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dataloaders.py
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dataloaders.py
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from scipy.ndimage.interpolation import rotate,zoom
from torch.utils.data import Dataset, DataLoader
import glob
import os
import numpy as np
import torch
from tqdm import tqdm
import h5py
import sigpy as sp
from utils import get_mvue
import pickle as pkl
from xml.etree import ElementTree as ET
import sys
class MVU_Estimator_Brain(Dataset):
def __init__(self, file_list, maps_dir, input_dir,
project_dir='./',
R=1,
image_size=(384,384),
acs_size=26,
pattern='random',
orientation='vertical'):
# Attributes
self.project_dir = project_dir
self.file_list = file_list
self.maps_dir = maps_dir
self.input_dir = input_dir
self.image_size = image_size
self.R = R
self.pattern = pattern
self.orientation = orientation
# Access meta-data of each scan to get number of slices
self.num_slices = np.zeros((len(self.file_list,)), dtype=int)
for idx, file in enumerate(self.file_list):
input_file = os.path.join(self.input_dir, os.path.basename(file))
with h5py.File(os.path.join(self.project_dir, input_file), 'r') as data:
self.num_slices[idx] = int(np.array(data['kspace']).shape[0])
# Create cumulative index for mapping
self.slice_mapper = np.cumsum(self.num_slices) - 1 # Counts from '0'
def __len__(self):
return int(np.sum(self.num_slices)) # Total number of slices from all scans
# Phase encode random mask generator
def _get_mask(self, acs_lines=30, total_lines=384, R=1, pattern='random'):
# Overall sampling budget
num_sampled_lines = np.floor(total_lines / R)
# Get locations of ACS lines
# !!! Assumes k-space is even sized and centered, true for fastMRI
center_line_idx = np.arange((total_lines - acs_lines) // 2,
(total_lines + acs_lines) // 2)
# Find remaining candidates
outer_line_idx = np.setdiff1d(np.arange(total_lines), center_line_idx)
if pattern == 'random':
# Sample remaining lines from outside the ACS at random
random_line_idx = np.random.choice(outer_line_idx,
size=int(num_sampled_lines - acs_lines), replace=False)
elif pattern == 'equispaced':
# Sample equispaced lines
# !!! Only supports integer for now
random_line_idx = outer_line_idx[::int(R)]
else:
raise NotImplementedError('Mask pattern not implemented')
# Create a mask and place ones at the right locations
mask = np.zeros((total_lines))
mask[center_line_idx] = 1.
mask[random_line_idx] = 1.
return mask
# Cropping utility - works with numpy / tensors
def _crop(self, x, wout, hout):
w, h = x.shape[-2:]
x1 = int(np.ceil((w - wout) / 2.))
y1 = int(np.ceil((h - hout) / 2.))
