Hello,
There is an alignment issue with preprocessing of fMRI data using afni_proc.py and I don’t know how to fix it. I didn’t get any warning or error message but when visually examine the images, I found that after volreg block, the epi data is clearly not correctly aligned to anatomical image.
The “align_epi_anat.py -anat P05_2_t1+orig -epi P05_vwfa_1+orig -epi_base 0 -giant_move -epi2anat” gives the same alignment results. Following is the output:
#++ align_epi_anat version: 1.58
#Script is running (command trimmed):
3dAttribute DELTA ./P05_vwfa_1+orig
#Script is running (command trimmed):
3dAttribute DELTA ./P05_vwfa_1+orig
#Script is running (command trimmed):
3dAttribute DELTA ./P05_2_t1+orig
#++ Multi-cost is lpc
#++ Removing all the temporary files
#Script is running:
\rm -f ./__tt_P05_vwfa_1*
#Script is running:
\rm -f ./__tt_P05_2_t1*
#Script is running (command trimmed):
3dcopy ./P05_2_t1+orig ./__tt_P05_2_t1+orig
++ 3dcopy: AFNI version=AFNI_19.1.19 (Jun 13 2019) [64-bit]
#++ Removing skull from anat data
#Script is running (command trimmed):
3dSkullStrip -orig_vol -input ./__tt_P05_2_t1+orig -prefix ./__tt_P05_2_t1_ns
#Script is running (command trimmed):
3dinfo ./__tt_P05_2_t1_ns+orig | \grep ‘Data Axes Tilt:’|\grep ‘Oblique’
#++ Dataset /Volumes/PatientReadingMRI/FischerBaum_PatientRSA/orig/P05_VWFA/__tt_P05_2_t1_ns+orig is not oblique
#Script is running (command trimmed):
3dinfo ./P05_vwfa_1+orig | \grep ‘Data Axes Tilt:’|\grep ‘Oblique’
#++ Dataset /Volumes/PatientReadingMRI/FischerBaum_PatientRSA/orig/P05_VWFA/P05_vwfa_1+orig is not oblique
#Script is running (command trimmed):
3dAttribute TAXIS_OFFSETS ./P05_vwfa_1+orig
#++ Correcting for slice timing
#Script is running (command trimmed):
3dTshift -prefix ./__tt_P05_vwfa_1_tsh -cubic ./P05_vwfa_1+orig
++ 3dTshift: AFNI version=AFNI_19.1.19 (Jun 13 2019) [64-bit]
#++ Volume registration for epi data
#Script is running (command trimmed):
3dvolreg -1Dfile ./P05_vwfa_1_tsh_vr_motion.1D -1Dmatrix_save ./__tt_P05_vwfa_1_tsh_vr_mat.aff12.1D -prefix ./__tt_P05_vwfa_1_tsh_vr -base 0 -cubic ./__tt_P05_vwfa_1_tsh+orig
++ 3dvolreg: AFNI version=AFNI_19.1.19 (Jun 13 2019) [64-bit]
++ Authored by: RW Cox
++ Max displacement in automask = 0.29 (mm) at sub-brick 111
++ Max delta displ in automask = 0.12 (mm) at sub-brick 20
#++ Creating representative epi sub-brick
#Script is running (command trimmed):
3dbucket -prefix ./__tt_P05_vwfa_1_tsh_vr_ts ./__tt_P05_vwfa_1_tsh_vr+orig’[0]’
++ 3dbucket: AFNI version=AFNI_19.1.19 (Jun 13 2019) [64-bit]
#++ removing skull or area outside brain
#Script is running (command trimmed):
3dSkullStrip -orig_vol -input ./__tt_P05_vwfa_1_tsh_vr_ts+orig -prefix ./__tt_P05_vwfa_1_tsh_vr_ts_ns
#++ Computing weight mask
#Script is running (command trimmed):
3dBrickStat -automask -percentile 90.000000 1 90.000000 ./__tt_P05_vwfa_1_tsh_vr_ts_ns+orig
#++ Applying threshold of 1274.000000 on /Volumes/PatientReadingMRI/FischerBaum_PatientRSA/orig/P05_VWFA/__tt_P05_vwfa_1_tsh_vr_ts_ns+orig
#Script is running (command trimmed):
3dcalc -datum float -prefix ./__tt_P05_vwfa_1_tsh_vr_ts_ns_wt -a ./__tt_P05_vwfa_1_tsh_vr_ts_ns+orig -expr ‘min(1,(a/1274.000000))’
