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Documentation.FirstRegistration History
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Changed lines 97-99 from:
registering tensor_aff.nii.gz to ixi_aging_template.nii.gz ...
starting at Wed 17 Mar 2010 11:12:10 GMT
to:
registering tensor_aff.nii.gz to ../template/ixi_aging_template.nii.gz ...
starting at Tue 14 May 2013 15:17:12 BST
Changed lines 102-107 from:
reading the buffer ... time consumed = 0.124621
converting the buffer ... time consumed = 0.329648
reading the buffer ... time consumed = 0.096498
converting the buffer ... time consumed = 0.320465
reading the buffer ... time consumed = 0.0061
converting the buffer ... time consumed = 0.061917
to:
Reading ../template/ixi_aging_template.nii.gz ... Done in 0.089588s
Reading tensor_aff_diffeo.nii.gz ... Done in 0.07375s
Reading ../template/ixi_aging_template_brain_mask.nii.gz ... Done in 0.0027s Changed lines 109-123 from:
before : sum = 8168.28, img = 136138, prior = 0, reg = 0
iter 0: sum = 8168.28, img = 136138, prior = 0, reg = 0
iter 1: sum = 7211.9, img = 112838, prior = 84.6267, reg = 10829.6
iter 2: sum = 7060.49, img = 115091, prior = 102.421, reg = 3619.39
iter 3: sum = 6949.42, img = 113655, prior = 240.254, reg = 2652.99
iter 4: sum = 6832.82, img = 109095, prior = 559.649, reg = 5779.49
iter 5: sum = 6396.04, img = 99003.7, prior = 1296.8, reg = 8153.44
iter 6: sum = 6179.73, img = 94654.8, prior = 1537.49, reg = 8667.3
iter 7: sum = 6059.45, img = 92756.1, prior = 1630.11, reg = 8276.83
iter 8: sum = 5972.22, img = 90753.6, prior = 1672.25, reg = 8994.44
iter 9: sum = 5871.24, img = 89937.1, prior = 1634.9, reg = 7788.08
iter 10: sum = 5817.53, img = 90583.3, prior = 1610.49, reg = 5537.17
iter 11: sum = 5807.34, img = 90685.2, prior = 1600.45, reg = 5154.64
iter = 77, iterGrad = 12
after : sum = 5802.36, img = 90609.8, prior = 1601.18, reg = 5142.08 to:
before : sum = 8158.72, img = 135979, prior = 0, reg = 0
iter 0: sum = 8158.72, img = 135979, prior = 0, reg = 0
iter 1: sum = 7210.51, img = 112827, prior = 85.6186, reg = 10808.2
iter 2: sum = 7062.5, img = 115089, prior = 101.244, reg = 3675.72
iter 3: sum = 6969.66, img = 114319, prior = 214.507, reg = 2226.6
iter 4: sum = 6790.31, img = 108454, prior = 610.284, reg = 5551.64
iter 5: sum = 6383.92, img = 97723.5, prior = 1522.14, reg = 9207.51
iter 6: sum = 6170.33, img = 94188.1, prior = 1652.23, reg = 8845.62
iter 7: sum = 6064.83, img = 92752.7, prior = 1650.75, reg = 8364.8
iter 8: sum = 5976.85, img = 91041, prior = 1612.5, reg = 8828.51
iter 9: sum = 5878.97, img = 90597.4, prior = 1507.46, reg = 7309.53
iter 10: sum = 5833.3, img = 91212.9, prior = 1463.47, reg = 5354.62
iter 11: sum = 5821.98, img = 91344.5, prior = 1452.53, reg = 4901.4
iter = 79, iterGrad = 12
after : sum = 5816.1, img = 91272.4, prior = 1462.49, reg = 4838.15 Changed lines 127-138 from:
before : sum = 5802.36, img = 90613.1, prior = 1601.08, reg = 5136.76
iter 0: sum = 5802.36, img = 90613.1, prior = 1601.08, reg = 5136.76
iter 1: sum = 5607.02, img = 88306.6, prior = 1531.51, reg = 3886.86
iter 2: sum = 5542.43, img = 87806.5, prior = 1563.3, reg = 2942.73
iter 3: sum = 5459.21, img = 84804.4, prior = 1833.63, reg = 4689.76
iter 4: sum = 5415.33, img = 82908.1, prior = 2134.14, reg = 5685.67
iter 5: sum = 5377.68, img = 81885.3, prior = 2440.31, reg = 5513.15
iter 6: sum = 5358.07, img = 81296.5, prior = 2600.79, reg = 5505.06
iter 7: sum = 5345.17, img = 80919.1, prior = 2700.55, reg = 5499.25
iter 8: sum = 5334.94, img = 80625, prior = 2774.01, reg = 5501.12
iter = 70, iterGrad = 9
after : sum = 5329.62, img = 80522.8, prior = 2770.06, reg = 5531.52 to:
before : sum = 5816.1, img = 91268.9, prior = 1463.12, reg = 4841.4
iter 0: sum = 5816.1, img = 91268.9, prior = 1463.12, reg = 4841.4
iter 1: sum = 5625.25, img = 88799.8, prior = 1405.19, reg = 3918.67
iter 2: sum = 5559.75, img = 88363.7, prior = 1443.22, reg = 2840.02
iter 3: sum = 5480.6, img = 85541.3, prior = 1714.91, reg = 4415.75
iter 4: sum = 5435.87, img = 83679.9, prior = 2009.07, reg = 5354.16
iter 5: sum = 5401.83, img = 82336.5, prior = 2364.7, reg = 5629.19
iter 6: sum = 5382.33, img = 81653.4, prior = 2578.97, reg = 5630.57
iter 7: sum = 5369.56, img = 81257.3, prior = 2703.85, reg = 5593.42
iter 8: sum = 5354.88, img = 80747.9, prior = 2830.33, reg = 5674.36
iter 9: sum = 5339.62, img = 79964.5, prior = 3006.74, reg = 6026.97
