def show_gaussian_smoothing_effects():
SNR=20
type='laplacian'
print 'Loading data'
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
S,stics=SticksAndBall(bvals, bvecs, d=0.0015, S0=100,
angles=[(0, 0),(90.,0),(90.,90.)],
fractions=[33,33,33], snr=SNR)
data=S[None,:]
ei=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei.radius=np.arange(0,5,0.1)
ei.gaussian_weight=None
ei.set_operator(type)
ei.update()
ei.fit()
ei2=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei2.radius=np.arange(0,5,0.1)
ei2.gaussian_weight=0.01
ei2.set_operator(type)
ei2.update()
ei2.fit()
ei3=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei3.radius=np.arange(0,5,0.1)
ei3.gaussian_weight=0.05
ei3.set_operator(type)
ei3.update()
ei3.fit()
ei4=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei4.radius=np.arange(0,5,0.1)
ei4.gaussian_weight=0.1
ei4.set_operator(type)
ei4.update()
ei4.fit()
blobs=np.zeros((4,len(ei.odf_vertices)))
blobs[0]=ei.ODF.ravel()
blobs[1]=ei2.ODF.ravel()
blobs[2]=ei3.ODF.ravel()
blobs[3]=ei4.ODF.ravel()
show_blobs(blobs[None,None,:,:],ei.odf_vertices,ei.odf_faces,size=1.5,scale=1.)
def show_phantom_crossing(fname,type='dsi',weight=None):
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
#fname='/home/eg309/Desktop/t5'
#fname='/home/eg309/Data/orbital_phantoms/20.0'
#fname='/tmp/t5'
fvol = np.memmap(fname, dtype='f8', mode='r', shape=(64,64,64,len(bvals)))
data=fvol[32-10:32+10,32-10:32+10,31:34,:]
#data=fvol[32,32,32,:][None,None,None,:]
ten=Tensor(data,bvals,bvecs,thresh=50)
FA=ten.fa()
def H(x):
res=(2*x*np.cos(x) + (x**2-2)*np.sin(x))/x**3
res[np.isnan(res)]=1/3.
return res
if type=='dsi':
mod=DiffusionSpectrum(data,bvals,bvecs,odf_sphere='symmetric642',auto=True,save_odfs=True)
if type=='gqi':
mod=GeneralizedQSampling(data,bvals,bvecs,1.2,odf_sphere='symmetric642',squared=False,save_odfs=True)
if type=='gqi2':
mod=GeneralizedQSampling(data,bvals,bvecs,3.,odf_sphere='symmetric642',squared=True,save_odfs=True)
if type=='eitl':
ei=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei.radius=np.arange(0,5,0.1)
ei.gaussian_weight=weight
ei.set_operator('laplacian')
ei.update()
ei.fit()
mod=ei
if type=='eitl2':
ei=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei.radius=np.arange(0,5,0.1)
ei.gaussian_weight=weight
ei.set_operator('laplap')
ei.update()
ei.fit()
mod=ei
if type=='eits':
ei=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei.radius=np.arange(0,5,0.1)
ei.gaussian_weight=weight
ei.set_operator('signal')
ei.update()
ei.fit()
mod=ei
show_blobs(mod.ODF[:,:,0,:][:,:,None,:],mod.odf_vertices,mod.odf_faces,fa_slice=FA[:,:,0],size=1.5,scale=1.)
#dn.radius=np.arange(1.,6,0.2)
#dn.radius=np.arange(0,1.,0.2)
dn.radiusn=len(dn.radius)
dn.origin=8#16
dn.precompute_fast_coords()
dn.precompute_equator_indices(5.)
dn.precompute_angular(0.1)
dn.fit()
"""
ei=EquatorialInversion(dat,bvals,bvecs,odf_sphere='symmetric642',
half_sphere_grads=True,auto=False,save_odfs=True,fast=True)
ei.radius=np.arange(0,5,0.1)
ei.gaussian_weight=0.05
ei.set_operator('laplacian')
ei.update()
ei.fit()
ds=DiffusionSpectrum(dat,bvals,bvecs,
odf_sphere='symmetric642',
mask=None,
half_sphere_grads=True,
auto=True,
save_odfs=True)
ten=Tensor(dat,bvals,bvecs)
FA=ten.fa()
#gq=GeneralizedQSampling(dat,bvals,bvecs,1.2,odf_sphere='symmetric642',squared=False,save_odfs=True)
#gq2=GeneralizedQSampling(dat,bvals,bvecs,3.,odf_sphere='symmetric642',squared=True,save_odfs=True)
#show_blobs(ds.ODF,ds.odf_vertices,ds.odf_faces,FA.squeeze(),size=1.5,scale=1.)
