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Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import numpy as np
import torch
from dataset.m4_utils.math import ifft2c, fft2c, complex_abs
from dataset.m4_utils.subsample import create_mask_for_mask_type, MaskFunc
import random
from typing import Dict, Optional, Sequence, Tuple, Union
from matplotlib import pyplot as plt
import os
def rss(data, dim=0):
"""
Compute the Root Sum of Squares (RSS).
RSS is computed assuming that dim is the coil dimension.
Args:
data (torch.Tensor): The input tensor
dim (int): The dimensions along which to apply the RSS transform
Returns:
torch.Tensor: The RSS value.
"""
return torch.sqrt((data ** 2).sum(dim))
def to_tensor(data):
"""
Convert numpy array to PyTorch tensor.
For complex arrays, the real and imaginary parts are stacked along the last
dimension.
Args:
data (np.array): Input numpy array.
Returns:
torch.Tensor: PyTorch version of data.
"""
if np.iscomplexobj(data):
data = np.stack((data.real, data.imag), axis=-1)
return torch.from_numpy(data)
def tensor_to_complex_np(data):
"""
Converts a complex torch tensor to numpy array.
Args:
data (torch.Tensor): Input data to be converted to numpy.
Returns:
np.array: Complex numpy version of data.
"""
data = data.numpy()
return data[..., 0] + 1j * data[..., 1]
def apply_mask(data, mask_func, seed=None, padding=None):
"""
Subsample given k-space by multiplying with a mask.
Args:
data (torch.Tensor): The input k-space data. This should have at least 3 dimensions, where
dimensions -3 and -2 are the spatial dimensions, and the final dimension has size
2 (for complex values).
mask_func (callable): A function that takes a shape (tuple of ints) and a random
number seed and returns a mask.
seed (int or 1-d array_like, optional): Seed for the random number generator.
Returns:
(tuple): tuple containing:
masked data (torch.Tensor): Subsampled k-space data
mask (torch.Tensor): The generated mask
"""
shape = np.array(data.shape)
shape[:-3] = 1
mask = mask_func(shape, seed)
if padding is not None:
mask[:, :, : padding[0]] = 0
mask[:, :, padding[1] :] = 0 # padding value inclusive on right of zeros
masked_data = data * mask + 0.0 # the + 0.0 removes the sign of the zeros
return masked_data, mask
def mask_center(x, mask_from, mask_to):
mask = torch.zeros_like(x)
mask[:, :, :, mask_from:mask_to] = x[:, :, :, mask_from:mask_to]
return mask
def center_crop(data, shape):
"""
Apply a center crop to the input real image or batch of real images.
Args:
data (torch.Tensor): The input tensor to be center cropped. It should
have at least 2 dimensions and the cropping is applied along the
last two dimensions.
shape (int, int): The output shape. The shape should be smaller than
the corresponding dimensions of data.
Returns:
torch.Tensor: The center cropped image.
"""
assert 0 < shape[0] <= data.shape[-2]
assert 0 < shape[1] <= data.shape[-1]
w_from = (data.shape[-2] - shape[0]) // 2
h_from = (data.shape[-1] - shape[1]) // 2
w_to = w_from + shape[0]
h_to = h_from + shape[1]
return data[..., w_from:w_to, h_from:h_to]
def complex_center_crop(data, shape):
"""
Apply a center crop to the input image or batch of complex images.
Args:
data (torch.Tensor): The complex input tensor to be center cropped. It
should have at least 3 dimensions and the cropping is applied along
dimensions -3 and -2 and the last dimensions should have a size of
2.
shape (int): The output shape. The shape should be smaller than
the corresponding dimensions of data.
Returns:
torch.Tensor: The center cropped image
"""
assert 0 < shape[0] <= data.shape[-3]
assert 0 < shape[1] <= data.shape[-2]
w_from = (data.shape[-3] - shape[0]) // 2 #80
h_from = (data.shape[-2] - shape[1]) // 2 #80
w_to = w_from + shape[0] #240
h_to = h_from + shape[1] #240
return data[..., w_from:w_to, h_from:h_to, :]
def center_crop_to_smallest(x, y):
"""
Apply a center crop on the larger image to the size of the smaller.
The minimum is taken over dim=-1 and dim=-2. If x is smaller than y at
dim=-1 and y is smaller than x at dim=-2, then the returned dimension will
be a mixture of the two.
