CT Data Generation for NN TrainingΒΆ
This example demonstrates how to generate synthetic CT data for training neural network models. If desired, a basic reconstruction can be generated using filtered back projection (FBP).
[1]:
import numpy as np
from scico import plot
from scico.flax.examples import load_ct_data
plot.config_notebook_plotting()
Read data from cache or generate if not available.
[2]:
N = 256 # phantom size
train_nimg = 536 # number of training images
test_nimg = 64 # number of testing images
nimg = train_nimg + test_nimg
n_projection = 45 # CT views
trdt, ttdt = load_ct_data(train_nimg, test_nimg, N, n_projection, verbose=True)
Platform : gpu
Device count : 8
Data generation time[s]: 123.95
Sinogram time[s]: 7.71
FBP time[s]: 2.67
Storing data in path : ~/.cache/scico/examples/data
Set --training-- : Size: 536
Set --testing -- : Size: 64
Data range --images -- : Min: 0.00, Max: 1.00
Data range --sinogram-- : Min: 0.00, Max: 0.67
Data range --FBP -- : Min: 0.00, Max: 1.00
Plot randomly selected sample.
[3]:
indx_tr = np.random.randint(0, train_nimg)
indx_te = np.random.randint(0, test_nimg)
fig, axes = plot.subplots(nrows=2, ncols=3, figsize=(9, 9))
plot.imview(
trdt["img"][indx_tr, ..., 0], title="Ground truth - Training Sample", fig=fig, ax=axes[0, 0]
)
plot.imview(
trdt["sino"][indx_tr, ..., 0], title="Sinogram - Training Sample", fig=fig, ax=axes[0, 1]
)
plot.imview(
trdt["fbp"][indx_tr, ..., 0],
title="FBP - Training Sample",
fig=fig,
ax=axes[0, 2],
)
plot.imview(
ttdt["img"][indx_te, ..., 0],
title="Ground truth - Testing Sample",
fig=fig,
ax=axes[1, 0],
)
plot.imview(
ttdt["sino"][indx_te, ..., 0], title="Sinogram - Testing Sample", fig=fig, ax=axes[1, 1]
)
plot.imview(
ttdt["fbp"][indx_te, ..., 0],
title="FBP - Testing Sample",
fig=fig,
ax=axes[1, 2],
)
fig.suptitle(r"Training and Testing samples")
fig.tight_layout()
fig.colorbar(
axes[0, 2].get_images()[0],
ax=axes,
shrink=0.5,
pad=0.05,
label="Arbitrary Units",
)
fig.show()