toy data question

[Xiao-R-Y]

Thanks for your excellent work.
I have a question when transform the .mat data to the .png data and found the slices are not correspond.
There are 25 images in T1 and T2 each, however ,with the same index, the corresponding images are not the same location,


The transform code are as follows:
`
import torch.utils.data
import numpy as np, h5py
import random

load_dir = 'SynDiff-main/SynDiff_sample_data/data_val_T1.mat'
padding = True
Norm = True
variable = 'data_fs'

f = h5py.File(load_dir,'r')
if np.array(f[variable]).ndim==3:
data=np.expand_dims(np.transpose(np.array(f[variable]),(0,2,1)),axis=1)
else:
data=np.transpose(np.array(f[variable]),(1,0,3,2))
data=data.astype(np.float32)

if padding:
pad_x=int((256-data.shape[2])/2)
pad_y=int((256-data.shape[3])/2)
print('padding in x-y with:'+str(pad_x)+'-'+str(pad_y))
data=np.pad(data,((0,0),(0,0),(pad_x,pad_x),(pad_y,pad_y)))
if Norm:
data=(data-0.5)/0.5

from PIL import Image
import numpy as np

data = ((data + 1) * 0.5 * 255).astype(np.uint8)

print(data.shape)

for i, sample in enumerate(data):
# print(sample.shape,sample)
# image = Image.fromarray(sample.transpose(1, 2, 0))
image = Image.fromarray(sample[0])
image.save(f'show/vT1_image_{i}.png')

`
I would like to know after registration, should the data not correspond?
Thank you.

[muzafferozbey]

SynDiff was proposed for Unsupervised Medical Image Translation with unpaired source-target images. Hence, training data doesn't have to be matched/registered. However, you can still employ SynDiff with paired images.