Args:
x (torch.Tensor): The first image.
y (torch.Tensor): The second image
Returns:
tuple: tuple of tensors x and y, each cropped to the minimim size.
"""
smallest_width = min(x.shape[-1], y.shape[-1])
smallest_height = min(x.shape[-2], y.shape[-2])
x = center_crop(x, (smallest_height, smallest_width))
y = center_crop(y, (smallest_height, smallest_width))
return x, y
def normalize(data, mean, stddev, eps=0.0):
"""
Normalize the given tensor.
Applies the formula (data - mean) / (stddev + eps).
Args:
data (torch.Tensor): Input data to be normalized.
mean (float): Mean value.
stddev (float): Standard deviation.
eps (float, default=0.0): Added to stddev to prevent dividing by zero.
Returns:
torch.Tensor: Normalized tensor
"""
return (data - mean) / (stddev + eps)
def normalize_instance(data, eps=0.0):
"""
Normalize the given tensor with instance norm/
Applies the formula (data - mean) / (stddev + eps), where mean and stddev
are computed from the data itself.
Args:
data (torch.Tensor): Input data to be normalized
eps (float): Added to stddev to prevent dividing by zero
Returns:
torch.Tensor: Normalized tensor
"""
mean = data.mean()
std = data.std()
return normalize(data, mean, std, eps), mean, std
class DataTransform(object):
"""
Data Transformer for training U-Net models.
"""
def __init__(self, which_challenge):
"""
Args:
which_challenge (str): Either "singlecoil" or "multicoil" denoting
the dataset.
mask_func (fastmri.data.subsample.MaskFunc): A function that can
create a mask of appropriate shape.
use_seed (bool): If true, this class computes a pseudo random
number generator seed from the filename. This ensures that the
same mask is used for all the slices of a given volume every
time.
"""
if which_challenge not in ("singlecoil", "multicoil"):
raise ValueError(f'Challenge should either be "singlecoil" or "multicoil"')
self.which_challenge = which_challenge
def __call__(self, kspace, mask, target, attrs, fname, slice_num):
"""
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.
mask (numpy.array): Mask from the test dataset.
target (numpy.array): Target image.
attrs (dict): Acquisition related information stored in the HDF5
object.
fname (str): File name.
slice_num (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.
fname (str): File name.
slice_num (int): Serial number of the slice.
"""
kspace = to_tensor(kspace)
# inverse Fourier transform to get zero filled solution
image = ifft2c(kspace)
# crop input to correct size
if target is not None:
crop_size = (target.shape[-2], target.shape[-1])
else:
crop_size = (attrs["recon_size"][0], attrs["recon_size"][1])
# check for sFLAIR 203
if image.shape[-2] < crop_size[1]:
crop_size = (image.shape[-2], image.shape[-2])
image = complex_center_crop(image, crop_size)
# getLR
imgfft = fft2c(image)
imgfft = complex_center_crop(imgfft, (160, 160))
LR_image = ifft2c(imgfft)
# absolute value
LR_image = complex_abs(LR_image)
# normalize input
LR_image, mean, std = normalize_instance(LR_image, eps=1e-11)
LR_image = LR_image.clamp(-6, 6)
# normalize target
if target is not None:
target = to_tensor(target)
target = center_crop(target, crop_size)
target = normalize(target, mean, std, eps=1e-11)
target = target.clamp(-6, 6)
else:
target = torch.Tensor([0])
return LR_image, target, mean, std, fname, slice_num
class DenoiseDataTransform(object):
def __init__(self, size, noise_rate):
super(DenoiseDataTransform, self).__init__()
self.size = (size, size)
self.noise_rate = noise_rate
def __call__(self, kspace, mask, target, attrs, fname, slice_num):
max_value = attrs["max"]
#target
target = to_tensor(target)
target = center_crop(target, self.size)
target, mean, std = normalize_instance(target, eps=1e-11)
target = target.clamp(-6, 6)
#image
kspace = to_tensor(kspace)
complex_image = ifft2c(kspace) #complex_image
image = complex_center_crop(complex_image, self.size)
noise_image = self.rician_noise(image, max_value)
noise_image = complex_abs(noise_image)
noise_image = normalize(noise_image, mean, std, eps=1e-11)
noise_image = noise_image.clamp(-6, 6)
return noise_image, target, mean, std, fname, slice_num
def rician_noise(self, X, noise_std):
#Add rician noise with variance sampled uniformly from the range 0 and 0.1
noise_std = random.uniform(0, noise_std*self.noise_rate)
Ir = X + noise_std * torch.randn(X.shape)
Ii = noise_std*torch.randn(X.shape)
In = torch.sqrt(Ir ** 2 + Ii ** 2)
return In
def apply_mask(
data: torch.Tensor,
mask_func: MaskFunc,
seed: Optional[Union[int, Tuple[int, ...]]] = None,
padding: Optional[Sequence[int]] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Subsample given k-space by multiplying with a mask.
