def test_nocalib():
"""
Check we get an error if there is no calibration data
"""
d = np.random.rand(5, 5, 5, 6)
perf_img = Image(name="perfusion", image=d)
wsp = Workspace(calib_method="voxelwise")
with pytest.raises(ValueError):
calib.calibrate(wsp, perf_img)
def test_bad_method():
"""
Check we get an error on an invalid method
"""
d = np.random.rand(5, 5, 5, 6)
perf_img = Image(name="perfusion", image=d)
d = np.random.rand(5, 5, 5, 6)
calib_img = Image(name="calib", image=d)
wsp = Workspace(calib=calib_img, calib_method="random")
with pytest.raises(ValueError):
calib.calibrate(wsp, perf_img)
def test_refregion_gain():
"""
Modify calibration gain
"""
GAIN = 3.4
perf_img, calib_img = _get_imgs()
ref_d = np.ones((5, 5, 5))
ref_img = Image(name="refmask", image=ref_d)
wsp = Workspace(calib=calib_img,
calib_method="refregion",
refmask=ref_img,
calib_aslreg=True,
tissref="wm",
calib_gain=GAIN)
perf_calib = calib.calibrate(wsp, perf_img)
calibrated_d = perf_calib.data
m0_expected = _expected_m0(np.mean(calib_img.data),
1.0,
50,
0.82,
gain=GAIN)
np.testing.assert_allclose(calibrated_d, perf_img.data / m0_expected)
def test_refregion_unknown():
"""
Reference region unknown type
"""
perf_img, calib_img = _get_imgs()
ref_d = np.ones((5, 5, 5))
ref_img = Image(name="refmask", image=ref_d)
wsp = Workspace(calib=calib_img,
calib_method="refregion",
refmask=ref_img,
calib_aslreg=True,
tissref="kryptonite")
with pytest.raises(ValueError):
calib.calibrate(wsp, perf_img)
def test_defaults():
"""
Check default calibration works
"""
perf_d = np.random.rand(5, 5, 5)
perf_img = Image(name="perfusion", image=perf_d)
calib_d = np.random.rand(5, 5, 5)
calib_img = Image(name="calib", image=calib_d)
wsp = Workspace(calib=calib_img, calib_method="voxelwise")
perf_calib = calib.calibrate(wsp, perf_img)
calibrated_d = perf_calib.data
assert (perf_calib.name == "perfusion_calib")
assert (perf_calib.shape == perf_img.shape)
# Default partition coefficient is 0.9
np.testing.assert_allclose(calibrated_d, 0.9 * perf_d / calib_d)
def test_partition_coeff():
"""
Check tissue/blood partition coefficient
"""
PC = 0.67
perf_d = np.random.rand(5, 5, 5)
perf_img = Image(name="perfusion", image=perf_d)
calib_d = np.random.rand(5, 5, 5)
calib_img = Image(name="calib", image=calib_d)
wsp = Workspace(calib=calib_img, calib_method="voxelwise", pct=PC)
perf_calib = calib.calibrate(wsp, perf_img)
calibrated_d = perf_calib.data
assert (perf_calib.name == "perfusion_calib")
assert (perf_calib.shape == perf_img.shape)
# Default partition coefficient is 0.9
np.testing.assert_allclose(calibrated_d, PC * perf_d / calib_d)
def test_alpha():
"""
Check inversion efficiency
"""
ALPHA = 0.74
perf_d = np.random.rand(5, 5, 5)
perf_img = Image(name="perfusion", image=perf_d)
calib_d = np.random.rand(5, 5, 5)
calib_img = Image(name="calib", image=calib_d)
wsp = Workspace(calib=calib_img, calib_method="voxelwise")
perf_calib = calib.calibrate(wsp, perf_img, alpha=ALPHA)
calibrated_d = perf_calib.data
assert (perf_calib.name == "perfusion_calib")
assert (perf_calib.shape == perf_img.shape)
# Default partition coefficient is 0.9
np.testing.assert_allclose(calibrated_d, 0.9 / ALPHA * perf_d / calib_d)
def test_refregion_gm_all():
"""
Reference region mask is whole image
"""
perf_img, calib_img = _get_imgs()
ref_d = np.ones((5, 5, 5))
ref_img = Image(name="refmask", image=ref_d)
wsp = Workspace(calib=calib_img,
calib_method="refregion",
refmask=ref_img,
calib_aslreg=True,
tissref="gm")
perf_calib = calib.calibrate(wsp, perf_img)
calibrated_d = perf_calib.data
# GM defaults
m0_expected = _expected_m0(np.mean(calib_img.data), 1.3, 100, 0.98)
np.testing.assert_allclose(calibrated_d, perf_img.data / m0_expected)
def test_refregion_csf_t2star():
"""
Using T2* instead of T2 for CSF
"""
perf_img, calib_img = _get_imgs()
ref_d = np.ones((5, 5, 5))
ref_img = Image(name="refmask", image=ref_d)
wsp = Workspace(calib=calib_img,
calib_method="refregion",
refmask=ref_img,
calib_aslreg=True,
tissref="csf",
t2star=True)
perf_calib = calib.calibrate(wsp, perf_img)
calibrated_d = perf_calib.data