return x[..., x1:x1+wout, y1:y1+hout]
def __getitem__(self, idx):
# Convert to numerical
if torch.is_tensor(idx):
idx = idx.tolist()
# Get scan and slice index
# First scan for which index is in the valid cumulative range
scan_idx = int(np.where((self.slice_mapper - idx) >= 0)[0][0])
# Offset from cumulative range
slice_idx = int(idx) if scan_idx == 0 else \
int(idx - self.slice_mapper[scan_idx] + self.num_slices[scan_idx] - 1)
# Load maps for specific scan and slice
maps_file = os.path.join(self.maps_dir,
os.path.basename(self.file_list[scan_idx]))
with h5py.File(os.path.join(self.project_dir, maps_file), 'r') as data:
# Get maps
maps = np.asarray(data['s_maps'][slice_idx])
# Load raw data for specific scan and slice
raw_file = os.path.join(self.input_dir,
os.path.basename(self.file_list[scan_idx]))
with h5py.File(os.path.join(self.project_dir, raw_file), 'r') as data:
# Get maps
gt_ksp = np.asarray(data['kspace'][slice_idx])
# Crop extra lines and reduce FoV in phase-encode
gt_ksp = sp.resize(gt_ksp, (
gt_ksp.shape[0], gt_ksp.shape[1], self.image_size[1]))
# Reduce FoV by half in the readout direction
gt_ksp = sp.ifft(gt_ksp, axes=(-2,))
gt_ksp = sp.resize(gt_ksp, (gt_ksp.shape[0], self.image_size[0],
gt_ksp.shape[2]))
gt_ksp = sp.fft(gt_ksp, axes=(-2,)) # Back to k-space
# Crop extra lines and reduce FoV in phase-encode
maps = sp.fft(maps, axes=(-2, -1)) # These are now maps in k-space
maps = sp.resize(maps, (
maps.shape[0], maps.shape[1], self.image_size[1]))
# Reduce FoV by half in the readout direction
maps = sp.ifft(maps, axes=(-2,))
maps = sp.resize(maps, (maps.shape[0], self.image_size[0],
maps.shape[2]))
maps = sp.fft(maps, axes=(-2,)) # Back to k-space
maps = sp.ifft(maps, axes=(-2, -1)) # Finally convert back to image domain
# find mvue image
mvue = get_mvue(gt_ksp.reshape((1,) + gt_ksp.shape), maps.reshape((1,) + maps.shape))
# !!! Removed ACS-based scaling if handled on the outside
scale_factor = 1.
# Scale data
mvue = mvue / scale_factor
gt_ksp = gt_ksp / scale_factor
# Compute ACS size based on R factor and sample size
total_lines = gt_ksp.shape[-1]
if 1 < self.R <= 6:
# Keep 8% of center samples
acs_lines = np.floor(0.08 * total_lines).astype(int)
else:
# Keep 4% of center samples
acs_lines = np.floor(0.04 * total_lines).astype(int)
# Get a mask
mask = self._get_mask(acs_lines, total_lines,
self.R, self.pattern)
# Mask k-space
if self.orientation == 'vertical':
gt_ksp *= mask[None, None, :]
elif self.orientation == 'horizontal':
gt_ksp *= mask[None, :, None]
else:
raise NotImplementedError
## name for mvue file
mvue_file = os.path.join(self.input_dir,
os.path.basename(self.file_list[scan_idx]))
# Output
sample = {
'mvue': mvue,
'maps': maps,
'ground_truth': gt_ksp,
'mask': mask,
'scale_factor': scale_factor,
# Just for feedback
'scan_idx': scan_idx,
'slice_idx': slice_idx,
'mvue_file': mvue_file}
return sample
class MVU_Estimator_Knees(Dataset):
def __init__(self, file_list, maps_dir, input_dir,
project_dir='./',
R=1,
image_size=(320, 320),
acs_size=26,
pattern='random',
orientation='vertical'):
# Attributes
self.project_dir = project_dir
self.file_list = file_list
self.acs_size = acs_size
self.maps_dir = maps_dir
self.input_dir = input_dir
self.R = R
self.image_size = image_size
self.pattern = pattern
self.orientation = orientation
# Access meta-data of each scan to get number of slices
self.num_slices = np.zeros((len(self.file_list,)), dtype=int)
for idx, file in enumerate(self.file_list):
raw_file = os.path.join(self.input_dir, os.path.basename(file))
with h5py.File(os.path.join(self.project_dir, raw_file), 'r') as data:
value = data['ismrmrd_header'][()]
value = ET.fromstring(value)
self.num_slices[idx] = int(value[4][2][3][1].text) + 1
# Create cumulative index for mapping
self.slice_mapper = np.cumsum(self.num_slices) - 1 # Counts from '0'
def __len__(self):
return int(np.sum(self.num_slices)) # Total number of slices from all scans
# Phase encode random mask generator
def _get_mask(self, acs_lines=30, total_lines=384, R=1, pattern='random'):
# Overall sampling budget
num_sampled_lines = np.floor(total_lines / R)
# Get locations of ACS lines
# !!! Assumes k-space is even sized and centered, true for fastMRI
center_line_idx = np.arange((total_lines - acs_lines) // 2,
(total_lines + acs_lines) // 2)
# Find remaining candidates
outer_line_idx = np.setdiff1d(np.arange(total_lines), center_line_idx)
if pattern == 'random':
# Sample remaining lines from outside the ACS at random
random_line_idx = np.random.choice(outer_line_idx,
size=int(num_sampled_lines - acs_lines), replace=False)
elif pattern == 'equispaced':
# Sample equispaced lines
# !!! Only supports integer for now
random_line_idx = outer_line_idx[::int(R)]
else:
raise NotImplementedError('Mask Pattern not implemented yet...')