iter 10: sum = 5321.12, img = 78827.7, prior = 3314.55, reg = 6500.24
iter 11: sum = 5306.74, img = 78208.9, prior = 3496.09, reg = 6614.74
iter 12: sum = 5291.45, img = 77610.8, prior = 3630.61, reg = 6793.49
iter 13: sum = 5278.73, img = 77181, prior = 3726.12, reg = 6881.43
iter 14: sum = 5269.66, img = 77015.4, prior = 3759.82, reg = 6818.98
iter = 118, iterGrad = 15
after : sum = 5265.43, img = 77061.1, prior = 3728.67, reg = 6722.72 Changed lines 148-166 from:
before : sum = 5329.62, img = 80527.6, prior = 2770.2, reg = 5523.74
iter 0: sum = 5329.62, img = 80527.6, prior = 2770.2, reg = 5523.74
iter 1: sum = 5085.43, img = 77289.6, prior = 2458.4, reg = 5055.34
iter 2: sum = 5022.94, img = 76956.7, prior = 2431.93, reg = 4058.62
iter 3: sum = 4959.16, img = 74813.8, prior = 2534.43, reg = 5422.2
iter 4: sum = 4928.64, img = 73488.7, prior = 2713.65, reg = 6198.92
iter 5: sum = 4902.69, img = 72547.2, prior = 2999.8, reg = 6246.94
iter 6: sum = 4885.75, img = 71990.7, prior = 3240.21, reg = 6057.24
iter 7: sum = 4875.76, img = 71651.8, prior = 3362.79, reg = 6009.35
iter 8: sum = 4864.53, img = 71164.1, prior = 3484.6, reg = 6155.52
iter 9: sum = 4854.35, img = 70744.4, prior = 3552.76, reg = 6360.12
iter 10: sum = 4841.87, img = 70304.9, prior = 3644.76, reg = 6477.54
iter 11: sum = 4830.68, img = 69946.6, prior = 3735.12, reg = 6509.38
iter 12: sum = 4819.41, img = 69493.2, prior = 3827.74, reg = 6676.15
iter 13: sum = 4808.09, img = 69011.1, prior = 3925.33, reg = 6872.21
iter 14: sum = 4798.17, img = 68612.8, prior = 4030.43, reg = 6959.05
iter 15: sum = 4789.83, img = 68397.1, prior = 4129.12, reg = 6827.26
iter = 130, iterGrad = 16
after : sum = 4786.13, img = 68421.1, prior = 4105.91, reg = 6757.16 to:
before : sum = 5265.43, img = 77063.6, prior = 3727.06, reg = 6722.73
iter 0: sum = 5265.43, img = 77063.6, prior = 3727.06, reg = 6722.73
iter 1: sum = 4993.26, img = 74081.1, prior = 3296.8, reg = 5467.9
iter 2: sum = 4926.22, img = 73720.4, prior = 3224.65, reg = 4513.37
iter 3: sum = 4863.62, img = 71868.2, prior = 3215.34, reg = 5749.89
iter 4: sum = 4836.11, img = 70807.6, prior = 3314.56, reg = 6404.92
iter 5: sum = 4812.95, img = 69964.3, prior = 3556.42, reg = 6486.28
iter 6: sum = 4800.12, img = 69570.3, prior = 3729.15, reg = 6324.78
iter 7: sum = 4793.56, img = 69422.8, prior = 3796.2, reg = 6214.18
iter = 65, iterGrad = 8
after : sum = 4790.48, img = 69411.8, prior = 3774.2, reg = 6209.05 Changed lines 162-170 from:
before : sum = 4786.13, img = 68419.8, prior = 4106.93, reg = 6756.25
iter 0: sum = 4786.13, img = 68419.8, prior = 4106.93, reg = 6756.25
iter 1: sum = 4567.69, img = 65875.8, prior = 3790.26, reg = 5902.84
iter 2: sum = 4507.44, img = 65530.5, prior = 3612.95, reg = 5357.95
iter 3: sum = 4410.79, img = 63065.6, prior = 3125.99, reg = 7856.44
iter 4: sum = 4363.41, img = 61668.3, prior = 2956.56, reg = 9191.38
iter 5: sum = 4348.32, img = 61353.2, prior = 3003.53, reg = 9169.37
iter 6: sum = 4332.71, img = 61094.2, prior = 3142.75, reg = 8819.52
iter 7: sum = 4325.18, img = 60979.5, prior = 3251.31, reg = 8531.86 to:
before : sum = 4790.48, img = 69411.4, prior = 3773.09, reg = 6212.3
iter 0: sum = 4790.48, img = 69411.4, prior = 3773.09, reg = 6212.3
iter 1: sum = 4581.37, img = 66837.3, prior = 3473.34, reg = 5595.1
iter 2: sum = 4523.92, img = 66511, prior = 3310.61, reg = 5055.02
iter 3: sum = 4428.27, img = 63992.3, prior = 2864.49, reg = 7557.09
iter 4: sum = 4380.25, img = 62494.1, prior = 2716.68, reg = 8973.4
iter 5: sum = 4364.7, img = 62142.7, prior = 2769.34, reg = 8980.19
iter 6: sum = 4347.86, img = 61840.1, prior = 2919.03, reg = 8638.8
iter 7: sum = 4339.33, img = 61704.8, prior = 3034.09, reg = 8340.82 Changed line 172 from:
after : sum = 4321.37, img = 60875.8, prior = 3257.98, reg = 8575.87 to:
after : sum = 4335.43, img = 61614.8, prior = 3042.83, reg = 8356.92 Changed lines 181-182 from:
converting the buffer . time consumed = 0.712962
writing the buffer ... time consumed = 5.45516
to:
Writing tensor_aff_to_ixi_aging_template.5.df.nii.gz ... Done in 4.16411s Changed lines 183-194 from:
reading the buffer . time consumed = 0.759686
converting the buffer ... time consumed = 1.15919
gaussian smoothing: sigma = [ 1, 1, 1] ... time consumed = 2.67
gaussian smoothing: sigma = [ 1, 1, 1] ... time consumed = 2.63
maxNorm = 9.68 iterations = 19
. . . . . .
