def _construct_ilang_model(self):
# define the ilang probabilistic model
self.ilang_model = MR_Static_Gaussian(self)
# construct a basic Directed Acyclical Graph
self.ilang_graph = ProbabilisticGraphicalModel(['x', 'k', 'sigma'])
self.ilang_graph.set_nodes_given(['k', 'sigma'], True)
self.ilang_graph.add_dependence(self.ilang_model, {
'x': 'x',
'k': 'k',
'sigma': 'sigma'
})
def _construct_ilang_model(self):
# define the ilang probabilistic model
self.ilang_model = MR_Static_Gaussian(self)
# construct a basic Directed Acyclical Graph
self.ilang_graph = ProbabilisticGraphicalModel(['x','k','sigma'])
self.ilang_graph.set_nodes_given(['k','sigma'],True)
self.ilang_graph.add_dependence(self.ilang_model,{'x':'x','k':'k','sigma':'sigma'})
class MR_Static_Scan:
def __init__(self):
self.kspace = None
def load_kspace(self,filename):
self.kspace = KSpace()
self.kspace.load_from_file(filename)
self._construct_ilang_model()
def reconstruct_iterative(self, method=None, iterations=20, tol=None, dt=0.4, beta=0.05):
kspace = self.kspace.get_static_data()
shape = kspace.shape
image = asarray(complex64(zeros(shape)),order='F')
for iter in range(iterations):
diff = kspace - self.project(image)
grad = -self.backproject(diff)
image_abs = abs(image)
kernel = -ones((3,3,3))
kernel[1,1,1]=26;
# 1) smooth magnitude
eps = 1e-8
norm = 2*(scipy.ndimage.filters.convolve(image_abs,kernel)+eps) / (image_abs+eps); # !!!
# 2) smooth square magnitude
#norm = 4*(scipy.ndimage.filters.convolve(image_abs,kernel))
g0_real = -real(image) * norm
g0_imag = -imag(image) * norm
g0 = complex64(g0_real+g0_imag*1j)
image = image - dt * (grad - beta*g0)
return Image3D(abs(image))
def reconstruct_ifft(self):
kspace = self.kspace.get_static_data()
image = asarray( fftshift(ifftn(kspace)), order='F')
return Image3D(abs(image))
def project(self, volume ):
return asarray( fftn(fftshift(volume)), order='F')
def backproject(self, kspace ):
return asarray( fftshift(ifftn(kspace)), order='F' )
def _construct_ilang_model(self):
# define the ilang probabilistic model
self.ilang_model = MR_Static_Gaussian(self)
# construct a basic Directed Acyclical Graph
self.ilang_graph = ProbabilisticGraphicalModel(['x','k','sigma'])
self.ilang_graph.set_nodes_given(['k','sigma'],True)
self.ilang_graph.add_dependence(self.ilang_model,{'x':'x','k':'k','sigma':'sigma'})
# construct a basic sampler object
#self.sampler = Sampler(self.ilang_graph)
def _repr_html_(self):
if not has_ipy_table:
return "Please install ipy_table."
table_data = [
['bla ',1],
['blabla',2], ]
table = ipy_table.make_table(table_data)
table = ipy_table.apply_theme('basic_left')
table = ipy_table.set_global_style(float_format="%3.3f")
return table._repr_html_()
class MR_Static_Scan:
def __init__(self):
self.kspace = None
def load_kspace(self, filename):
self.kspace = KSpace()
self.kspace.load_from_file(filename)
self._construct_ilang_model()
def reconstruct_iterative(self,
method=None,
iterations=20,
tol=None,
dt=0.4,
beta=0.05):
kspace = self.kspace.get_static_data()
shape = kspace.shape
image = asarray(complex64(zeros(shape)), order='F')
for iter in range(iterations):
diff = kspace - self.project(image)
grad = -self.backproject(diff)
image_abs = abs(image)
kernel = -ones((3, 3, 3))
kernel[1, 1, 1] = 26
# 1) smooth magnitude
eps = 1e-8
norm = 2 * (scipy.ndimage.filters.convolve(image_abs, kernel) +
eps) / (image_abs + eps)
# !!!
# 2) smooth square magnitude
#norm = 4*(scipy.ndimage.filters.convolve(image_abs,kernel))
g0_real = -real(image) * norm
g0_imag = -imag(image) * norm
g0 = complex64(g0_real + g0_imag * 1j)
image = image - dt * (grad - beta * g0)
return Image3D(abs(image))
def reconstruct_ifft(self):
kspace = self.kspace.get_static_data()
image = asarray(fftshift(ifftn(kspace)), order='F')
return Image3D(abs(image))
def project(self, volume):
return asarray(fftn(fftshift(volume)), order='F')
def backproject(self, kspace):
return asarray(fftshift(ifftn(kspace)), order='F')
def _construct_ilang_model(self):
# define the ilang probabilistic model
self.ilang_model = MR_Static_Gaussian(self)
# construct a basic Directed Acyclical Graph
self.ilang_graph = ProbabilisticGraphicalModel(['x', 'k', 'sigma'])
self.ilang_graph.set_nodes_given(['k', 'sigma'], True)
self.ilang_graph.add_dependence(self.ilang_model, {
'x': 'x',
'k': 'k',
'sigma': 'sigma'
})
# construct a basic sampler object
#self.sampler = Sampler(self.ilang_graph)
def _repr_html_(self):
if not has_ipy_table:
return "Please install ipy_table."
table_data = [
['bla ', 1],
['blabla', 2],
]
table = ipy_table.make_table(table_data)
table = ipy_table.apply_theme('basic_left')
table = ipy_table.set_global_style(float_format="%3.3f")
return table._repr_html_()