++ 3dcalc: AFNI version=AFNI_19.1.19 (Jun 13 2019) [64-bit]
++ Authored by: A cast of thousands
++ Output dataset ././__tt_P05_vwfa_1_tsh_vr_ts_ns_wt+orig.BRIK
#++ Aligning anat data to epi data
#Script is running (command trimmed):
3dAllineate -lpc -wtprefix ./__tt_P05_2_t1_ns_al_wtal -weight ./__tt_P05_vwfa_1_tsh_vr_ts_ns_wt+orig -source ./__tt_P05_2_t1_ns+orig -prefix ./__tt_P05_2_t1_al -base ./__tt_P05_vwfa_1_tsh_vr_ts_ns+orig -cmass -1Dmatrix_save ./P05_2_t1_al_mat.aff12.1D -master BASE -mast_dxyz 0.976562 -weight_frac 1.0 -maxrot 6 -maxshf 10 -VERB -warp aff -source_automask+4 -twobest 11 -twopass -VERB -maxrot 45 -maxshf 40 -fineblur 1 -source_automask+2
++ 3dAllineate: AFNI version=AFNI_19.1.19 (Jun 13 2019) [64-bit]
++ Authored by: Zhark the Registrator
++ Source dataset: ./__tt_P05_2_t1_ns+orig.HEAD
++ Base dataset: ./__tt_P05_vwfa_1_tsh_vr_ts_ns+orig.HEAD
++ Loading datasets
++ 1625226 voxels in -source_automask+2
++ Zero-pad: ybot=12 ytop=10
++ Zero-pad: zbot=16 ztop=14
++ 214271 voxels [14.9%] in weight mask
++ Output dataset ./__tt_P05_2_t1_ns_al_wtal+orig.BRIK
++ Number of points for matching = 214271
++ NOTE: base and source coordinate systems have different handedness
-
Orientations: base=Right handed (RAI); source=Left handed (ASR)
-
- It is nothing to worry about: 3dAllineate aligns based on coordinates.
-
- But it is always important to check the alignment visually to be sure.
++ Local correlation: blok type = ‘RHDD(7.38)’
++ base center of mass = 65.499 68.146 40.939 (index)
- source center of mass = 137.546 122.640 95.330 (index)
- source-target CM = -2.418 15.720 -8.544 (xyz)
- center of mass shifts = -2.418 15.720 -8.544
++ shift param auto-range: -63.6…58.7 -56.0…87.5 -79.8…62.7 - Range param#4 [z-angle] = -6.000000 … 6.000000
- Range param#5 [x-angle] = -6.000000 … 6.000000
- Range param#6 [y-angle] = -6.000000 … 6.000000
- Range param#1 [x-shift] = -12.417618 … 7.582382
- Range param#2 [y-shift] = 5.719864 … 25.719864
- Range param#3 [z-shift] = -18.544090 … 1.455910
- Range param#4 [z-angle] = -45.000000 … 45.000000
- Range param#5 [x-angle] = -45.000000 … 45.000000
- Range param#6 [y-angle] = -45.000000 … 45.000000
- Range param#1 [x-shift] = -42.417618 … 37.582382
- Range param#2 [y-shift] = -24.280136 … 55.719864
- Range param#3 [z-shift] = -48.544090 … 31.455910
- 12 free parameters
++ Normalized convergence radius = 0.0000089
++ changing output grid spacing to 0.9766 mm
++ OpenMP thread count = 4
++ ======= Allineation of 1 sub-bricks using Local Pearson Correlation Signed ======= - source mask has 1625226 [out of 12582912] voxels
- base mask has 243567 [out of 1440000] voxels
++ ========== sub-brick #0 ========== [total CPU to here=3.4 s]
++ *** Coarse pass begins *** -
- Enter alignment setup routine
-
- copying base image
-
- copying source image
-
- Smoothing base; radius=3.00
-
- Smoothing source; radius=3.00
- !source mask fill: ubot=0 usiz=267.5
-
- copying weight image
-
- using 98765 points from base image [use_all=0]
-
- Exit alignment setup routine
-
- Search for coarse starting parameters