converting the buffer ... time consumed = 0.71
writing the buffer ... time consumed = 5.45
reading the buffer ... time consumed = 0.006225
converting the buffer ... time consumed = 0.061575
input volume ixi_aging_template_brain_mask.nii.gz
size: 128x128x64, voxel size: 1.75x1.75x2.25, origin: [-0, 0, -0]
to:
Reading tensor_aff_to_ixi_aging_template.5.df.nii.gz ... Done in 0.520504s
Gaussian smoothing: sigma = [ 1, 1, 1] ... Done in 1.77s
Gaussian smoothing: sigma = [ 1, 1, 1] ... Done in 1.7s
maxNorm = 9.49 iterations = 18
Voxelwise scaling tensor_aff_to_ixi_aging_template.5.df.nii.gz by 3.81e-06 ... Done in 0.0356s
. . . . . . . . . . . . . . . . . .
Writing tensor_aff_to_ixi_aging_template.5.df.nii.gz ... Done in 4.09s
Reading ../template/ixi_aging_template_brain_mask.nii.gz ... Done in 0.002721s
input volume ../template/ixi_aging_template_brain_mask.nii.gz
size: 128x128x64, voxel size: 1.75x1.75x2.25, origin: [0, 0, -0] Changed lines 194-195 from:
output volume tensor_aff_jac_mask.nii.gz
size: 224x224x144, voxel size: 1x1x1, origin: [0, 0, 0] to:
output volume specification: size: 224x224x144, voxel size: 1x1x1, origin: [0, 0, 0] Changed lines 196-197 from:
converting the buffer ... time consumed = 0.216576
writing the buffer ... time consumed = 0.391072
to:
Writing tensor_aff_jac_mask.nii.gz ... Done in 0.259388s Changed lines 198-199 from:
reading the buffer . time consumed = 0.763508
converting the buffer ... time consumed = 1.14678
to:
Reading tensor_aff_to_ixi_aging_template.5.df.nii.gz ... Done in 0.518063s Changed lines 200-202 from:
converting the buffer . time consumed = 0.206743
writing the buffer ... time consumed = 3.90551
JACOBIAN STATISTICS: after current iteration mean = 0.982445 min = 0.492266 max = 1.97035
to:
Writing tensor_aff_to_ixi_aging_template.5.df_jac.nii.gz ... Done in 2.87775s
JACOBIAN STATISTICS: after current iteration mean = 0.980048 min = 0.487728 max = 1.87831 # of voxels = 1.78546e+06 Changed lines 208-215 from:
reading the buffer ... time consumed = 0.110032
converting the buffer . time consumed = 0.327256
reading the buffer ... time consumed = 0.759356
converting the buffer ... time consumed = 1.16019
backward resampling ...time consumed = 3.82906
converting the buffer ... time consumed = 0.181622
writing the buffer ... time consumed = 0.875428
IMAGE SIMILARITY: after previous iteration = 136150 after current iteration = 71152.7
to:
Reading tensor_aff.nii.gz ... Done in 0.079715s
Reading tensor_aff_to_ixi_aging_template.5.df.nii.gz ... Done in 0.531295s
backward resampling ...time consumed = 2.34449
Writing tensor_aff_diffeo_current.nii.gz ... Done in 0.667442s
IMAGE SIMILARITY: after previous iteration = 135992 after current iteration = 71756
Changed line 54 from:
Final Registration Parameters = 106 -58.1 -60.9 0.158 -0.033 -0.108 to:
Final Registration Parameters = 106 -58.1 -60.9 0.158 -0.033 -0.108 Changed lines 83-84 from:
The detailed usage of the command is given here. The output in the command window looks very similar to that of the rigid registration. The program takes about a minute to run and will overwrite the "tensor_aff.nii.gz" and "tensor.aff" with the newer version, i.e., the affine aligned volume and the corresponding affine transformation.
to:
The detailed usage of the command is not given here, since the output in the command window looks very similar to that of the rigid registration. The program takes about a minute to run and will overwrite the "tensor_aff.nii.gz" and "tensor.aff" with the newer version, i.e., the affine aligned volume and the corresponding affine transformation.