# Create a mask and place ones at the right locations
mask = np.zeros((total_lines))
mask[center_line_idx] = 1.
mask[random_line_idx] = 1.
return mask
def _knees_remove_zeros(self, kimage):
# Compute sum-energy of lines
# !!! This is because some lines are near-empty
line_energy = np.sum(np.square(np.abs(kimage)),
axis=(0, 1))
dead_lines = np.where(line_energy < 1e-12)[0] # Sufficient for FP32
# Always remove an even number of lines
dead_lines_front = np.sum(dead_lines < 160)
dead_lines_back = np.sum(dead_lines > 160)
if np.mod(dead_lines_front, 2):
dead_lines = np.delete(dead_lines, 0)
if np.mod(dead_lines_back, 2):
dead_lines = np.delete(dead_lines, -1)
# Remove dead lines completely
k_image = np.delete(kimage, dead_lines, axis=-1)
return k_image
# Cropping utility - works with numpy / tensors
def _crop(self, x, wout, hout):
w, h = x.shape[-2:]
x1 = int(np.ceil((w - wout) / 2.))
y1 = int(np.ceil((h - hout) / 2.))
return x[..., x1:x1+wout, y1:y1+hout]
def __getitem__(self, idx):
# Convert to numerical
if torch.is_tensor(idx):
idx = idx.tolist()
# Get scan and slice index
# First scan for which index is in the valid cumulative range
scan_idx = int(np.where((self.slice_mapper - idx) >= 0)[0][0])
# Offset from cumulative range
slice_idx = int(idx) if scan_idx == 0 else \
int(idx - self.slice_mapper[scan_idx] + self.num_slices[scan_idx] - 1)
# Load maps for specific scan and slice
maps_file = os.path.join(self.maps_dir,
os.path.basename(self.file_list[scan_idx]))
with h5py.File(os.path.join(self.project_dir, maps_file), 'r') as data:
# Get maps
maps = np.asarray(data['s_maps'][slice_idx])
# Load raw data for specific scan and slice
raw_file = os.path.join(self.input_dir,
os.path.basename(self.file_list[scan_idx]))
with h5py.File(os.path.join(self.project_dir, raw_file), 'r') as data:
# Get maps
gt_ksp = np.asarray(data['kspace'][slice_idx])
gt_ksp = self._knees_remove_zeros(gt_ksp)
# Crop extra lines and reduce FoV by half in readout
gt_ksp = sp.resize(gt_ksp, (
gt_ksp.shape[0], gt_ksp.shape[1], self.image_size[1]))
# Reduce FoV by half in the readout direction
gt_ksp = sp.ifft(gt_ksp, axes=(-2,))
gt_ksp = sp.resize(gt_ksp, (gt_ksp.shape[0], self.image_size[0],
gt_ksp.shape[2]))
gt_ksp = sp.fft(gt_ksp, axes=(-2,)) # Back to k-space
# Crop extra lines and reduce FoV by half in readout
maps = sp.fft(maps, axes=(-2, -1)) # These are now maps in k-space
maps = sp.resize(maps, (
maps.shape[0], maps.shape[1], self.image_size[1]))
# Reduce FoV by half in the readout direction
maps = sp.ifft(maps, axes=(-2,))
maps = sp.resize(maps, (maps.shape[0], self.image_size[0],
maps.shape[2]))
maps = sp.fft(maps, axes=(-2,)) # Back to k-space
maps = sp.ifft(maps, axes=(-2, -1)) # Finally convert back to image domain
# find mvue image
mvue = get_mvue(gt_ksp.reshape((1,) + gt_ksp.shape), maps.reshape((1,) + maps.shape))
# # Load MVUE slice from specific scan
mvue_file = os.path.join(self.input_dir,
os.path.basename(self.file_list[scan_idx]))
# !!! Removed ACS-based scaling if handled on the outside
scale_factor = 1.
