Saritas and Tolga Çukur},
      year={2022},
      eprint={2212.13233},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

(c) ICON Lab 2023

Prerequisites

• Python 3.6
• CuDNN 8.2.1
• PyTorch 1.10.0

Acknowledgements

For questions/comments please send an email to: alperg@ee.bilkent.edu.tr