def compare_dni_dsi_gqi_gqi2_eit():
#stop
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
type=3
axesn=200
SNR=20
data,gold,angs,anglesn,axesn,angles=generate_gold_data(bvals,bvecs,fibno=type,axesn=axesn,SNR=SNR)
gq=GeneralizedQSampling(data,bvals,bvecs,1.2,odf_sphere='symmetric642',squared=False,save_odfs=True)
gq2=GeneralizedQSampling(data,bvals,bvecs,3.,odf_sphere='symmetric642',squared=True,save_odfs=True)
ds=DiffusionSpectrum(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True)
ds.filter_width=32.
ds.update()
ds.fit()
ei=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei.radius=np.arange(0,5,0.1)
ei.gaussian_weight=None#0.01
ei.set_operator('laplacian')
ei.update()
ei.fit()
ei2=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei2.radius=np.arange(0,5,0.1)
ei2.gaussian_weight=None
ei2.set_operator('laplap')
ei2.update()
ei2.fit()
ei3=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei3.radius=np.arange(0,5,0.1)
ei3.gaussian_weight=None
ei3.set_operator('signal')
ei3.update()
ei3.fit()
"""
blobs=np.zeros((2,4,642))
no=200
blobs[0,0,:]=gq.ODF[no]
blobs[0,1,:]=gq2.ODF[no]
blobs[0,2,:]=ds.ODF[no]
blobs[0,3,:]=ei.ODF[no]
no=399
blobs[1,0,:]=gq.ODF[no]
blobs[1,1,:]=gq2.ODF[no]
blobs[1,2,:]=ds.ODF[no]
blobs[1,3,:]=ei.ODF[no]
show_blobs(blobs[None,:,:,:],ei.odf_vertices,ei.odf_faces,1.2)
"""
#stop
vts=gq.odf_vertices
def simple_peaks(ODF,faces,thr):
x,g=ODF.shape
PK=np.zeros((x,5))
IN=np.zeros((x,5))
for (i,odf) in enumerate(ODF):
peaks,inds=peak_finding(odf,faces)
ibigp=np.where(peaks>thr*peaks[0])[0]
l=len(ibigp)
if l>3:
l=3
PK[i,:l]=peaks[:l]
IN[i,:l]=inds[:l]
return PK,IN
thresh=0.5
PK,IN=simple_peaks(ds.ODF,ds.odf_faces,thresh)
res,me,st =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
PK,IN=simple_peaks(gq.ODF,ds.odf_faces,thresh)
res2,me2,st2 =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
PK,IN=simple_peaks(gq2.ODF,ds.odf_faces,thresh)
res3,me3,st3 =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
PK,IN=simple_peaks(ei.ODF,ds.odf_faces,thresh)
res4,me4,st4 =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
PK,IN=simple_peaks(ei2.ODF,ds.odf_faces,thresh)
res5,me5,st5 =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
PK,IN=simple_peaks(ei3.ODF,ei3.odf_faces,thresh)
res6,me6,st6 =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
#res7,me7,st7 =do_compare(ei2.PK,ei2.IN,0,anglesn,axesn,type)
if type==1:
plt.figure(1)
plt.plot(res,'r',label='DSI')
plt.plot(res2,'g',label='GQI')
plt.plot(res3,'b',label='GQI2')
plt.plot(res4,'k',label='EITL')
plt.plot(res5,'k--',label='EITL2')
plt.plot(res6,'k-.',label='EITS')
#plt.xlabel('angle')
#plt.ylabel('resolution')
#plt.title('Angular accuracy')
plt.legend()
plt.show()
else:
if type==3:
x,y,z=sphere2cart(np.ones(len(angles)),np.deg2rad(angles),np.zeros(len(angles)))
x2,y2,z2=sphere2cart(np.ones(len(angles)),np.deg2rad(angles),np.deg2rad(120*np.ones(len(angles))))
angles2=[]
for i in range(len(x)):