# Scale data
mvue = mvue / scale_factor
gt_ksp = gt_ksp / scale_factor
# Compute ACS size based on R factor and sample size
total_lines = gt_ksp.shape[-1]
if 1 < self.R <= 6:
# Keep 8% of center samples
acs_lines = np.floor(0.08 * total_lines).astype(int)
else:
# Keep 4% of center samples
acs_lines = np.floor(0.04 * total_lines).astype(int)
# Get a mask
mask = self._get_mask(acs_lines, total_lines,
self.R, self.pattern)
# Mask k-space
if self.orientation == 'vertical':
gt_ksp *= mask[None, None, :]
elif self.orientation == 'horizontal':
gt_ksp *= mask[None, :, None]
else:
raise NotImplementedError
# Output
sample = {
'mvue': mvue,
'maps': maps,
'ground_truth': gt_ksp,
'mask': mask,
'scale_factor': scale_factor,
# Just for feedback
'scan_idx': scan_idx,
'slice_idx': slice_idx,
'mvue_file': mvue_file}
return sample
class MVU_Estimator_Stanford_Knees(Dataset):
def __init__(self, file_list, maps_dir, input_dir,
project_dir='./',
R=1,
image_size=(320,320),
acs_size=26,
pattern='random',
orientation='vertical'):
# Attributes
self.project_dir = project_dir
self.acs_size = acs_size
self.maps_dir = maps_dir
self.input_dir = input_dir
self.R = R
self.image_size = image_size
self.pattern = pattern
self.orientation = orientation
self.file_list = sorted(file_list)
if len(self.file_list) == 0:
raise IOError('No image files found in the specified path')
# Access meta-data of each scan to get number of slices
# self.maps_file = os.path.join(maps_dir, 'Stanford-Knee-Axial-Selected.h5')
# self.raw_file = os.path.join(input_dir, 'Stanford-Knee-Axial-Selected.h5')
# with h5py.File(os.path.join(self.project_dir, self.raw_file), 'r') as data:
# self.num_slices = np.array(data['kspace']).shape[0]
@property
def num_slices(self):
num_slices = np.zeros((len(self.file_list,)), dtype=int)
for idx, file in enumerate(self.file_list):
with h5py.File(os.path.join(self.project_dir, file), 'r') as data:
num_slices[idx] = np.array(data['kspace']).shape[0]
return num_slices
@property
def slice_mapper(self):
return np.cumsum(self.num_slices) - 1 # Counts from '0'
def __len__(self):
return int(np.sum(self.num_slices)) # Total number of slices from all scans
def __getitem__(self, idx):
# Convert to numerical
if torch.is_tensor(idx):
idx = idx.tolist()
# Get scan and slice index
# First scan for which index is in the valid cumulative range
scan_idx = int(np.where((self.slice_mapper - idx) >= 0)[0][0])
# Offset from cumulative range
slice_idx = int(idx) if scan_idx == 0 else \
int(idx - self.slice_mapper[scan_idx] + self.num_slices[scan_idx] - 1)
# Load specific slice from specific scan
with h5py.File(os.path.join(self.project_dir, self.file_list[scan_idx]), 'r') as data:
# Get maps, kspace, masks
gt_ksp = np.asarray(data['kspace'])[slice_idx]
maps = np.asarray(data['s_maps'])[slice_idx]
mask = np.asarray(data['masks'])[slice_idx]
# find mvue image
mvue = get_mvue(gt_ksp.reshape((1,) + gt_ksp.shape), maps.reshape((1,) + maps.shape))
# # Load MVUE slice from specific scan
mvue_file = os.path.join(self.input_dir,
os.path.basename(self.file_list[scan_idx]))
# !!! Removed ACS-based scaling if handled on the outside
scale_factor = 1.