m0_expected = _expected_m0(np.mean(calib_img.data), 4.3, 400, 1.15)
np.testing.assert_allclose(calibrated_d, perf_img.data / m0_expected)
def test_shorttr_corr():
"""
Check correction for short TR
"""
TR, T1 = 3.0, 1.1
perf_d = np.random.rand(5, 5, 5)
perf_img = Image(name="perfusion", image=perf_d)
calib_d = np.random.rand(5, 5, 5)
calib_img = Image(name="calib", image=calib_d)
wsp = Workspace(calib=calib_img, calib_method="voxelwise", tr=TR, t1=T1)
perf_calib = calib.calibrate(wsp, perf_img)
calibrated_d = perf_calib.data
assert (perf_calib.name == "perfusion_calib")
assert (perf_calib.shape == perf_img.shape)
factor = 1 - math.exp(-TR / T1)
# Default partition coefficient is 0.9
np.testing.assert_allclose(calibrated_d, 0.9 * factor * perf_d / calib_d)
def test_cgain_var():
"""
Check calibration gain on variances
"""
GAIN = 1.23
perf_d = np.random.rand(5, 5, 5)
perf_img = Image(name="perfusion", image=perf_d)
calib_d = np.random.rand(5, 5, 5)
calib_img = Image(name="calib", image=calib_d)
wsp = Workspace(calib=calib_img, calib_method="voxelwise", calib_gain=GAIN)
perf_calib = calib.calibrate(wsp, perf_img, var=True)
calibrated_d = perf_calib.data
assert (perf_calib.name == "perfusion_calib")
assert (perf_calib.shape == perf_img.shape)
# Default partition coefficient is 0.9
np.testing.assert_allclose(
calibrated_d, 0.9 * 0.9 / GAIN / GAIN * perf_d / calib_d / calib_d)
def test_multiplier():
"""
Check end multiplier for physical units
"""
MULTIPLIER = 7654
perf_d = np.random.rand(5, 5, 5)
perf_img = Image(name="perfusion", image=perf_d)
calib_d = np.random.rand(5, 5, 5)
calib_img = Image(name="calib", image=calib_d)
wsp = Workspace(calib=calib_img, calib_method="voxelwise")
perf_calib = calib.calibrate(wsp, perf_img, multiplier=MULTIPLIER)
calibrated_d = perf_calib.data
assert (perf_calib.name == "perfusion_calib")
assert (perf_calib.shape == perf_img.shape)
# Default partition coefficient is 0.9
np.testing.assert_allclose(calibrated_d,
0.9 * MULTIPLIER * perf_d / calib_d)
def run(self, options):
""" Run the process """
from oxasl import Workspace, calib
from fsl.data.image import Image
data = self.get_data(options)
img = Image(data.raw(), name=data.name)
roi = self.get_roi(options, data.grid)
options["mask"] = Image(roi.raw(), name=roi.name)
calib_name = options.pop("calib-data")
if calib_name not in self.ivm.data:
raise QpException("Calibration data not found: %s" % calib_name)
else:
calib_img = Image(self.ivm.data[calib_name].resample(
data.grid).raw(),
name=calib_name)
ref_roi_name = options.pop("ref-roi", None)
if ref_roi_name is not None:
if ref_roi_name not in self.ivm.rois:
raise QpException("Reference ROI not found: %s" % calib_name)
else:
options["ref_mask"] = Image(
self.ivm.rois[ref_roi_name].resample(data.grid).raw(),
name=ref_roi_name)
options["calib_method"] = options.pop("method", None)
output_name = options.pop("output-name", data.name + "_calib")
logbuf = six.StringIO()
wsp = Workspace(log=logbuf, **options)
wsp.calib = calib_img
## FIXME variance mode
calibrated = calib.calibrate(wsp, img)
self.log(logbuf.getvalue())
self.ivm.add(name=output_name,
data=calibrated.data,
grid=data.grid,
make_current=True)
def test_shorttr_corr_not1():
"""
Check correction for short TR is not done (and generates warning) if T1 not given
"""
TR = 3.0
perf_d = np.random.rand(5, 5, 5)
perf_img = Image(name="perfusion", image=perf_d)
calib_d = np.random.rand(5, 5, 5)
calib_img = Image(name="calib", image=calib_d)
log = StringIO()
wsp = Workspace(calib=calib_img, calib_method="voxelwise", tr=TR, log=log)
perf_calib = calib.calibrate(wsp, perf_img)
calibrated_d = perf_calib.data
assert (perf_calib.name == "perfusion_calib")
assert (perf_calib.shape == perf_img.shape)
assert ("WARNING" in log.getvalue())
# Default partition coefficient is 0.9
np.testing.assert_allclose(calibrated_d, 0.9 * perf_d / calib_d)
def test_refregion_pc():
"""
Reference region override partition coeff of ref tissue
"""
PC = 0.67
perf_img, calib_img = _get_imgs()
ref_d = np.ones((5, 5, 5))
ref_img = Image(name="refmask", image=ref_d)
wsp = Workspace(calib=calib_img,
calib_method="refregion",