Changed line 38 from:
Rigid Registration of tensor.nii.gz to ixi_aging_template.nii.gz to:
Rigid Registration of tensor.nii.gz to ../template/ixi_aging_template.nii.gz Changed lines 41-42 from:
reading the buffer ... time consumed = 0.124157
converting the buffer ... time consumed = 0.315986
to:
Reading ../template/ixi_aging_template.nii.gz ... Done in 0.086205s Changed lines 43-47 from:
gaussian smoothing: sigma = [0.873, 0.873, 0.624] ... time consumed = 0.538
reading the buffer .. time consumed = 0.0837
converting the buffer . time consumed = 0.225
gaussian smoothing: sigma = [0.736, 0.736, 0.736] ... time consumed = 0.299
Initial Difference = 5.19e+06
to:
Gaussian smoothing: sigma = [0.873, 0.873, 0.624] ... Done in 0.333s
Reading tensor.nii.gz ... Done in 0.0609s
Gaussian smoothing: sigma = [0.736, 0.736, 0.736] ... Done in 0.188s
Initial Difference = 5.13e+06 Changed line 49 from:
both iteration 0 : 1.9e+06 to:
both iteration 0 : 1.91e+06 Changed lines 52-54 from:
both iteration 3 : 1.65e+06
cpu time consumed in seconds: 2.63
Final Registration Parameters = -106 -58.1 -60.9 0.16 0.034 0.104 to:
both iteration 3 : 1.64e+06
cpu time consumed in seconds: 1.4
Final Registration Parameters = 106 -58.1 -60.9 0.158 -0.033 -0.108 Changed lines 63-70 from:
reading the buffer .. time consumed = 0.096026
converting the buffer .. time consumed = 0.220238
trans (inverse) applied = [-119.059, -56.001, -40.015]
[ 0.995, 0.099, 0.034; -0.103, 0.982, 0.160; -0.017, -0.162, 0.987]
reading output volume specification from ixi_aging_template.nii.gz
backward resampling ...time consumed = 0.661
converting the buffer ... time consumed = 0.189
writing the buffer ... time consumed = 0.548
to:
Reading tensor.nii.gz ... Done in 0.068549s
trans (inverse) applied = [120.391, -79.026, -43.910]
[ 0.994, -0.102, -0.033; 0.106, 0.982, 0.158; 0.016, -0.160, 0.987]
reading output volume specification from ../template/ixi_aging_template.nii.gz
backward resampling ...time consumed = 0.431
Writing tensor_aff.nii.gz ... Done in 0.426s
Changed lines 17-18 from:
The [[http://www.nitrc.org/frs/download.php/2306/ixi_aging_template_v2.0.tgz|IXI aging DTI template]] contains, among other things, the file "ixi_aging_template.nii.gz", the template in the DTI-TK compatible format, which we will use as the template volume. We will also need the file "ixi_aging_template_brain_mask.nii.gz", which is a binary image specifying the area in the volume belonging to the brain parenchyma. The FA map of the template looks like this:
to:
The [[http://www.nitrc.org/frs/download.php/5518/ixi_aging_template_v3.0.tgz | IXI aging DTI template]] contains, among other things, the file "ixi_aging_template.nii.gz", the template in the DTI-TK compatible format, which we will use as the template volume. We will also need the file "ixi_aging_template_brain_mask.nii.gz", which is a binary image specifying the area in the volume belonging to the brain parenchyma. The FA map of the template looks like this:
Changed line 243 from:
# Studying the detailed registration tutorial. to:
# Studying the detailed registration tutorial.
Changed lines 7-9 from:
This tutorial shows you how to register one DTI volume to another using DTI-TK. The goal is to demonstrate the registration performance of DTI-TK without you having to understand many intricacies of working with DTI volumes, discussed in depth in other tutorials. Hopefully, you will be satisfied with its performance and find it worthwhile to go on to understand how to properly prepare your dataset for use with DTI-TK!
to:
This tutorial shows you how to register one DTI volume to another using DTI-TK. The goal is to demonstrate the registration performance of DTI-TK without you having to understand many intricacies of working with DTI volumes, discussed in depth in other tutorials. Hopefully, you will be satisfied with its performance and find it worthwhile to go on to understand how to properly prepare your dataset for use with DTI-TK!
'''In addition, it is very important that you run through this tutorial on your own computing environment. Reproducing the result of this tutorial is a good indicator that your system can handle DTI-TK correctly!'''
Changed lines 13-14 from:
%center%Attach:tensor_fa.png
to:
%center%Attach:tensor_fa.png|'''FA map of the subject'''
Changed lines 18-20 from:
%center%Attach:ixi_aging_template_fa.png|'FA map of the template'
to:
%center%Attach:ixi_aging_template_fa.png|'''FA map of the template'''
Changed lines 76-77 from:
%center%Attach:tensor_aff_fa.png
to:
%center%Attach:tensor_aff_fa.png|'''FA map of the rigidly-aligned subject'''
Changed lines 88-90 from:
%center%Attach:tensor_aff_fa_2.png
to:
%center%Attach:tensor_aff_fa_2.png|'''FA map of the affinely-aligned subject'''
Changed lines 236-237 from:
%center%Attach:tensor_aff_diffeo_fa.png
to:
%center%Attach:tensor_aff_diffeo_fa.png|'''FA map of the deformably-aligned subject'''
Changed lines 18-20 from:
%center%Attach:ixi_aging_template_fa.png
to:
%center%Attach:ixi_aging_template_fa.png|'FA map of the template'
Changed lines 2-3 from:
!First Registration with DTI-TK (Draft Add links to this page)
to:
!First Registration with DTI-TK
Changed lines 16-17 from:
The [[http://www.nitrc.org/frs/download.php/2306/ixi_aging_template_v2.0.tgz|IXI aging]] DTI template contains, among other things, the file "ixi_aging_template.nii.gz", the template in the DTI-TK compatible format, which we will use as the template volume. We will also need the file "ixi_aging_template_brain_mask.nii.gz", which is a binary image specifying the area in the volume belonging to the brain parenchyma. The FA map of the template looks like this:
to:
The [[http://www.nitrc.org/frs/download.php/2306/ixi_aging_template_v2.0.tgz|IXI aging DTI template]] contains, among other things, the file "ixi_aging_template.nii.gz", the template in the DTI-TK compatible format, which we will use as the template volume. We will also need the file "ixi_aging_template_brain_mask.nii.gz", which is a binary image specifying the area in the volume belonging to the brain parenchyma. The FA map of the template looks like this:
Changed lines 30-31 from:
->[@ dti_rigid_reg ixi_aging_template.nii.gz tensor.nii.gz EDS 4 4 4 0.01 to:
->[@
dti_rigid_reg ixi_aging_template.nii.gz tensor.nii.gz EDS 4 4 4 0.01 Changed lines 36-37 from:
->[@ Rigid Registration of tensor.nii.gz to ixi_aging_template.nii.gz to:
->[@
Rigid Registration of tensor.nii.gz to ixi_aging_template.nii.gz Changed lines 76-80 from:
Attach:tensor_aff_fa.png
On a 3GHz Intel core 2 duo machine, the program takes less than a minute to run. It will create two new files
"tensor_aff.nii.gz", the rigidly aligned DTI volume, and "tensor.aff", the corresponding rigid transformation.