# Scale data
mvue = mvue / scale_factor
gt_ksp = gt_ksp / scale_factor
# apply mask
gt_ksp *= mask[None, :, :]
# Output
sample = {
'mvue': mvue,
'maps': maps,
'ground_truth': gt_ksp,
'mask': mask,
'scale_factor': scale_factor,
# Just for feedback
'scan_idx': scan_idx,
'slice_idx': slice_idx,
'mvue_file': mvue_file}
return sample
# class MVU_Estimator_Stanford_Knees(Dataset):
# def __init__(self, maps_dir, input_dir,
# project_dir='./',
# R=1,
# image_size=(320,320),
# acs_size=26,
# pattern='random',
# orientation='vertical'):
# # Attributes
# self.project_dir = project_dir
# self.acs_size = acs_size
# self.maps_dir = maps_dir
# self.input_dir = input_dir
# self.R = R
# self.image_size = image_size
# self.pattern = pattern
# self.orientation = orientation
# # Access meta-data of each scan to get number of slices
# self.num_slices = np.ones(18, dtype=int)
# self.maps_file = os.path.join(maps_dir, 'Stanford_maps_rotated.h5')
# self.raw_file = os.path.join(input_dir, 'Stanford_knees.pkl')
# def __len__(self):
# return int(np.sum(self.num_slices)) # Total number of slices from all scans
# # Phase encode random mask generator
# def _get_mask(self, acs_lines=30, total_lines=384, R=1, pattern='random'):
# # Overall sampling budget
# num_sampled_lines = np.floor(total_lines / R)
# # Get locations of ACS lines
# # !!! Assumes k-space is even sized and centered, true for fastMRI
# center_line_idx = np.arange((total_lines - acs_lines) // 2,
# (total_lines + acs_lines) // 2)
# # Find remaining candidates
# outer_line_idx = np.setdiff1d(np.arange(total_lines), center_line_idx)
# if pattern == 'random':
# # Sample remaining lines from outside the ACS at random
# random_line_idx = np.random.choice(outer_line_idx,
# size=int(num_sampled_lines - acs_lines), replace=False)
# elif pattern == 'equispaced':
# # Sample equispaced lines
# # !!! Only supports integer for now
# random_line_idx = outer_line_idx[::int(R)]
# else:
# raise NotImplementedError('Mask Pattern not implemented yet...')
# # Create a mask and place ones at the right locations
# mask = np.zeros((total_lines))
# mask[center_line_idx] = 1.
# mask[random_line_idx] = 1.
# return mask
# # Cropping utility - works with numpy / tensors
# def _crop(self, x, wout, hout):
# w, h = x.shape[-2:]
# x1 = int(np.ceil((w - wout) / 2.))
# y1 = int(np.ceil((h - hout) / 2.))
# return x[..., x1:x1+wout, y1:y1+hout]
# def _rotatecomplex(self, a,angle,reshape=True):
# r = rotate(a.real,angle,reshape=reshape,mode='wrap')
# i = rotate(a.imag,angle,reshape=reshape,mode='wrap')
# return r+1j*i
# def __getitem__(self, idx):
# # Convert to numerical
# if torch.is_tensor(idx):
# idx = idx.tolist()
# # Load maps for specific scan and slice
# with h5py.File(os.path.join(self.project_dir, self.maps_file), 'r') as data:
# # Get maps
# maps = np.asarray(data[f'ge{idx+1}.h5'])
# # Load raw data for specific scan and slice
# with open(os.path.join(self.project_dir, self.raw_file), 'rb') as f:
# # Get maps
# data = pkl.load(f)
# slice_ksp = np.asarray(data[f'ge{idx+1}.h5']['kspace'])