- 88762 total points stored in 1312 ‘RHDD(7.96645)’ bloks
-
- number of free params = 6
-
- Test (64+61)*64 params [top5=o±.]:#[#1=-0.0135179] o[#3=-0.0169718] -…[#10=-0.0273781] o.+±[#131=-0.032992] -.±.+o++.-[#1155=-0.0396421] .[#3628=-0.0411247] ++$[#4490=-0.0452505] *o…
-
- best 45 costs found:
0 v=-0.045251: -14.02 20.28 -4.65 -24.53 29.49 26.10 [rand]
1 v=-0.043390: -14.02 11.16 -4.65 -24.53 29.49 26.10 [rand]
2 v=-0.041125: -15.75 2.39 4.79 -30.00 30.00 -30.00 [grid]
3 v=-0.039642: 10.92 -10.95 4.79 15.00 30.00 15.00 [grid]
4 v=-0.038775: -6.50 21.95 -0.75 -4.32 22.85 18.95 [rand]
5 v=-0.037162: -24.74 -13.91 -1.55 -8.73 38.75 -33.73 [rand]
6 v=-0.036055: -14.02 11.16 -12.44 -24.53 29.49 26.10 [rand]
7 v=-0.035914: 9.18 20.28 -12.44 24.53 29.49 -26.10 [rand]
8 v=-0.034816: -6.50 9.49 -0.75 -4.32 22.85 18.95 [rand]
9 v=-0.034808: 24.25 42.39 -35.21 -30.00 30.00 -30.00 [grid]
10 v=-0.034664: 24.25 29.05 -21.88 -30.00 30.00 -30.00 [grid]
11 v=-0.034424: 1.66 9.49 -0.75 4.32 22.85 18.95 [rand]
12 v=-0.033785: 1.66 21.95 -0.75 4.32 22.85 18.95 [rand]
13 v=-0.033208: 28.97 45.68 -12.95 -42.54 -9.20 -25.31 [rand]
14 v=-0.032992: 10.92 -10.95 4.79 15.00 15.00 15.00 [grid]
15 v=-0.032827: 11.00 -6.97 22.79 4.37 -20.12 -15.12 [rand]
16 v=-0.032718: -15.75 2.39 18.12 -15.00 -15.00 15.00 [grid]
17 v=-0.032241: 12.87 -5.17 21.85 14.56 -29.40 -15.92 [rand]
18 v=-0.032012: -15.75 -10.95 18.12 -15.00 -15.00 15.00 [grid]
19 v=-0.031864: 24.25 42.39 -35.21 -30.00 15.00 -30.00 [grid]
20 v=-0.031721: -18.56 6.25 15.81 -20.99 15.73 16.09 [rand]
21 v=-0.031403: -26.77 -13.97 4.48 -28.68 38.91 -32.36 [rand]
22 v=-0.031373: 10.92 29.05 4.79 15.00 30.00 15.00 [grid]
23 v=-0.031365: 1.66 21.95 -0.75 4.32 22.85 -18.95 [rand]
24 v=-0.031262: 10.92 2.39 -21.88 -15.00 30.00 30.00 [grid]
25 v=-0.031121: -15.75 -10.95 18.12 -15.00 -30.00 15.00 [grid]
26 v=-0.030997: -18.56 6.25 15.81 -20.99 15.73 -16.09 [rand]
27 v=-0.030917: 19.90 -13.91 -1.55 8.73 38.75 33.73 [rand]
28 v=-0.030900: -14.02 20.28 -4.65 24.53 29.49 26.10 [rand]
29 v=-0.030745: 32.45 46.27 -24.32 -11.68 29.04 -39.38 [rand]
30 v=-0.030671: -14.02 20.28 -12.44 -24.53 29.49 26.10 [rand]
31 v=-0.030353: 13.73 6.25 15.81 20.99 15.73 16.09 [rand]
32 v=-0.030296: 11.61 0.00 6.12 24.25 37.55 4.41 [rand]
33 v=-0.030214: -6.50 9.49 -0.75 4.32 22.85 -18.95 [rand]
34 v=-0.030064: 10.92 -10.95 18.12 15.00 -30.00 -15.00 [grid]
35 v=-0.030029: 10.92 -10.95 18.12 15.00 -15.00 15.00 [grid]
36 v=-0.029969: 32.45 -14.83 7.23 -11.68 29.04 -39.38 [rand]
37 v=-0.029882: -39.22 48.81 -28.74 39.82 23.12 9.72 [rand]
38 v=-0.029564: 1.66 9.49 -0.75 -4.32 22.85 18.95 [rand]
39 v=-0.029416: 10.92 42.39 4.79 15.00 30.00 15.00 [grid]
40 v=-0.029388: 24.25 42.39 4.79 -30.00 15.00 -30.00 [grid]
41 v=-0.029384: -15.75 2.39 4.79 -30.00 15.00 30.00 [grid]
42 v=-0.029250: -6.50 9.49 -0.75 4.32 22.85 18.95 [rand]
43 v=-0.029233: -15.75 -10.95 4.79 -15.00 -15.00 30.00 [grid]
44 v=-0.029180: -21.91 32.39 -5.94 -30.54 19.59 20.03 [rand]
- best 45 costs found:
-
- A little optimization:*[#8005=-0.0533862] *[#8012=-0.0540303] [#8018=-0.0541096] …[#8121=-0.0557016] *[#8125=-0.0563337] [#8129=-0.0564603] …[#8945=-0.0573645] *[#8956=-0.05757] …