to:
%center%Attach:tensor_aff_fa.png
On a 3GHz Intel core 2 duo machine, the program takes less than a minute to run. It will create two new files "tensor_aff.nii.gz", the rigidly aligned DTI volume, and "tensor.aff", the corresponding rigid transformation.
Changed lines 82-83 from:
->[@dti_affine_reg ixi_aging_template.nii.gz tensor.nii.gz EDS 4 4 4 0.01 1 to:
->[@
dti_affine_reg ixi_aging_template.nii.gz tensor.nii.gz EDS 4 4 4 0.01 1 Changed lines 86-89 from:
The detailed usage of the command is given here. The output in the command window looks very similar to
that of the rigid registration.
The program takes about a minute to run and will overwrite the "tensor_aff.nii.gz" and "tensor.aff" with the newer version, i.e., the affine aligned volume and the corresponding affine transformation.
to:
The detailed usage of the command is given here. The output in the command window looks very similar to that of the rigid registration. The program takes about a minute to run and will overwrite the "tensor_aff.nii.gz" and "tensor.aff" with the newer version, i.e., the affine aligned volume and the corresponding affine transformation.
Changed lines 93-94 from:
->[@ dti_diffeomorphic_reg ixi_aging_template.nii.gz tensor_aff.nii.gz ixi_aging_template_brain_mask.nii.gz 1 5 0.002 to:
->[@
dti_diffeomorphic_reg ixi_aging_template.nii.gz tensor_aff.nii.gz ixi_aging_template_brain_mask.nii.gz 1 5 0.002 Changed lines 99-100 from:
->[@registering tensor_aff.nii.gz to ixi_aging_template.nii.gz ... to:
->[@
registering tensor_aff.nii.gz to ixi_aging_template.nii.gz ... Changed line 242 from:
# Studying the detailed registration tutorial. to:
# Studying the detailed registration tutorial.
Changed lines 13-14 from:
%cframe%Attach:tensor_fa.png
to:
%center%Attach:tensor_fa.png
Changed lines 18-20 from:
Attach:ixi_aging_template_fa.png
to:
%center%Attach:ixi_aging_template_fa.png
Changed lines 88-90 from:
Attach:tensor_aff_fa_2.png
to:
%center%Attach:tensor_aff_fa_2.png
Changed lines 234-235 from:
Attach:tensor_aff_diffeo_fa.png
to:
%center%Attach:tensor_aff_diffeo_fa.png
Changed lines 13-14 from:
%cfloat%Attach:tensor_fa.png
to:
%cframe%Attach:tensor_fa.png
Changed lines 13-14 from:
to:
%cfloat%Attach:tensor_fa.png
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!!Overview
This tutorial shows you how to register one DTI volume to another using DTI-TK. The goal is to demonstrate the registration performance of DTI-TK without you having to understand many intricacies of working with DTI volumes, discussed in depth in other tutorials. Hopefully, you will be satisfied with its performance and find it worthwhile to go on to understand how to properly prepare your dataset for use with DTI-TK!
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!First Registration with DTI-TK
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!First Registration with DTI-TK (Draft Add links to this page)
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Attach:tensor_aff_fa_diffeo.png
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Attach:tensor_aff_diffeo_fa.png
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The example data set contains the file "tensor.nii.gz", an example DTI volume in the DTI-TK compatible format, which we will use as the subject volume for registration.
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[[http://www.nitrc.org/frs/download.php/1237/DTITK_Sample_Data.zip|The example data]] set contains the file "tensor.nii.gz", an example DTI volume in the DTI-TK compatible format, which we will use as the subject volume for registration.