Args:
data: The input k-space data. This should have at least 3 dimensions,
where dimensions -3 and -2 are the spatial dimensions, and the
final dimension has size 2 (for complex values).
mask_func: A function that takes a shape (tuple of ints) and a random
number seed and returns a mask.
seed: Seed for the random number generator.
padding: Padding value to apply for mask.
Returns:
tuple containing:
masked data: Subsampled k-space data
mask: The generated mask
"""
shape = np.array(data.shape)
shape[:-3] = 1
mask = mask_func(shape, seed)
if padding is not None:
mask[:, :, : padding[0]] = 0
mask[:, :, padding[1] :] = 0 # padding value inclusive on right of zeros
masked_data = data * mask + 0.0 # the + 0.0 removes the sign of the zeros
return masked_data, mask
class ReconstructionTransform(object):
"""
Data Transformer for training U-Net models.
"""
def __init__(self, which_challenge, mask_func=None, use_seed=True):
"""
Args:
which_challenge (str): Either "singlecoil" or "multicoil" denoting
the dataset.
mask_func (fastmri.data.subsample.MaskFunc): A function that can
create a mask of appropriate shape.
use_seed (bool): If true, this class computes a pseudo random
number generator seed from the filename. This ensures that the
same mask is used for all the slices of a given volume every
time.
"""
if which_challenge not in ("singlecoil", "multicoil"):
raise ValueError(f'Challenge should either be "singlecoil" or "multicoil"')
self.mask_func = mask_func
self.which_challenge = which_challenge
self.use_seed = use_seed
def __call__(self, kspace, mask, target, attrs, fname, slice_num):
"""
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.
mask (numpy.array): Mask from the test dataset.
target (numpy.array): Target image.
attrs (dict): Acquisition related information stored in the HDF5
object.
fname (str): File name.
slice_num (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.
fname (str): File name.
slice_num (int): Serial number of the slice.
"""
kspace = to_tensor(kspace)
# apply mask
if self.mask_func:
seed = None if not self.use_seed else tuple(map(ord, fname))
masked_kspace, mask = apply_mask(kspace, self.mask_func, seed)
else:
masked_kspace = kspace
# inverse Fourier transform to get zero filled solution
image = ifft2c(masked_kspace)
# crop input to correct size
if target is not None:
crop_size = (target.shape[-2], target.shape[-1])
else:
crop_size = (attrs["recon_size"][0], attrs["recon_size"][1])
# check for sFLAIR 203
if image.shape[-2] < crop_size[1]:
crop_size = (image.shape[-2], image.shape[-2])
image = complex_center_crop(image, crop_size)
# print('image',image.shape)
# absolute value
image = complex_abs(image)
# apply Root-Sum-of-Squares if multicoil data
if self.which_challenge == "multicoil":
image = rss(image)
# normalize input
image, mean, std = normalize_instance(image, eps=1e-11)
image = image.clamp(-6, 6)
# normalize target
if target is not None:
target = to_tensor(target)
target = center_crop(target, crop_size)
target = normalize(target, mean, std, eps=1e-11)
target = target.clamp(-6, 6)
else:
target = torch.Tensor([0])
return image, target, mean, std, fname, slice_num
def build_transforms(MASKTYPE, CENTER_FRACTIONS, ACCELERATIONS, mode = 'train'):
challenge = 'singlecoil'
return ReconstructionTransform(challenge)
# if mode == 'train':
# mask = create_mask_for_mask_type(
# MASKTYPE, CENTER_FRACTIONS, ACCELERATIONS,
# )
# return ReconstructionTransform(challenge, mask, use_seed=False)
#
# elif mode == 'val' or mode == 'test':
# mask = create_mask_for_mask_type(
# MASKTYPE, CENTER_FRACTIONS, ACCELERATIONS,
# )
# return ReconstructionTransform(challenge, mask)
#
# else:
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