# # rotate kspace by 90 degrees
# gt_ksp = slice_ksp.copy()
# for c,coil in enumerate(slice_ksp):
# gt_ksp[c,:,:] = self._rotatecomplex(coil,90) # the kspace is rotated, so werotate it back to the original format
# # find mvue image
# mvue = get_mvue(gt_ksp.reshape((1,) + gt_ksp.shape), maps.reshape((1,) + maps.shape))
# # # Load MVUE slice from specific scan
# mvue_file = os.path.join(self.input_dir,f'ge{idx+1}.h5')
# # !!! Removed ACS-based scaling if handled on the outside
# scale_factor = 1.
# # Scale data
# mvue = mvue / scale_factor
# gt_ksp = gt_ksp / scale_factor
# # Compute ACS size based on R factor and sample size
# total_lines = gt_ksp.shape[-1]
# if 1 < self.R <= 6:
# # Keep 8% of center samples
# acs_lines = np.floor(0.08 * total_lines).astype(int)
# else:
# # Keep 4% of center samples
# acs_lines = np.floor(0.04 * total_lines).astype(int)
# # Get a mask
# mask = self._get_mask(acs_lines, total_lines,
# self.R, self.pattern)
# # Mask k-space
# if self.orientation == 'vertical':
# gt_ksp *= mask[None, None, :]
# elif self.orientation == 'horizontal':
# gt_ksp *= mask[None, :, None]
# else:
# raise NotImplementedError
# # Output
# sample = {
# 'mvue': mvue,
# 'maps': maps,
# 'ground_truth': gt_ksp,
# 'mask': mask,
# 'scale_factor': scale_factor,
# # Just for feedback
# 'scan_idx': 1,
# 'slice_idx': idx+1,
# 'mvue_file': mvue_file}
# return sample
class MVU_Estimator_Abdomen(Dataset):
def __init__(self, maps_dir, input_dir,
project_dir='./',
R=1,
image_size=(158,320),
acs_size=26,
pattern='random',
rotate=True,
orientation='vertical'):
# Attributes
self.project_dir = project_dir
self.acs_size = acs_size
self.maps_dir = maps_dir
self.input_dir = input_dir
self.R = R
self.image_size = image_size
self.pattern = pattern
self.orientation = orientation
self.rotate = rotate
# Access meta-data of each scan to get number of slices
self.maps_file = os.path.join(self.project_dir, maps_dir, 'data2.h5')
self.raw_file = os.path.join(self.project_dir, input_dir, 'data2.h5')
with h5py.File(self.raw_file, 'r') as f:
self.num_slices = np.array(f['ksp']).shape[0]
def __len__(self):
return self.num_slices # Total number of slices from all scans
# Phase encode random mask generator
def _get_mask(self, acs_lines=30, total_lines=384, R=1, pattern='random'):
# Overall sampling budget
num_sampled_lines = np.floor(total_lines / R)
# Get locations of ACS lines
# !!! Assumes k-space is even sized and centered, true for fastMRI
center_line_idx = np.arange((total_lines - acs_lines) // 2,
(total_lines + acs_lines) // 2)
# Find remaining candidates
outer_line_idx = np.setdiff1d(np.arange(total_lines), center_line_idx)
if pattern == 'random':
# Sample remaining lines from outside the ACS at random
random_line_idx = np.random.choice(outer_line_idx,
size=int(num_sampled_lines - acs_lines), replace=False)
elif pattern == 'equispaced':
# Sample equispaced lines
# !!! Only supports integer for now
random_line_idx = outer_line_idx[::int(R)]
else:
raise NotImplementedError('Mask Pattern not implemented yet...')