Attach:tensor_fa.png
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The [[http://www.nitrc.org/frs/download.php/1237/DTITK_Sample_Data.zip|IXI aging]] DTI template contains, among other things, the file "ixi_aging_template.nii.gz", the template in the DTI-TK compatible format, which we will use as the template volume. We will also need the file "ixi_aging_template_brain_mask.nii.gz", which is a binary image specifying the area in the volume belonging to the brain parenchyma. The FA map of the template looks like this:
%c%Attach:tensor_fa.png
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The [[http://www.nitrc.org/frs/download.php/2306/ixi_aging_template_v2.0.tgz|IXI aging]] DTI template contains, among other things, the file "ixi_aging_template.nii.gz", the template in the DTI-TK compatible format, which we will use as the template volume. We will also need the file "ixi_aging_template_brain_mask.nii.gz", which is a binary image specifying the area in the volume belonging to the brain parenchyma. The FA map of the template looks like this:
Attach:ixi_aging_template_fa.png
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Attach:tensor_aff_fa_2.png
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Attach:tensor_aff_fa_diffeo.png
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'''FA map of the subject'''
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'''FA map of the subject''' Changed lines 14-15 from:
'''FA map of the subject'''[[>>]]
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'''FA map of the subject'''\\
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'''FA map of the subject'''[[>>]]
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'''FA map of the subject'''
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'''FA map of the subject'''\\
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'''FA map of the subject'''
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The IXI aging DTI template contains, among other things, the file "ixi_aging_template.nii.gz", the template in the DTI-TK compatible format, which we will use as the template volume. We will also need the file "ixi_aging_template_brain_mask.nii.gz", which is a binary image specifying the area in the volume belonging to the brain parenchyma. The FA map of the template looks like this:
to:
The [[http://www.nitrc.org/frs/download.php/1237/DTITK_Sample_Data.zip|IXI aging]] DTI template contains, among other things, the file "ixi_aging_template.nii.gz", the template in the DTI-TK compatible format, which we will use as the template volume. We will also need the file "ixi_aging_template_brain_mask.nii.gz", which is a binary image specifying the area in the volume belonging to the brain parenchyma. The FA map of the template looks like this:
Attach: tensor_fa.png
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The program takes about 5 minutes or less to complete for each iteration. The number of iterations is an input argument that can be adjusted for your need. The example given uses 5 iterations and completes in less than 20 minutes. The program will generate a pair of new files that include: "tensor_aff_diffeo.nii.gz", the diffeomorphic aligned volume, "tensor_aff_diffeo.df.nii.gz", the corresponding deformation field. There are a few other intermediate files will be created during the registration but will be removed when the registration completes. to:
The program takes about 5 minutes or less to complete for each iteration. The number of iterations is an input argument that can be adjusted for your need. The example given uses 5 iterations and completes in less than 20 minutes. The program will generate a pair of new files that include: "tensor_aff_diffeo.nii.gz", the diffeomorphic aligned volume, "tensor_aff_diffeo.df.nii.gz", the corresponding deformation field. There are a few other intermediate files will be created during the registration but will be removed when the registration completes.
!!What's Next?
If you are now convinced that you should give DTI-TK a try on your own data set, you should proceed by
# Reading the tutorials on data format, visualization and checking diffusivity unit
# Going through the detailed preprocessing tutorial
# Studying the detailed registration tutorial.
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(:noleft:)(:notitle:)(:title DTI-TK::First Registration:)
!First Registration with DTI-TK
We assume that you have followed the Installation tutorial to [[http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Downloads.Downloads| download]] and [[http://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Documentation.install| install]] the DTI-TK binary packages.
!! Download the example data set
The example data set contains the file "tensor.nii.gz", an example DTI volume in the DTI-TK compatible format, which we will use as the subject volume for registration.
!!Download the IXI aging DTI template
The IXI aging DTI template contains, among other things, the file "ixi_aging_template.nii.gz", the template in the DTI-TK compatible format, which we will use as the template volume. We will also need the file "ixi_aging_template_brain_mask.nii.gz", which is a binary image specifying the area in the volume belonging to the brain parenchyma. The FA map of the template looks like this:
!!Organize the data for registration
For convenience, copy "tensor.nii.gz", "ixi_aging_template.nii.gz", and "ixi_aging_template_brain_mask.nii.gz" to a new directory of your choice.
Note: Make sure you are in the new directory before running the rest of the tutorial.
!!Run the rigid registration
The command to use:
->[@ dti_rigid_reg ixi_aging_template.nii.gz tensor.nii.gz EDS 4 4 4 0.01
@]
The detailed usage of the command is given here. The output in the command window should look like this:
->[@ Rigid Registration of tensor.nii.gz to ixi_aging_template.nii.gz
Similarity Measure : EDS
Reorient Option : FS
reading the buffer ... time consumed = 0.124157
converting the buffer ... time consumed = 0.315986
sampling step size = [2, 2, 2]
gaussian smoothing: sigma = [0.873, 0.873, 0.624] ... time consumed = 0.538
reading the buffer ... time consumed = 0.0837
converting the buffer ... time consumed = 0.225
gaussian smoothing: sigma = [0.736, 0.736, 0.736] ... time consumed = 0.299
Initial Difference = 5.19e+06
initial estimate provided by center of mass alignment
cgm optimizer ftol = 0.01
both iteration 0 : 1.9e+06
both iteration 1 : 1.71e+06
both iteration 2 : 1.66e+06
both iteration 3 : 1.65e+06
cpu time consumed in seconds: 2.63
Final Registration Parameters = -106 -58.1 -60.9 0.16 0.034 0.104
Final Difference = 1.64e+06
Total Difference Computation = 30
Total Gradient Computation = 4
Output the rigid transformation as tensor.aff
inFile = tensor.nii.gz
outFile = tensor_aff.nii.gz
interpolation option is LEI
reorientOption = PPD
reading the buffer ... time consumed = 0.096026
converting the buffer ... time consumed = 0.220238
trans (inverse) applied = [-119.059, -56.001, -40.015]
[ 0.995, 0.099, 0.034; -0.103, 0.982, 0.160; -0.017, -0.162, 0.987]
reading output volume specification from ixi_aging_template.nii.gz
backward resampling ...time consumed = 0.661
converting the buffer ... time consumed = 0.189
writing the buffer ... time consumed = 0.548
done
@]
On a 3GHz Intel core 2 duo machine, the program takes less than a minute to run. It will create two new files
"tensor_aff.nii.gz", the rigidly aligned DTI volume, and "tensor.aff", the corresponding rigid transformation.