angles2.append(np.rad2deg(np.arccos(np.dot([x[i],y[i],z[i]],[x2[i],y2[i],z2[i]]))))
angles=angles2
plt.figure(1)
plt.plot(angles,me,'r',linewidth=3.,label='DSI')
plt.plot(angles,me2,'g',linewidth=3.,label='GQI')
plt.plot(angles,me3,'b',linewidth=3.,label='GQI2')
plt.plot(angles,me4,'k',linewidth=3.,label='EITL')
plt.plot(angles,me5,'k--',linewidth=3.,label='EITL2')
plt.plot(angles,me6,'k-.',linewidth=3.,label='EITS')
#plt.plot(angles,me7,'r--',linewidth=3.,label='EITL2')
plt.xlabel('angle')
plt.ylabel('similarity')
title='Angular similarity of ' + str(type) + '-fibres crossing with SNR ' + str(SNR)
plt.title(title)
plt.legend(loc='center right')
plt.savefig('/tmp/test.png',dpi=300)
plt.show()
def show_simulated_2fiber_crossings():
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
data=create_data_2fibers(bvals,bvecs,d=0.0015,S0=100,angles=np.arange(0,47,5),snr=None)
#data=create_data_2fibers(bvals,bvecs,d=0.0015,S0=100,angles=np.arange(0,92,5),snr=None)
print data.shape
#stop
#"""
dn=DiffusionNabla(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=True)
dn.peak_thr=.4
dn.iso_thr=.05
dn.radius=np.arange(0,5,0.1)
dn.radiusn=len(dn.radius)
dn.create_qspace(bvals,bvecs,16,8)
dn.radon_params(64)
dn.precompute_interp_coords()
dn.precompute_fast_coords()
dn.precompute_equator_indices(5.)
dn.precompute_angular(None)#0.01)
dn.fit()
dn2=DiffusionNabla(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=False)
dn2.peak_thr=.4
dn2.iso_thr=.05
dn2.radius=np.arange(0,5,0.1)
dn2.radiusn=len(dn.radius)
dn2.create_qspace(bvals,bvecs,16,8)
dn2.radon_params(64)
dn2.precompute_interp_coords()
dn2.precompute_fast_coords()
dn2.precompute_equator_indices(5.)
dn2.precompute_angular(None)#0.01)
dn2.fit()
#"""
eis=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=True)
#eis.radius=np.arange(0,6,0.2)
eis.radius=np.arange(0,5,0.1)
eis.gaussian_weight=None
eis.set_operator('signal')#'laplap')
eis.update()
eis.fit()
eil=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=True)
#eil.radius=np.arange(0,6,0.2)
eil.radius=np.arange(0,5,0.1)
eil.gaussian_weight=None
eil.set_operator('laplacian')#'laplap')
eil.update()
eil.fit()
eil2=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=True)
#eil2.radius=np.arange(0,6,0.2)
eil2.radius=np.arange(0,5,0.1)
eil2.gaussian_weight=None
eil2.set_operator('laplap')
eil2.update()
eil2.fit()
ds=DiffusionSpectrum(data,bvals,bvecs,odf_sphere='symmetric642',auto=True,save_odfs=True)
gq=GeneralizedQSampling(data,bvals,bvecs,1.2,odf_sphere='symmetric642',squared=False,auto=False,save_odfs=True)
gq.fit()
gq2=GeneralizedQSampling(data,bvals,bvecs,3.,odf_sphere='symmetric642',squared=True,auto=False,save_odfs=True)
gq2.fit()
#blobs=np.zeros((19,1,8,dn.odfn))
blobs=np.zeros((10,1,6,dn.odfn))
"""
blobs[:,0,0]=dn.odfs()
blobs[:,0,1]=dn2.odfs()
blobs[:,0,2]=eis.odfs()
blobs[:,0,3]=eil.odfs()
blobs[:,0,4]=eil2.odfs()
blobs[:,0,5]=ds.odfs()
blobs[:,0,6]=gq.odfs()
blobs[:,0,7]=gq2.odfs()
"""
blobs[:,0,0]=dn2.odfs()
blobs[:,0,1]=eil.odfs()
eo=eil2.odfs()
#eo[eo<0.05*eo.max()]=0
