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run_refine_vcc_spirit.py
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run_refine_vcc_spirit.py
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import os, sys, h5py, torch, fastmri, bart, time
sys.path.append("train")
import numpy as np
import cupy as cp
import sigpy as sp
from train.modl import MoDLNet
from src.refine import RefineKspace
from src.evaluate import *
from fastmri.data import subsample, transforms
from src.evaluate import metrics, gmsd
import glob
def VCC(x):
# x: kspace with (nC,nX,nY)
vcc_ksp = x.copy()
vcc_ksp = np.conj(vcc_ksp[:,::-1,::-1])
if vcc_ksp.shape[1] % 2 == 0:
vcc_ksp = np.roll(vcc_ksp,1,1)
if vcc_ksp.shape[2] % 2 == 0:
vcc_ksp = np.roll(vcc_ksp,1,2)
return vcc_ksp
def espirit_calibrate(x, calsize=17):
# x: kspace with (nC,nX,nY)
# out: sens with (nC,nX,nY)
tmp = np.moveaxis(x,0,-1)[:,:,None]
senss = bart.bart(1,'ecalib -r {} -m 1 -P'.format(calsize),tmp)
s = np.moveaxis(senss,-1,0).squeeze()
return s
def DL_recon(ksp, mask, sens, net, option='ksp'):
ksp = transforms.to_tensor(ksp)
mask = transforms.to_tensor(mask[...,None]).byte()
sens = transforms.to_tensor(sens)
ksp = ksp.unsqueeze(0).cuda()
mask = mask.unsqueeze(0).cuda()
sens = sens.unsqueeze(0).cuda()
with torch.no_grad():
ksp_dl = net(ksp,mask,sens,option=option)
# im_dl = net(ksp,mask, sens)
# im_dl = transforms.tensor_to_complex_np(im_dl.cpu().detach()).squeeze().copy()
# return im_dl
ksp_dl = transforms.tensor_to_complex_np(ksp_dl.cpu().detach()).squeeze().copy()
return ksp_dl
def runRefine(net, x_full, x_us, sens, calsize=21, lam=2, option='img'):
# x_full: fully sampled kspace for ref
# x_us: undersampled kspace with (nC,nX,nY)
# First get sensitivity maps
if np.mean(np.abs(sens)) < 0.01:
sens = espirit_calibrate(x_us,calsize)
else:
sens = sens
im_full = sp.ifft(x_full, axes=(1,2))
im_full = np.sum(im_full * np.conj(sens),0)
mask = np.abs(x_us[0,100])>0
mask = mask[None,None]
if option == 'ksp':
ksp_coil = DL_recon(x_us,mask,sens,net,option=option)
else:
im_dl = DL_recon(x_us,mask,sens,net,option=option)
im_coil = im_dl[None] * sens
ksp_coil = sp.fft(im_coil, axes=(1,2))
t0 = time.time()
refine_op = RefineKspace(x_us.copy(),
ksp_coil.copy(),
method='spirit',
autoespirit=True,
nacs=calsize,
kSize=[5,5],
rank=200,
lam=lam,
vcc=True,
domain='conv',
verbose=True, device=sp.Device(0))
ksp_refine = refine_op.run(niter=500, tol=1e-4)
im_refine = np.sum(sp.ifft(ksp_refine, axes=(1,2)) * np.conj(sens), axis=0)
print('Elapsed time:',time.time() - t0)
if option == 'img' or option == 'cs':
return im_full, im_dl, im_refine
else:
return x_full, ksp_coil, ksp_refine
def runvccspirit(subject_number, slice_number,lam=5, R=4, nacs=21, im_type='Brain', option='img'):
# Loading model
net = MoDLNet(num_cascades=10)
if im_type == 'Brain':
if R < 5:
model_dir = 'train/models/Brain_T1Post_R4_MoDL/Brain_T1Post_R_4.pt'
else:
model_dir = 'train/models/Brain_T1Post_R6_MoDL/Brain_T1Post_R_6.pt'
net.load_state_dict(torch.load(model_dir, map_location="cpu")['model_state_dict'])
elif im_type == 'Knee':
if R < 5:
model_dir = 'train/models/fastMRI_mini_PD_R4_MoDL/fastMRI_mini_PD_R_4.pt'
else:
model_dir = 'train/models/fastMRI_mini_PD_R6_MoDL/fastMRI_mini_PD_R_6.pt'
net.load_state_dict(torch.load(model_dir, map_location="cpu")['model_state_dict'])
else:
if R < 5:
model_dir = 'train/models/fastMRI_mini_PD_FS_R4_MoDL/fastMRI_mini_PD_FS_R_4.pt'
else:
model_dir = 'train/models/fastMRI_mini_PD_FS_R6_MoDL/fastMRI_mini_PD_FS_R_6.pt'
net.load_state_dict(torch.load(model_dir, map_location="cpu")['model_state_dict'])
net = net.cuda()
# loading subject
if im_type == 'Brain':
basedir = '/mnt/dense/kanghyun/Brain_T1Post/Test/'
elif im_type == 'Knee':
basedir = '/mnt/dense/kanghyun/fastMRI_mini_PD/Test/'
else:
basedir = '/mnt/dense/kanghyun/fastMRI_mini_PD_FS/Test'
h5file = glob.glob(os.path.join(basedir, '*.h5'))[subject_number]
with h5py.File(h5file,'r') as hr:
X = hr['kspace'][slice_number] # saving the array to numpy (RAM)
sens = hr['esp_maps'][slice_number]
scale_factor = 10 / dict(hr.attrs)['max']
X *= scale_factor
# loading mask func
cf = nacs / X.shape[-1]
mask_func = subsample.MagicMaskFractionFunc(
center_fractions=[cf],
accelerations=[R])
seednum = 400 # R=4
mask, _ = mask_func(list(X.shape) + [1], seed=seednum) # trick because undersampling is in axis -1
mask = mask[...,0]
mask = mask.numpy()
X_us = X * mask
im_full, im_dl, im_refine = runRefine(net, X, X_us, sens, lam=lam, option=option)
if option == 'cs':
xsize = 320
ksp_us_bart = np.moveaxis(X_us,0,-1)[:,:,None]
sens_bart = np.moveaxis(sens,0,-1)[:,:,None]
L1_ESPIRIT = bart.bart(1,'pics -R W:3:0:0.01',ksp_us_bart, sens_bart)
im_cs = sp.resize(L1_ESPIRIT[::-1], (xsize,xsize))
im_d = sp.resize(im_dl[::-1], (xsize,xsize))
im_r = sp.resize(im_refine[::-1], (xsize,xsize))
im_f = sp.resize(im_full[::-1], (xsize,xsize))
return im_cs, im_d, im_r, im_f
if option == 'img':
xsize = 320
im_d = sp.resize(im_dl[::-1], (xsize,xsize))
im_r = sp.resize(im_refine[::-1], (xsize,xsize))
im_f = sp.resize(im_full[::-1], (xsize,xsize))
ssim_dl, psnr_dl, nrmse_dl = metrics(np.abs(im_d), np.abs(im_f))
ssim_refine, psnr_refine, nrmse_refine = metrics(np.abs(im_r), np.abs(im_f))
print('SSIM DL:{:.3f}, SSIM_Refine:{:.3f}, PSNR_DL:{:.2f}, PSNR_Refine:{:.2f}'.format(ssim_dl, ssim_refine, psnr_dl, psnr_refine))
return im_d, im_r, im_f
else:
return im_dl, im_refine, im_full