!!Run the affine registration
The command to use:
->[@dti_affine_reg ixi_aging_template.nii.gz tensor.nii.gz EDS 4 4 4 0.01 1
@]
The detailed usage of the command is given here. The output in the command window looks very similar to
that of the rigid registration.
The program takes about a minute to run and will overwrite the "tensor_aff.nii.gz" and "tensor.aff" with the newer version, i.e., the affine aligned volume and the corresponding affine transformation.
!!Run the diffeomorphic registration
The command to use:
->[@ dti_diffeomorphic_reg ixi_aging_template.nii.gz tensor_aff.nii.gz ixi_aging_template_brain_mask.nii.gz 1 5 0.002
@]
The detailed usage of the command is given here. It registers the subject to the template in an iterative process. For one iteration, the output in the command window looks like this:
->[@registering tensor_aff.nii.gz to ixi_aging_template.nii.gz ...
starting at Wed 17 Mar 2010 11:12:10 GMT
iteration 1 begins ...
Similarity Measure : DDS, Reorient Option : FS
reading the buffer ... time consumed = 0.124621
converting the buffer ... time consumed = 0.329648
reading the buffer ... time consumed = 0.096498
converting the buffer ... time consumed = 0.320465
reading the buffer ... time consumed = 0.0061
converting the buffer ... time consumed = 0.061917
starting level 2, ending level 5
Level 2
Piecewise Affine Setup: size = 8, 8, 4; vsize = 28, 28, 36
pDim = 3072; scalings : img = 0.06, prior = 0.1, reg = 0.04; ftol = 0.002
before : sum = 8168.28, img = 136138, prior = 0, reg = 0
iter 0: sum = 8168.28, img = 136138, prior = 0, reg = 0
iter 1: sum = 7211.9, img = 112838, prior = 84.6267, reg = 10829.6
iter 2: sum = 7060.49, img = 115091, prior = 102.421, reg = 3619.39
iter 3: sum = 6949.42, img = 113655, prior = 240.254, reg = 2652.99
iter 4: sum = 6832.82, img = 109095, prior = 559.649, reg = 5779.49
iter 5: sum = 6396.04, img = 99003.7, prior = 1296.8, reg = 8153.44
iter 6: sum = 6179.73, img = 94654.8, prior = 1537.49, reg = 8667.3
iter 7: sum = 6059.45, img = 92756.1, prior = 1630.11, reg = 8276.83
iter 8: sum = 5972.22, img = 90753.6, prior = 1672.25, reg = 8994.44
iter 9: sum = 5871.24, img = 89937.1, prior = 1634.9, reg = 7788.08
iter 10: sum = 5817.53, img = 90583.3, prior = 1610.49, reg = 5537.17
iter 11: sum = 5807.34, img = 90685.2, prior = 1600.45, reg = 5154.64
iter = 77, iterGrad = 12
after : sum = 5802.36, img = 90609.8, prior = 1601.18, reg = 5142.08
Level 3
Piecewise Affine Setup: size = 16, 16, 8; vsize = 14, 14, 18
pDim = 24576; scalings : img = 0.06, prior = 0.1, reg = 0.04; ftol = 0.002
before : sum = 5802.36, img = 90613.1, prior = 1601.08, reg = 5136.76
iter 0: sum = 5802.36, img = 90613.1, prior = 1601.08, reg = 5136.76
iter 1: sum = 5607.02, img = 88306.6, prior = 1531.51, reg = 3886.86
iter 2: sum = 5542.43, img = 87806.5, prior = 1563.3, reg = 2942.73
iter 3: sum = 5459.21, img = 84804.4, prior = 1833.63, reg = 4689.76
iter 4: sum = 5415.33, img = 82908.1, prior = 2134.14, reg = 5685.67
iter 5: sum = 5377.68, img = 81885.3, prior = 2440.31, reg = 5513.15
iter 6: sum = 5358.07, img = 81296.5, prior = 2600.79, reg = 5505.06
iter 7: sum = 5345.17, img = 80919.1, prior = 2700.55, reg = 5499.25
iter 8: sum = 5334.94, img = 80625, prior = 2774.01, reg = 5501.12
iter = 70, iterGrad = 9
after : sum = 5329.62, img = 80522.8, prior = 2770.06, reg = 5531.52
Level 4
Piecewise Affine Setup: size = 32, 32, 16; vsize = 7, 7, 9
pDim = 196608; scalings : img = 0.06, prior = 0.1, reg = 0.04; ftol = 0.002
before : sum = 5329.62, img = 80527.6, prior = 2770.2, reg = 5523.74
iter 0: sum = 5329.62, img = 80527.6, prior = 2770.2, reg = 5523.74
iter 1: sum = 5085.43, img = 77289.6, prior = 2458.4, reg = 5055.34
iter 2: sum = 5022.94, img = 76956.7, prior = 2431.93, reg = 4058.62
iter 3: sum = 4959.16, img = 74813.8, prior = 2534.43, reg = 5422.2
iter 4: sum = 4928.64, img = 73488.7, prior = 2713.65, reg = 6198.92
iter 5: sum = 4902.69, img = 72547.2, prior = 2999.8, reg = 6246.94
iter 6: sum = 4885.75, img = 71990.7, prior = 3240.21, reg = 6057.24
iter 7: sum = 4875.76, img = 71651.8, prior = 3362.79, reg = 6009.35
iter 8: sum = 4864.53, img = 71164.1, prior = 3484.6, reg = 6155.52
iter 9: sum = 4854.35, img = 70744.4, prior = 3552.76, reg = 6360.12
iter 10: sum = 4841.87, img = 70304.9, prior = 3644.76, reg = 6477.54