# Create a mask and place ones at the right locations
mask = np.zeros((total_lines))
mask[center_line_idx] = 1.
mask[random_line_idx] = 1.
return mask
# Cropping utility - works with numpy / tensors
def _crop(self, x, wout, hout):
w, h = x.shape[-2:]
x1 = int(np.ceil((w - wout) / 2.))
y1 = int(np.ceil((h - hout) / 2.))
return x[..., x1:x1+wout, y1:y1+hout]
def _rotatecomplex(self, a,angle,reshape=True):
r = rotate(a.real,angle,reshape=reshape,mode='wrap')
i = rotate(a.imag,angle,reshape=reshape,mode='wrap')
return r+1j*i
def __getitem__(self, idx):
# Convert to numerical
if torch.is_tensor(idx):
idx = idx.tolist()
# Load maps for specific scan and slice
with h5py.File(os.path.join(self.project_dir, self.maps_file), 'r') as data:
# Get maps
maps = np.asarray(data['maps'])[idx]
# Load raw data for specific scan and slice
with h5py.File(os.path.join(self.project_dir, self.raw_file), 'r') as data:
# Get maps
slice_ksp = np.asarray(data['ksp'])[idx]
# # rotate kspace by 90 degrees
if self.rotate:
gt_ksp = slice_ksp.copy()
for c,coil in enumerate(slice_ksp):
gt_ksp[c,:,:] = self._rotatecomplex(coil,90) # the kspace is rotated, so werotate it back to the original format
else:
gt_ksp = slice_ksp.copy()
# pad readout in image domain
x = sp.ifft(gt_ksp, axes=(-1,))
x = sp.resize(x, (x.shape[0], x.shape[1], self.image_size[1]))
# pad phase-encode in kspace domain
gt_ksp = sp.fft(x, axes=(-1,))
gt_ksp = sp.resize(gt_ksp, (gt_ksp.shape[0], self.image_size[0], self.image_size[1]))
# Crop extra lines and reduce FoV by half in readout
maps = sp.fft(maps, axes=(-1, -2)) # These are now maps in k-space
maps = sp.ifft(maps, axes=(-1,))
maps = sp.resize(maps, (
maps.shape[0], maps.shape[1], self.image_size[1]))
maps = sp.fft(maps, axes=(-1,))
# pad phase-encode in kspace domain
maps = sp.resize(maps, (maps.shape[0], self.image_size[0],
self.image_size[1]))
maps = sp.ifft(maps, axes=(-1, -2)) # Finally convert back to image domain
# find mvue image
mvue = get_mvue(gt_ksp.reshape((1,) + gt_ksp.shape), maps.reshape((1,) + maps.shape))
# # Load MVUE slice from specific scan
mvue_file = os.path.join(self.input_dir,str(idx))
# !!! Removed ACS-based scaling if handled on the outside
scale_factor = 1.
# Scale data
mvue = mvue / scale_factor
gt_ksp = gt_ksp / scale_factor
# Compute ACS size based on R factor and sample size
if self.orientation == 'horizontal':
total_lines = gt_ksp.shape[-2]
elif self.orientation == 'vertical':
total_lines = gt_ksp.shape[-1]
else:
raise NotImplementedError
if 1 < self.R <= 6:
# Keep 8% of center samples
acs_lines = np.floor(0.08 * total_lines).astype(int)
else:
# Keep 4% of center samples
acs_lines = np.floor(0.04 * total_lines).astype(int)
# Get a mask
mask = self._get_mask(acs_lines, total_lines,
self.R, self.pattern)
# Mask k-space
if self.orientation == 'vertical':
gt_ksp *= mask[None, None, :]
elif self.orientation == 'horizontal':
gt_ksp *= mask[None, :, None]
else:
raise NotImplementedError
# Output
sample = {
'mvue': mvue,
'maps': maps,
'ground_truth': gt_ksp,
'mask': mask,
'scale_factor': scale_factor,
# Just for feedback
'scan_idx': 1,
'slice_idx': idx+1,
'mvue_file': mvue_file}
return sample