def __call__(self, kspace, target, attrs, fname, slice):
"""
Args:
kspace (numpy.array): Input k-space of shape (num_coils, rows, cols, 2) for multi-coil
data or (rows, cols, 2) for single coil data.
target (numpy.array): Target image
attrs (dict): Acquisition related information stored in the HDF5 object.
fname (str): File name
slice (int): Serial number of the slice.
Returns:
(tuple): tuple containing:
image (torch.Tensor): Zero-filled input image.
target (torch.Tensor): Target image converted to a torch Tensor.
mean (float): Mean value used for normalization.
std (float): Standard deviation value used for normalization.
norm (float): L2 norm of the entire volume.
"""
kspace = transforms.to_tensor(kspace)
# Apply mask
seed = None if not self.use_seed else tuple(map(ord, fname))
if self.use_mask:
mask = transforms.get_mask(kspace, self.mask_func, seed)
masked_kspace = mask * kspace
else:
masked_kspace = kspace
image = transforms.ifft2(masked_kspace)
_, mean_image, std_image = transforms.normalize_instance(image, eps=1e-11)
masked_kspace, mean, std = transforms.normalize_instance_complex(masked_kspace)
kspace = transforms.normalize(kspace, mean, std)
return masked_kspace, kspace, mean, std, mean_image, std_image, mask
def __call__(self, kspace, target, attrs, fname, slice):
"""
Args:
kspace (numpy.array): Input k-space of shape (num_coils, rows, cols, 2) for multi-coil
data or (rows, cols, 2) for single coil data.
target (numpy.array): Target image
attrs (dict): Acquisition related information stored in the HDF5 object.
fname (str): File name
slice (int): Serial number of the slice.
Returns:
(tuple): tuple containing:
image (torch.Tensor): Zero-filled input image.
target (torch.Tensor): Target image converted to a torch Tensor.
mean (float): Mean value used for normalization.
std (float): Standard deviation value used for normalization.
norm (float): L2 norm of the entire volume.
"""
target_inference = transforms.to_tensor(target)
kspace = transforms.to_tensor(kspace)
target = transforms.ifft2(kspace)
# Apply mask
seed = None if not self.use_seed else tuple(map(ord, fname))
if self.use_mask:
mask = transforms.get_mask(kspace, self.mask_func, seed)
masked_kspace = mask * kspace
else:
masked_kspace = kspace
image = transforms.ifft2(masked_kspace)
image_crop = transforms.complex_center_crop(
image, (self.resolution, self.resolution))
_, mean, std = transforms.normalize_instance_complex(image_crop,
eps=1e-11)
image_abs = transforms.complex_abs(image_crop)
image_abs, mean_abs, std_abs = transforms.normalize_instance(image_abs,
eps=1e-11)
image = transforms.normalize(image, mean, std)
target_image_complex_norm = transforms.normalize(target, mean, std)
target_kspace_train = transforms.fft2(target_image_complex_norm)
target = transforms.complex_center_crop(target, (320, 320))
target = transforms.complex_abs(target)
target_train = target
if RENORM:
target_train = transforms.normalize(target_train, mean_abs,
std_abs)
if CLAMP:
image = image.clamp(-6, 6)
target_train = target_train.clamp(-6, 6)
return image, target_train, target_kspace_train, mean, std, mask, mean_abs, std_abs, target_inference, attrs[
'max'], attrs['norm'].astype(np.float32)
def __call__(self, kspace, target, attrs, fname, slice):
"""
Args:
kspace (numpy.array): Input k-space of shape (num_coils, rows, cols, 2) for multi-coil
data or (rows, cols, 2) for single coil data.
target (numpy.array): Target image
attrs (dict): Acquisition related information stored in the HDF5 object.
fname (str): File name
slice (int): Serial number of the slice.
Returns:
(tuple): tuple containing:
image (torch.Tensor): Zero-filled input image.
target (torch.Tensor): Target image converted to a torch Tensor.
mean (float): Mean value used for normalization.
std (float): Standard deviation value used for normalization.
norm (float): L2 norm of the entire volume.
"""
kspace = transforms.to_tensor(kspace)
# Apply mask
seed = None if not self.use_seed else tuple(map(ord, fname))
mask = transforms.get_mask(kspace, self.mask_func, seed)
masked_kspace = mask * kspace
# Inverse Fourier Transform to get zero filled solution
image_precrop = transforms.ifft2(masked_kspace)
masked_kspace, _, _ = transforms.normalize_instance_complex(
masked_kspace, eps=1e-11)
kspace, _, _ = transforms.normalize_instance_complex(kspace, eps=1e-11)
# Crop input image
image = transforms.complex_center_crop(
image_precrop, (self.resolution, self.resolution))
image_abs = transforms.complex_abs(image)
_, mean_abs, std_abs = transforms.normalize_instance(image_abs,
eps=1e-11)
# Normalize input
image, mean, std = transforms.normalize_instance_complex(image,
eps=1e-11)
image = image.clamp(-6, 6)
target = transforms.to_tensor(target)
target_train = target
if not TRAIN_COMPLEX:
# Normalize target
if RENORM:
target_train = transforms.normalize(target,
mean_abs,
std_abs,
eps=1e-11)
target_train = target_train.clamp(
-6, 6
) # Return target (for viz) and target_clamped (for training)
else:
target_train = transforms.ifft2(kspace)
target_train = transforms.complex_center_crop(
target_train, (self.resolution, self.resolution))
if RENORM:
target_train = transforms.normalize(target_train,
mean,
std,
eps=1e-11)
return image, target_train, mean, std, attrs['norm'].astype(
np.float32
), target, mean_abs, std_abs, kspace, masked_kspace, image_precrop