@@ -52,7 +52,7 @@ def diffeomorphic_tensor_pipeline(name='DiffeoTen',
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params = {'array_size' : (128 , 128 , 64 )}):
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"""
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Workflow that performs a diffeomorphic registration
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- (Rigid and Affine follwed by Diffeomorphic)
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+ (Rigid and Affine followed by Diffeomorphic)
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Note: the requirements for a diffeomorphic registration specify that
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the dimension 0 is a power of 2 so images are resliced prior to
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registration. Remember to move origin and reslice prior to applying xfm to
@@ -143,7 +143,7 @@ def diffeomorphic_tensor_pipeline(name='DiffeoTen',
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return wf
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-
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+ '''
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def apply_diffeo(name='ApplyDiffeo', params={'array_size': (128, 128, 64)}):
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"""
@@ -167,10 +167,9 @@ def apply_diffeo(name='ApplyDiffeo', params={'array_size': (128, 128, 64)}):
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outputnode = pe.Node(niu.IdentityInterface(
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fields=['out_file']),
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name='outputnode')
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- origin_node = pe .Node (dtitk .TVAdjustVoxSp (origin = (0 , 0 , 0 )),
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+ origin_node = pe.Node(dtitk.SVAdjustVoxSp (origin=(0, 0, 0)),
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name='origin_node')
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- reslice_node_pow2 = pe .Node (dtitk .TVResample (
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- origin = (0 , 0 , 0 ),
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+ reslice_node_pow2 = pe.Node(dtitk.SVResample(
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array_size=params['array_size']),
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name='reslice_node_pow2')
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apply_xfm_node = pe.Node(dtitk.DiffeoScalarVol(), name='apply_xfm_node')
@@ -183,4 +182,4 @@ def apply_diffeo(name='ApplyDiffeo', params={'array_size': (128, 128, 64)}):
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wf.connect(inputnode, 'xfm_file', apply_xfm_node, 'transform')
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wf.connect(apply_xfm_node, 'out_file', outputnode, 'out_file')
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- return wf
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+ return wf'''
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