blobs[:,0,2]=eo
blobs[:,0,3]=ds.odfs()
blobs[:,0,4]=gq.odfs()
blobs[:,0,5]=gq2.odfs()
show_blobs(blobs,dn.odf_vertices,dn.odf_faces,scale=0.5)
def compare_sdni_eitl():
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
type=3
axesn=200
SNR=20
data,gold,angs,anglesn,axesn,angles=generate_gold_data(bvals,bvecs,fibno=type,axesn=axesn,SNR=SNR)
ei=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=True)
ei.radius=np.arange(0,5,0.1)
ei.gaussian_weight=None
ei.set_operator('laplacian')
#{'reflect','constant','nearest','mirror', 'wrap'}
ei.set_mode(order=1,zoom=1,mode='constant')
ei.update()
ei.fit()
dn=DiffusionNabla(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=False)
dn.radius=np.arange(0,5,0.1)
dn.gaussian_weight=None
dn.update()
dn.fit()
vts=ei.odf_vertices
def simple_peaks(ODF,faces,thr):
x,g=ODF.shape
PK=np.zeros((x,5))
IN=np.zeros((x,5))
for (i,odf) in enumerate(ODF):
peaks,inds=peak_finding(odf,faces)
ibigp=np.where(peaks>thr*peaks[0])[0]
l=len(ibigp)
if l>3:
l=3
PK[i,:l]=peaks[:l]
IN[i,:l]=inds[:l]
return PK,IN
print "test0"
thresh=0.5
PK,IN=simple_peaks(ei.ODF,ei.odf_faces,thresh)
res,me,st =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
#PKG,ING=simple_peaks(eig.ODF,ei.odf_faces,thresh)
#resg,meg,stg =do_compare(gold,vts,PKG,ING,0,anglesn,axesn,type)
PK,IN=simple_peaks(dn.ODF,dn.odf_faces,thresh)
res2,me2,st2 =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
if type==3:
x,y,z=sphere2cart(np.ones(len(angles)),np.deg2rad(angles),np.zeros(len(angles)))
x2,y2,z2=sphere2cart(np.ones(len(angles)),np.deg2rad(angles),np.deg2rad(120*np.ones(len(angles))))
angles2=[]
for i in range(len(x)):
angles2.append(np.rad2deg(np.arccos(np.dot([x[i],y[i],z[i]],[x2[i],y2[i],z2[i]]))))
angles=angles2
print "test1"
plt.figure(1)
plt.plot(angles,me,'k:',linewidth=3.,label='EITL')
plt.plot(angles,me2,'k--',linewidth=3.,label='sDNI')
#plt.plot(angles,me7,'r--',linewidth=3.,label='EITL2')
plt.xlabel('angle')
plt.ylabel('similarity')
title='Angular similarity of ' + str(type) + '-fibres crossing with SNR ' + str(SNR)
plt.title(title)
plt.legend(loc='center right')
plt.savefig('/tmp/test.png',dpi=300)
plt.show()
def humans():
no_seeds=10**6
visualize = False
save_odfs = False
dirname = "data/"
for root, dirs, files in os.walk(dirname):
if root.endswith('101_32'):
base_dir = root+'/'
filename = 'raw'
base_filename = base_dir + filename
nii_filename = base_filename + 'bet.nii.gz'
bvec_filename = base_filename + '.bvec'
bval_filename = base_filename + '.bval'
flirt_mat = base_dir + 'DTI/flirt.mat'
fa_filename = base_dir + 'DTI/fa.nii.gz'
fsl_ref = '/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz'
dpy_filename = base_dir + 'DTI/res_tracks_dti.dpy'
print bvec_filename
img = nib.load(nii_filename)
data = img.get_data()
affine = img.get_affine()
bvals = np.loadtxt(bval_filename)
gradients = np.loadtxt(bvec_filename).T # this is the unitary direction of the gradient
tensors = Tensor(data, bvals, gradients, thresh=50)
