MAC-ReconNet-Multiple-Acquisition-Context-based-CNN

A single network that uses dynamic weight prediction for multiple acquisition context-based MRI Reconstruction

MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight Prediction (MIDL 2020)

Dependencies

Packages

• PyTorch
• TensorboardX
• numpy
• tqdm

An exhaustive list of packages used could be found in the requirements.txt file. Install the same using the following command:

 conda create --name <env> --file requirements.txt

Folder hierarchies

Folder hierarchy to set up datasets (input, label pairs)

Each Acquisition context has three settings - DATASET_TYPE, MASK_TYPE and acceleration factor represented in short ACC_FACTOR

DATASET_TYPE indicates the different types of anatomy images that we would like to combine in a single training MASK type indicates the kind of mask pattern which we use in the training. ACC_FACTOR - the acceleration factor for reconstruction

<base_path>/datasets/DATASET_TYPE/MASK_TYPE/ACC_FACTOR

Example: For example, 1. if we intend to combine T1 and FLAIR as two different data types of a subject then we could set this as DATASET_TYPE folders are 'mrbrain_t1' and'mrbrain_flair'. 2. if we use two mask types - Cartesian and Gaussian, then MASK_TYPE folders are cartesian, gaussian. 3. If we intend combining three acceleration factors, 4x, 5x and 8x, then ACC_FACTOR folders are acc_4x, acc_5x and acc_8x. For these settings the folder hierarchy is

										      datasets
											|
						------------------------------------------------------------------------
						|									|
					mrbrain_t1								mrbrain_flair
						|									|
				-------------------------						---------------------------------
				|			|					        |				|
			    cartesian	         gaussian					     cartesian			     gaussian
			      |			   |					                |				|
	train, validation		train, validation			               train, validation	        train, validation
	----------------		----------------			                ---------------			-----------------
            |	|	|		|	|	|					|	|	|	   	|	|	|
     acc_4x  acc_5x   acc_8x	     acc_4x  acc_5x   acc_8x		                  acc_4x     acc_5x   acc_8x	      acc_4x  acc_5x	acc_8x

The folder hierarchy for the under-sampling masks are stored in a folder seperately as follows. <base_path>/us_masks/DATASET_TYPE/MASK_TYPE/mask_<ACC_FACTOR>.npy

For example, to stored the 4x mask for Gaussian mask type for MRBrains FLAIR is stored as follows.

<base_path>/us_masks/mrbrain_t1/gaussian/mask_4x.npy

Note that if the mask is made on the fly for a given mask type and acceleration factor, then accordingly the changes needs to be done in dataset.py to generate random mask and based on that generate the under-sampled images. In that case, mask need not be stored.

Folder hierarchy for experiments folder

The hierarchy is similar to the one for datasets but in the experiments/<model_name>/results folder

<base_path>/experiments/<model_name>/results/DATASET_TYPE/MASK_TYPE/ACC_FACTOR

<model_name> - this is the folder with the model name in which the model files are stored. results - this is the folder in which all the predicted test files are stored in .h5 format for each acquisition context.

Train code

sh train_combinedall.sh

Test code

sh valid_combinedall.sh

Evaluate PSNR / SSIM metrics

sh evaluate_combinedall.sh

Display PSNR / SSIM metrics

sh report_collect_combinedall.sh

Citations

If you use the MAC-ReconNet in your research, please consider citing:

@InProceedings{MAC-ReconNet-MIDL2020,
  title = 	 {{MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight Prediction}},
  author =       {Ramanarayanan, Sriprabha and Murugesan, Balamurali and Ram, Keerthi and Sivaprakasam, Mohanasankar},
  booktitle = 	 {Medical Imaging with Deep Learning},
  pages = 	 {696--708},
  year = 	 {2020},
  volume = 	 {121},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {06--08 Jul},
  pdf = 	 {http://proceedings.mlr.press/v121/ramanarayanan20a/ramanarayanan20a.pdf},
  url = 	 {
http://proceedings.mlr.press/v121/ramanarayanan20a.html
}
}