def test_pvc_only_one_map_given2():
"""
Check that PVC correction fails if you only give the WM map
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace(infertiss=True, inferbat=True)
wsp.pwm = Image(np.random.rand(5, 5, 5))
with pytest.raises(ValueError):
basil.basil_steps(wsp, img)
def test_pvc_no_tissue():
"""
Check that PVC correction fails if you do not infer the tissue component
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace(infertiss=False, inferbat=True)
wsp.pgm = Image(np.random.rand(5, 5, 5))
wsp.pwm = Image(np.random.rand(5, 5, 5))
with pytest.raises(ValueError):
basil.basil_steps(wsp, img)
def test_pvc():
"""
FIXME we need to test the PVC initialization step
and how to do this is not finalized
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace(infertiss=True, inferbat=True)
wsp.pgm = Image(np.random.rand(5, 5, 5))
wsp.pwm = Image(np.random.rand(5, 5, 5))
steps = basil.basil_steps(wsp, img)
assert (len(steps) == 3)
options = _get_defaults(img)
# options.update({
# "incpve" : True,
# })
_check_step(steps[0], desc_text="tissue", options=options)
# _check_step(steps[1], desc_text="PVE", options=options)
options.update({
"method": "spatialvb",
"param-spatial-priors": "N+",
"PSP_byname1": "ftiss",
"PSP_byname1_type": "M",
"max-iterations": 200,
"convergence": "maxits",
})
options.pop("max-trials")
_check_step(steps[2], desc_text="spatial")
def test_onestep():
"""
Check that single step mode works when you would normally get multiple steps
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace(infertiss=True,
inferbat=True,
infertau=True,
inferart=True,
spatial=True,
onestep=True)
steps = basil.basil_steps(wsp, img)
assert (len(steps) == 1)
options = _get_defaults(img)
options.update({
"method": "spatialvb",
"param-spatial-priors": "N+",
"PSP_byname1": "ftiss",
"PSP_byname1_type": "M",
"inctau": True,
"incart": True,
"inferart": True,
"infertau": True,
"convergence": "maxits",
})
options.pop("max-trials")
_check_step(steps[0], desc_text="spatial", options=options)
def test_t1im():
"""
Check T1 image priors are correctly handled
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace(infertiss=True, inferbat=True, infert1=True)
wsp.t1im = Image(np.random.rand(5, 5, 5))
steps = basil.basil_steps(wsp, img)
assert (len(steps) == 2)
options = _get_defaults(img)
_check_step(steps[0],
desc_text="tissue",
options=dict(options, **{
"inct1": True,
}))
_check_step(
steps[1],
desc_text="T1",
options=dict(
options,
**{
"inct1": True,
"infert1": True,
"PSP_byname1": "T_1",
"PSP_byname1_type": "I",
#"PSP_byname1_image" : "t1file",
}))
def run(self, options):
""" Run the process """
from oxasl import basil
self.get_asldata(options)
self.asldata = self.asldata.diff().reorder("rt")
self.ivm.add(self.asldata.data, grid=self.grid, name=self.asldata.name)
roi = self.get_roi(options, self.grid)
if roi.name not in self.ivm.rois:
self.ivm.add(roi)
self.debug("Basil options: ")
self.debug(options)
wsp = workspace_from_options(options,
images=["t1im", "pwm", "pgm"],
grid=self.grid,
ivm=self.ivm)
wsp.asldata = self.asldata
wsp.mask = qpdata_to_fslimage(roi)
self.steps = basil.basil_steps(wsp, self.asldata)
self.log(wsp.log.getvalue())
self.step_num = 0
self.status = Process.RUNNING
self._next_step()
def test_nodata():
"""
Check we get an error if there is no data
"""
wsp = Workspace()
with pytest.raises(ValueError):
steps = basil.basil_steps(wsp, None)
def test_infer_nothing():
"""
Check we get an error if there is nothing to infer
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace()
with pytest.raises(ValueError):
steps = basil.basil_steps(wsp, img)
def test_initmvn():
"""
Check the supply of an initialization MVN
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace(infertiss=True, inferbat=True)
wsp.initmvn = Image(np.random.rand(5, 5, 5, 6))
steps = basil.basil_steps(wsp, img)
assert (len(steps) == 1)
options = _get_defaults(img)
options.update({"continue-from-mvn": wsp.initmvn})
_check_step(steps[0], desc_text="tissue", options=options)
def test_defaults():
"""