FA = tensors.fa()
FA = tensors.fa()
famask=FA>=.2
ds=DiffusionSpectrum(data,bvals,gradients,odf_sphere='symmetric642',mask=famask,half_sphere_grads=True,auto=True,save_odfs=save_odfs)
gq=GeneralizedQSampling(data,bvals,gradients,1.2,odf_sphere='symmetric642',mask=famask,squared=False,save_odfs=save_odfs)
ei=EquatorialInversion(data,bvals,gradients,odf_sphere='symmetric642',mask=famask,half_sphere_grads=True,auto=False,save_odfs=save_odfs,fast=True)
ei.radius=np.arange(0,5,0.4)
ei.gaussian_weight=0.05
ei.set_operator('laplacian')
ei.update()
ei.fit()
ds.PK[FA<.2]=np.zeros(5)
ei.PK[FA<.2]=np.zeros(5)
gq.PK[FA<.2]=np.zeros(5)
print 'create seeds'
x,y,z,g=ei.PK.shape
seeds=np.zeros((no_seeds,3))
sid=0
while sid<no_seeds:
rx=(x-1)*np.random.rand()
ry=(y-1)*np.random.rand()
rz=(z-1)*np.random.rand()
seed=np.ascontiguousarray(np.array([rx,ry,rz]),dtype=np.float64)
seeds[sid]=seed
sid+=1
euler = EuDX(a=FA, ind=tensors.ind(), seeds=seeds, a_low=.2)
dt_tracks = [track for track in euler]
euler2 = EuDX(a=ds.PK, ind=ds.IN, seeds=seeds, odf_vertices=ds.odf_vertices, a_low=.2)
ds_tracks = [track for track in euler2]
euler3 = EuDX(a=gq.PK, ind=gq.IN, seeds=seeds, odf_vertices=gq.odf_vertices, a_low=.2)
gq_tracks = [track for track in euler3]
euler4 = EuDX(a=ei.PK, ind=ei.IN, seeds=seeds, odf_vertices=ei.odf_vertices, a_low=.2)
ei_tracks = [track for track in euler4]
if visualize:
renderer = fvtk.ren()
fvtk.add(renderer, fvtk.line(tensor_tracks, fvtk.red, opacity=1.0))
fvtk.show(renderer)
print 'Load images to be used for registration'
img_fa =nib.load(fa_filename)
img_ref =nib.load(fsl_ref)
mat=flirt2aff(np.loadtxt(flirt_mat),img_fa,img_ref)
del img_fa
del img_ref
print 'transform the tracks'
dt_linear = transform_tracks(dt_tracks,mat)
ds_linear = transform_tracks(ds_tracks,mat)
gq_linear = transform_tracks(gq_tracks,mat)
ei_linear = transform_tracks(ei_tracks,mat)
print 'save tensor tracks'
dpy_filename = base_dir + 'DTI/dt_linear.dpy'
print dpy_filename
dpr_linear = Dpy(dpy_filename, 'w')
dpr_linear.write_tracks(dt_linear)
dpr_linear.close()
print 'save ei tracks'
dpy_filename = base_dir + 'DTI/ei_linear.dpy'
print dpy_filename
dpr_linear = Dpy(dpy_filename, 'w')
dpr_linear.write_tracks(ei_linear)
dpr_linear.close()
print 'save ds tracks'
dpy_filename = base_dir + 'DTI/ds_linear.dpy'
print dpy_filename
dpr_linear = Dpy(dpy_filename, 'w')
dpr_linear.write_tracks(ds_linear)
dpr_linear.close()
print 'save gq tracks'
dpy_filename = base_dir + 'DTI/gq_linear.dpy'
print dpy_filename
dpr_linear = Dpy(dpy_filename, 'w')
dpr_linear.write_tracks(gq_linear)
dpr_linear.close()
print 'save lengths'
pkl_filename = base_dir + 'DTI/dt_lengths.pkl'
save_pickle(pkl_filename,lengths(dt_linear))
pkl_filename = base_dir + 'DTI/ei_lengths.pkl'
save_pickle(pkl_filename,lengths(ei_linear))
pkl_filename = base_dir + 'DTI/gq_lengths.pkl'
save_pickle(pkl_filename,lengths(gq_linear))
pkl_filename = base_dir + 'DTI/ds_lengths.pkl'
save_pickle(pkl_filename,lengths(ds_linear))