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))
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.
"""
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
# Inverse Fourier Transform to get zero filled solution
image = transforms.ifft2(masked_kspace)
# Crop input image
image = transforms.complex_center_crop(
image, (self.resolution, self.resolution))
# Absolute value
image = transforms.complex_abs(image)
# Apply Root-Sum-of-Squares if multicoil data
if self.which_challenge == 'multicoil':
image = transforms.root_sum_of_squares(image)
# Normalize input
image, mean, std = transforms.normalize_instance(image, eps=1e-11)
if CLAMP:
image = image.clamp(-6, 6)
# Normalize target
target = transforms.to_tensor(target)
target_train = transforms.normalize(target, mean, std, eps=1e-11)
if CLAMP:
target_train = target_train.clamp(
-6,
6) # Return target (for viz) and target_clamped (for training)
return image, target_train, mean, std, attrs['norm'].astype(
np.float32), target
def __call__(self, kspace, target, attrs, fname, slice):
"""
Args:
kspace (numpy.Array): k-space measurements
target (numpy.Array): Target image
attrs (dict): Acquisition related information stored in the HDF5 object
fname (pathlib.Path): Path to the input file
slice (int): Serial number of the slice
Returns:
(tuple): tuple containing:
image (torch.Tensor): Normalized zero-filled input image
mean (float): Mean of the zero-filled image
std (float): Standard deviation of the zero-filled image
fname (pathlib.Path): Path to the input file
slice (int): Serial number of the slice
"""
kspace = transforms.to_tensor(kspace)
if self.mask_func is not None:
seed = tuple(map(ord, fname))
mask = transforms.get_mask(kspace, self.mask_func, seed)
masked_kspace = mask * kspace
else:
masked_kspace = kspace
# Inverse Fourier Transform to get zero filled solution
image = transforms.ifft2(masked_kspace)
# Crop input image
image = transforms.complex_center_crop(image, (self.resolution, self.resolution))
# Absolute value
image = transforms.complex_abs(image)
# Apply Root-Sum-of-Squares if multicoil data
if self.which_challenge == 'multicoil':
image = transforms.root_sum_of_squares(image)
# Normalize input
image, mean, std = transforms.normalize_instance(image)
image = image.clamp(-6, 6)
return image, mean, std, fname, slice
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
# Inverse Fourier Transform to get zero filled solution
image = transforms.ifft2(masked_kspace)
# Crop input image
image = transforms.complex_center_crop(
image, (self.resolution, self.resolution))
# Absolute value
image = transforms.complex_abs(image)
# Apply Root-Sum-of-Squares if multicoil data
if self.which_challenge == 'multicoil':
image = transforms.root_sum_of_squares(image)
# Normalize input
if self.normalize:
image, mean, std = transforms.normalize_instance(image, eps=1e-11)
if CLAMP:
image = image.clamp(-6, 6)
else:
mean = -1.0
std = -1.0
# Normalize target
if target is not None:
target = transforms.to_tensor(target)
target_train = target
if self.normalize:
target_train = transforms.normalize(target,
mean,
std,
eps=1e-11)
if CLAMP:
target_train = target_train.clamp(
-6, 6
) # Return target (for viz) and target_clamped (for training)
norm = attrs['norm'].astype(np.float32)
else:
target_train = []
target = []
norm = -1.0
image_updated = []
if os.path.exists(
'/home/manivasagam/code/fastMRIPrivate/models/unet_volumes/reconstructions_train/'
+ fname):
updated_fname = '/home/manivasagam/code/fastMRIPrivate/models/unet_volumes/reconstructions_train/' + fname
with h5py.File(updated_fname, 'r') as data:
image_updated = data['reconstruction'][slice]
image_updated = transforms.to_tensor(image_updated)
elif os.path.exists(
'/home/manivasagam/code/fastMRIPrivate/models/unet_volumes/reconstructions_val/'
+ fname):
updated_fname = '/home/manivasagam/code/fastMRIPrivate/models/unet_volumes/reconstructions_val/' + fname
with h5py.File(updated_fname, 'r') as data:
image_updated = data['reconstruction'][slice]
image_updated = transforms.to_tensor(image_updated)
elif os.path.exists(
'/home/manivasagam/code/fastMRIPrivate/models/unet_volumes/reconstructions_test/'
+ fname):
updated_fname = '/home/manivasagam/code/fastMRIPrivate/models/unet_volumes/reconstructions_test/' + fname
with h5py.File(updated_fname, 'r') as data:
image_updated = data['reconstruction'][slice]
image_updated = transforms.to_tensor(image_updated)
return image, target_train, mean, std, norm, target, image_updated