refmask=ref_img,
calib_aslreg=True,
tissref="wm",
pcr=PC)
perf_calib = calib.calibrate(wsp, perf_img)
calibrated_d = perf_calib.data
m0_expected = _expected_m0(np.mean(calib_img.data), 1.0, 50, PC)
np.testing.assert_allclose(calibrated_d, perf_img.data / m0_expected)
def test_refregion_t2r():
"""
Reference region override T2 of ref tissue
"""
T2 = 635
perf_img, calib_img = _get_imgs()
ref_d = np.ones((5, 5, 5))
ref_img = Image(name="refmask", image=ref_d)
wsp = Workspace(calib=calib_img,
calib_method="refregion",
refmask=ref_img,
calib_aslreg=True,
tissref="wm",
t2r=T2)
perf_calib = calib.calibrate(wsp, perf_img)
calibrated_d = perf_calib.data
m0_expected = _expected_m0(np.mean(calib_img.data), 1.0, T2, 0.82)
np.testing.assert_allclose(calibrated_d, perf_img.data / m0_expected)
def test_refregion_defaults():
"""
Reference region defaults to CSF
"""
perf_img, calib_img = _get_imgs()
ref_d = np.ones((5, 5, 5))
ref_img = Image(name="refmask", image=ref_d)
wsp = Workspace(calib=calib_img,
calib_method="refregion",
refmask=ref_img,
calib_aslreg=True)
perf_calib = calib.calibrate(wsp, perf_img)
calibrated_d = perf_calib.data
assert (perf_calib.name == "perfusion_calib")
assert (perf_calib.shape == perf_img.shape)
# CSF defaults
m0_expected = _expected_m0(np.mean(calib_img.data), 4.3, 750, 1.15)
np.testing.assert_allclose(calibrated_d, perf_img.data / m0_expected)
def test_refregion_tr():
"""
Reference region override TR
"""
TR = 7.1
perf_img, calib_img = _get_imgs()
ref_d = np.ones((5, 5, 5))
ref_img = Image(name="refmask", image=ref_d)
wsp = Workspace(calib=calib_img,
calib_method="refregion",
refmask=ref_img,
calib_aslreg=True,
tissref="wm",
tr=TR)
perf_calib = calib.calibrate(wsp, perf_img)
calibrated_d = perf_calib.data
# WM defaults
m0_expected = _expected_m0(np.mean(calib_img.data), 1.0, 50, 0.82, tr=TR)
np.testing.assert_allclose(calibrated_d, perf_img.data / m0_expected)
def output_native(wsp, basil_wsp, report=None):
"""
Create native space output images
:param wsp: Workspace object. Output will be placed in a sub workspace named ``native``
:param basil_wsp: Workspace in which Basil modelling has been run. The ``finalstep``
attribute is expected to point to the final output workspace
"""
if not wsp.output_native: return
wsp.sub("native")
# Output the differenced data averaged across repeats for kinetic curve comparison
# with the model
if wsp.asldata.iaf in ("tc", "ct", "diff"):
wsp.native.diffdata_mean = wsp.asldata.diff().mean_across_repeats()
# Output model fitting results
prefixes = ["", "mean"]
if wsp.output_stddev:
prefixes.append("std")
if wsp.output_var:
prefixes.append("var")
for fabber_name, oxasl_output in OUTPUT_ITEMS.items():
for prefix in prefixes:
is_variance = prefix == "var"
if is_variance:
# Variance is not output by Fabber natively so we get it by
# squaring the standard deviation. We also pass the flag
# to the calibration routine so it can square the correction
# factors
fabber_output = "std_%s" % fabber_name
elif prefix:
fabber_output = "%s_%s" % (prefix, fabber_name)
else:
fabber_output = fabber_name
img = basil_wsp.finalstep.ifnone(fabber_output, None)
if img is not None:
# Make negative/nan values = 0 and ensure masked value zeroed
data = np.copy(img.data)
data[~np.isfinite(data)] = 0
data[img.data < 0] = 0
data[wsp.rois.mask.data == 0] = 0
img = Image(data, header=img.header)
name, multiplier, calibrate, _, _, _ = oxasl_output
if prefix and prefix != "mean":
name = "%s_%s" % (name, prefix)
if calibrate:
# Anything that needs calibration also requires sensitivity correction
img, = corrections.apply_sensitivity_correction(wsp, img)
if is_variance:
img = Image(np.square(img.data), header=img.header)
setattr(wsp.native, name, img)
if calibrate and wsp.calib is not None:
alpha = wsp.ifnone(
"calib_alpha",
1.0 if wsp.asldata.iaf in ("ve", "vediff") else
0.85 if wsp.asldata.casl else 0.98)
img = calib.calibrate(wsp,
img,
multiplier=multiplier,
alpha=alpha,
var=is_variance)
name = "%s_calib" % name
setattr(wsp.native, name, img)
if wsp.save_mask:
wsp.native.mask = wsp.rois.mask
output_report(wsp.native, report=report)