iter 11: sum = 4830.68, img = 69946.6, prior = 3735.12, reg = 6509.38
iter 12: sum = 4819.41, img = 69493.2, prior = 3827.74, reg = 6676.15
iter 13: sum = 4808.09, img = 69011.1, prior = 3925.33, reg = 6872.21
iter 14: sum = 4798.17, img = 68612.8, prior = 4030.43, reg = 6959.05
iter 15: sum = 4789.83, img = 68397.1, prior = 4129.12, reg = 6827.26
iter = 130, iterGrad = 16
after : sum = 4786.13, img = 68421.1, prior = 4105.91, reg = 6757.16
Level 5
Piecewise Affine Setup: size = 64, 64, 32; vsize = 3.5, 3.5, 4.5
pDim = 1572864; scalings : img = 0.06, prior = 0.1, reg = 0.04; ftol = 0.002
before : sum = 4786.13, img = 68419.8, prior = 4106.93, reg = 6756.25
iter 0: sum = 4786.13, img = 68419.8, prior = 4106.93, reg = 6756.25
iter 1: sum = 4567.69, img = 65875.8, prior = 3790.26, reg = 5902.84
iter 2: sum = 4507.44, img = 65530.5, prior = 3612.95, reg = 5357.95
iter 3: sum = 4410.79, img = 63065.6, prior = 3125.99, reg = 7856.44
iter 4: sum = 4363.41, img = 61668.3, prior = 2956.56, reg = 9191.38
iter 5: sum = 4348.32, img = 61353.2, prior = 3003.53, reg = 9169.37
iter 6: sum = 4332.71, img = 61094.2, prior = 3142.75, reg = 8819.52
iter 7: sum = 4325.18, img = 60979.5, prior = 3251.31, reg = 8531.86
iter = 65, iterGrad = 8
after : sum = 4321.37, img = 60875.8, prior = 3257.98, reg = 8575.87
Little Endian System
Writing VECTORS ...
size is currently determined internally.
vsize = 1, 1, 1
origin is currently default to [0, 0, 0].
outFile = tensor_aff_to_ixi_aging_template.5.df.nii.gz
Little Endian System
Reading VECTORS ...
converting the buffer ... time consumed = 0.712962
writing the buffer ... time consumed = 5.45516
converting to the diffeomorphic deformation field: ...
reading the buffer ... time consumed = 0.759686
converting the buffer ... time consumed = 1.15919
gaussian smoothing: sigma = [ 1, 1, 1] ... time consumed = 2.67
gaussian smoothing: sigma = [ 1, 1, 1] ... time consumed = 2.63
maxNorm = 9.68 iterations = 19
. . . . . . . . . . . . . . . . . . .
converting the buffer ... time consumed = 0.71
writing the buffer ... time consumed = 5.45
reading the buffer ... time consumed = 0.006225
converting the buffer ... time consumed = 0.061575
input volume ixi_aging_template_brain_mask.nii.gz
size: 128x128x64, voxel size: 1.75x1.75x2.25, origin: [-0, 0, -0]
reading output volume specification from tensor_aff_to_ixi_aging_template.5.df.nii.gz
output volume tensor_aff_jac_mask.nii.gz
size: 224x224x144, voxel size: 1x1x1, origin: [0, 0, 0]
matching the center of the old and the new voxel spaces
converting the buffer ... time consumed = 0.216576
writing the buffer ... time consumed = 0.391072
inFile = tensor_aff_to_ixi_aging_template.5.df.nii.gz
reading the buffer ... time consumed = 0.763508
converting the buffer ... time consumed = 1.14678
outFile = tensor_aff_to_ixi_aging_template.5.df_jac.nii.gz
converting the buffer ... time consumed = 0.206743
writing the buffer ... time consumed = 3.90551
JACOBIAN STATISTICS: after current iteration mean = 0.982445 min = 0.492266 max = 1.97035
inFile = tensor_aff.nii.gz
outFile = tensor_aff_diffeo_current.nii.gz
transFile = tensor_aff_to_ixi_aging_template.5.df.nii.gz
df option is FD
interpolation option is LEI
reorientOption = PPD
reading the buffer ... time consumed = 0.110032
converting the buffer ... time consumed = 0.327256
reading the buffer ... time consumed = 0.759356
converting the buffer ... time consumed = 1.16019
backward resampling ...time consumed = 3.82906
converting the buffer ... time consumed = 0.181622
writing the buffer ... time consumed = 0.875428
IMAGE SIMILARITY: after previous iteration = 136150 after current iteration = 71152.7
iteration 1 done
@]
The program takes about 5 minutes or less to complete for each iteration. The number of iterations is an input argument that can be adjusted for your need. The example given uses 5 iterations and completes in less than 20 minutes. The program will generate a pair of new files that include: "tensor_aff_diffeo.nii.gz", the diffeomorphic aligned volume, "tensor_aff_diffeo.df.nii.gz", the corresponding deformation field. There are a few other intermediate files will be created during the registration but will be removed when the registration completes.
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