How can I cut exactly the first harmonic (H1) from a FFT of an image I have 4D( 2D + slices along z axis + time frames) gray-scale image for the heart beating on different moments.
I do like to take Fourier Transform along time axis(for each slice separately), and analyze the fundamental Harmonic (also called H1 component, where H stands for Hilbert Space) so I can determine pixel regions corresponding to ROI which show strongest response to cardiac frequency.
I'm using python for this purpose, and I tried to do that with the following code, but I'm not sure that this is the correct way to do it, because I don't know how to determine the cut-frequency to keep only the fundamental Harmonic. 


This link to the image which I'm dealing with

import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt

img = nib.load('patient057_4d.nii.gz')
f = np.fft.fft2(img)
#  Move the DC component of the FFT output to the center of the spectrum
fshift = np.fft.fftshift(f)
fshift_orig = fshift.copy()
# logarithmic transformation
 magnitude_spectrum = 20*np.log(np.abs(fshift))
# Create mask
rows, cols = img.shape
crow, ccol = int(rows/2), int(cols/2)
# Use mask to remove low frequency components
dist1 = 20
dist2 = 10
fshift[crow-dist1:crow+dist1, ccol-dist1:ccol+dist1] = 0
#fshift[crow-dist2:crow+dist2, ccol-dist2:ccol+dist2] = fshift_orig[crow-dist2:crow+dist2, ccol-dist2:ccol+dist2] 

# logarithmic transformation
magnitude_spectrum1 = 20*np.log(np.abs(fshift)) 
f_ishift = np.fft.ifftshift(fshift)
# inverse Fourier transform
img_back = np.fft.ifft2(f_ishift)
# get rid of imaginary part by abs
img_back = np.abs(img_back)
plt.figure(num = 'Im_Back')
plt.imshow(abs(fshift[:,:,2,2]).astype('uint8'),cmap='gray')
plt.show()

```
The solution was to take Fourier transform 3D for each slice, then to chose only the 2nd component of the Transform to transform it back to the spatial space, and that's it.
The benefit of this is to detect if something is moving along the third axis(time in my case). 


for sl in range(img.shape[2]):
   #-----Fourier--H1-----------------------------------------
   # ff1[:, :, 1] H1 compnent 1, if 0 then DC
   ff1 = FFT.fftn(img[:,:,sl,:])
   fh = np.absolute(FFT.ifftn(ff1[:, :, 1])) 

   #-----Fourier--H1-----------------------------------------
```