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Ensure same osf for both target and source data samples #8

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Ensure same osf for both target and source data samples #8

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17 changes: 14 additions & 3 deletions src/ggrappa/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,7 +59,7 @@ def get_src_tgs_blocks(blocks, idxs_src, idxs_tgs, check_type='acs'):
return select_blocks[..., idxs_src], select_blocks[..., idxs_tgs]

def get_grappa_filled_data_and_loc(sig, rec, params):
rec[:, np.abs(sig).sum(axis=0)!=0] = 0
#rec[:, np.abs(sig).sum(axis=0)!=0] = 0
sampled_mask = np.abs(rec).sum(axis=0) != 0
extra_data = rec[:, sampled_mask]
rec_loc = np.nonzero(sampled_mask)
Expand Down Expand Up @@ -99,7 +99,8 @@ def get_cart_portion_sparkling(kspace_shots, traj_params, kspace_data, calc_osf_
locs = np.ones((grads.shape[0], 2))*-1
sampled_loc = [[],] * grads.shape[0]
cart_loc = [[],] * grads.shape[0]

new_kspace_data = []
new_kspace_loc = []
gridded_data = np.zeros((kspace_data.shape[0], *traj_params['img_size']), dtype=np.complex64)
for start, end in zip(starts, ends):
row, start_col = start
Expand All @@ -116,6 +117,8 @@ def get_cart_portion_sparkling(kspace_shots, traj_params, kspace_data, calc_osf_
(s_loc[1]-s_loc[0])*
np.diff(kspace_shots[row, s_loc[0]:s_loc[0]+2, 0])*traj_params['img_size'][0]
) == 0:
new_kspace_data.append(re_kspace_data[:, row])
new_kspace_loc.append(kspace_shots[row, ...])
continue
data = sp.signal.resample(
re_kspace_data[:, row, s_loc[0]: s_loc[1]],
Expand All @@ -125,11 +128,19 @@ def get_cart_portion_sparkling(kspace_shots, traj_params, kspace_data, calc_osf_
),
axis=-1,
)
new_kspace_data.append(np.hstack([
re_kspace_data[:, row, :s_loc[0]],
re_kspace_data[:, row, s_loc[1]:]
]))
new_kspace_loc.append(np.concatenate([
kspace_shots[row, :s_loc[0]],
kspace_shots[row, s_loc[1]:],
], axis=0))
locs += 0.5
locs *= np.asarray(traj_params["img_size"]).T
rounded_locs = locs.round(0).astype('int')
gridded_data[:, rounded_locs[0][0]:rounded_locs[0][0]+len(data[0]), rounded_locs[0][1], rounded_locs[0][2]] = data
return gridded_data
return gridded_data, np.hstack(new_kspace_data), np.concatenate(new_kspace_loc, axis=0)

def pad_back_to_size(sig, vol_shape, start_loc, end_loc):
"""Pads a given signal tensor back to a specified volume shape.
Expand Down