Check the basic defaults (infer tissue perfusion and bolus arrival time)
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace()
wsp.infertiss = True
wsp.inferbat = True
steps = basil.basil_steps(wsp, img)
assert (len(steps) == 1)
options = _get_defaults(img)
_check_step(steps[0], desc_text="tissue", options=options)
def test_fix_bat():
"""
Check fixing the arrival time, which is normally inferred
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace(infertiss=True, inferbat=False)
steps = basil.basil_steps(wsp, img)
assert (len(steps) == 1)
options = _get_defaults(img)
options.pop("incbat")
options.pop("inferbat")
_check_step(steps[0], desc_text="tissue", options=options)
def test_random_extra_options():
"""
Check that any additional keyword arguments are passed to Fabber
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace(infertiss=True, inferbat=True)
kwargs = {
"phase-of-moon-correction-factor": 7,
"random-output-proportion-percent": 36,
}
steps = basil.basil_steps(wsp, img, **kwargs)
assert (len(steps) == 1)
options = _get_defaults(img)
options.update(kwargs)
_check_step(steps[0], desc_text="tissue", options=options)
def test_artonly():
"""
Check we can infer arterial component without tissue step
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace(infertiss=False, inferbat=True, inferart=True)
steps = basil.basil_steps(wsp, img)
assert (len(steps) == 1)
options = _get_defaults(img)
options.update({
"incart": True,
"inferart": True,
})
options.pop("inctiss")
options.pop("infertiss")
_check_step(steps[0], desc_text="arterial", options=options)
def test_max_iterations():
"""
Check that max iterations can be overridden
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace(infertiss=True, inferbat=True)
kwargs = {
"max-iterations": 123,
}
steps = basil.basil_steps(wsp, img, **kwargs)
assert (len(steps) == 1)
options = _get_defaults(img)
options.update({
"max-iterations": 123,
})
_check_step(steps[0], desc_text="tissue", options=options)
def test_inferarttau():
"""
Check inference of bolus duration (tau) and arterial component
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace(infertiss=True,
inferbat=True,
infertau=True,
inferart=True)
steps = basil.basil_steps(wsp, img)
assert (len(steps) == 3)
options = _get_defaults(img)
_check_step(steps[0],
desc_text="tissue",
options=dict(options, **{
"inctau": True,
"incart": True,
}))
_check_step(steps[1],
desc_text="arterial",
options=dict(
options, **{
"inctau": True,
"incart": True,
"inferart": True,
}))
_check_step(steps[2],
desc_text="bolus",
options=dict(
options, **{
"inctau": True,
"incart": True,
"infertau": True,
"inferart": True,
}))
def test_spatial():
"""
Check final spatial step
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace(infertiss=True, inferbat=True, spatial=True)
steps = basil.basil_steps(wsp, img)
assert (len(steps) == 2)
options = _get_defaults(img)
_check_step(steps[0], desc_text="tissue", options=options)
options.update({
"method": "spatialvb",
"param-spatial-priors": "N+",
"PSP_byname1": "ftiss",
"PSP_byname1_type": "M",
"convergence": "maxits",
})
options.pop("max-trials")
_check_step(steps[1], desc_text="spatial", options=options)
def test_inferpc():
"""
Check the pre-capiliary component
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace(infertiss=True, inferbat=True, inferpc=True)
steps = basil.basil_steps(wsp, img)
assert (len(steps) == 2)
options = _get_defaults(img)
_check_step(steps[0],
desc_text="tissue",
options=dict(options, **{
"incpc": True,
}))
_check_step(steps[1],
desc_text="pre-capiliary",
options=dict(options, **{
"incpc": True,
"inferpc": True,
}))
def test_infert1():
"""
Check inference of T1
"""
d = np.random.rand(5, 5, 5, 6)
img = AslImage(name="asldata", image=d, tis=[1.5], order="prt")
wsp = Workspace(infertiss=True, inferbat=True, infert1=True)
steps = basil.basil_steps(wsp, img)
assert (len(steps) == 2)
options = _get_defaults(img)
_check_step(steps[0],
desc_text="tissue",
options=dict(options, **{
"inct1": True,
}))
_check_step(steps[1],
desc_text="T1",
options=dict(options, **{
"inct1": True,
"infert1": True,
}))