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[TMI 2024] "High-Frequency Space Diffusion Model for Accelerated MRI"

Home Page: https://arxiv.org/pdf/2208.05481.pdf

Python 90.31% Shell 2.42% C++ 0.79% Cuda 6.48%

diffusion-model fastmri

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zhangjh705 breeze-zero sober235 karaogluhh heng309 gaochangwu

hfs-sde's Issues

About the dataset

Could you provide the preprocessed training and testing datasets of fastMRI used in your paper? The dataset is too large and difficult to preprocess. Your work is truly excellent.

About the data preprocess

Is your algorithm sensitive to data preprocessing methods? I did the min-max normalization and the results were not ideal.
I retrained the diffusion model in the algorithm on your weights file.

About generate_smaps.py

HFS-SDE/data_prepare/generate_smaps.py

Lines 45 to 50 in 197725c

    
           # Estimate coil sensitivity map 
        
           for i in range(kspace_data.shape[0]): 
        
               smaps_bart = bart(1, "ecalib -m 1 -S", kspace_data[:]) 
        
               smaps[i] = np.ascontiguousarray( 
        
                   smaps_bart[0].transpose(2, 0, 1).astype(np.complex64) 
        
               )

I'm a little unsure about the bart internal input requirements, but here it seems to be a sens map of slice 0 each time

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	# Estimate coil sensitivity map
	for i in range(kspace_data.shape[0]):
	smaps_bart = bart(1, "ecalib -m 1 -S", kspace_data[:])
	smaps[i] = np.ascontiguousarray(
	smaps_bart[0].transpose(2, 0, 1).astype(np.complex64)
	)