-
- costs of the above after a little optimization:
0 v=-0.054110: -13.99 20.72 -4.96 -19.71 29.29 25.98 [rand]
1 v=-0.053381: -14.18 14.61 -4.76 -24.42 29.35 25.58 [rand]
2 v=-0.050748: -16.64 2.81 5.03 -30.23 33.93 -29.00 [grid]
3 v=-0.040017: 11.14 -11.13 4.38 14.83 30.01 14.92 [grid]
4 v=-0.056460: -6.70 21.57 -1.29 -3.95 26.10 19.51 [rand]
5 v=-0.047045: -22.28 -13.37 -0.41 -8.12 38.30 -30.94 [rand]
6 v=-0.044907: -13.47 6.19 -13.61 -23.97 30.09 24.93 [rand]
7 v=-0.048707: 4.74 20.51 -13.68 23.36 29.29 -27.78 [rand]
8 v=-0.047900: -7.09 13.44 -1.02 -2.10 22.91 19.01 [rand]
9 v=-0.038326: 24.02 38.62 -35.51 -30.00 29.66 -30.16 [grid]
10 v=-0.035157: 24.25 25.12 -21.86 -29.78 30.26 -30.28 [grid]
11 v=-0.054864: -3.32 10.96 -0.54 2.18 22.48 17.92 [rand]
12 v=-0.045747: 1.55 21.87 -1.92 6.34 30.22 17.25 [rand]
13 v=-0.042513: 32.55 44.43 -12.82 -42.93 -10.10 -25.19 [rand]
14 v=-0.039537: 11.19 -11.60 4.55 19.14 14.37 14.30 [grid]
15 v=-0.043267: 9.74 -3.30 25.47 3.77 -20.98 -12.14 [rand]
16 v=-0.048370: -16.62 -0.51 16.99 -13.69 -14.55 13.82 [grid]
17 v=-0.046601: 13.53 -9.16 21.48 14.90 -30.04 -14.74 [rand]
18 v=-0.040405: -19.15 -9.86 18.80 -14.58 -15.14 15.48 [grid]
19 v=-0.047657: 23.90 52.17 -34.22 -33.07 12.76 -32.94 [grid]
20 v=-0.047672: -19.26 9.75 10.90 -20.39 14.67 14.52 [rand]
21 v=-0.037578: -26.96 -12.88 3.49 -27.55 43.49 -32.41 [rand]
22 v=-0.038698: 10.81 34.00 5.97 15.96 30.94 16.48 [grid]
23 v=-0.044498: 2.99 19.00 -1.86 6.19 24.38 -17.19 [rand]
24 v=-0.040177: 13.97 0.63 -20.54 -14.24 30.96 29.44 [grid]
25 v=-0.049061: -15.54 -11.96 24.00 -14.19 -31.04 14.42 [grid]
26 v=-0.033165: -14.47 6.13 16.08 -21.35 15.06 -16.04 [rand]
27 v=-0.035432: 20.61 -10.53 -1.41 8.60 38.22 33.46 [rand]
28 v=-0.037227: -14.33 20.95 -4.81 24.56 29.57 30.50 [rand]
29 v=-0.036375: 31.62 45.51 -24.30 -13.13 25.22 -41.69 [rand]
*30 v=-0.057570: -11.77 19.08 -9.33 -21.84 33.82 32.48 [rand]
31 v=-0.036822: 13.74 7.48 14.86 20.87 16.11 21.05 [rand]
32 v=-0.036050: 15.10 0.15 5.43 24.77 37.98 5.09 [rand]
33 v=-0.045941: -6.48 9.78 0.17 2.14 22.10 -20.74 [rand]
34 v=-0.040540: 11.38 -10.83 19.20 14.94 -30.00 -10.27 [grid]
35 v=-0.041124: 11.76 -10.07 17.72 18.62 -14.34 13.85 [grid]
36 v=-0.034122: 32.64 -10.38 6.76 -12.06 28.80 -39.05 [rand]
37 v=-0.029933: -39.22 48.79 -28.71 40.20 22.89 9.74 [rand]
38 v=-0.048851: -2.51 10.87 -0.92 -1.07 23.89 20.66 [rand]
39 v=-0.041334: 10.85 36.61 4.03 17.30 30.37 12.84 [grid]
40 v=-0.051590: 24.36 45.92 5.73 -32.28 14.13 -35.85 [grid]
41 v=-0.036594: -16.60 3.07 10.35 -29.92 13.30 29.67 [grid]
42 v=-0.057548: -2.46 10.92 -1.54 3.40 23.63 18.15 [rand]
43 v=-0.042594: -15.42 -10.04 8.84 -19.60 -14.99 31.05 [grid]
44 v=-0.042530: -22.22 30.13 -5.38 -29.92 23.76 19.94 [rand]
- costs of the above after a little optimization:
-
- save #30 for twobest
-
- save #42 for twobest
-
- save # 4 for twobest
-
- skip #11 for twobest: too close to set #42
-
- save # 0 for twobest
-
- save # 1 for twobest
-
- save #40 for twobest
-
- save # 2 for twobest
-
- save #25 for twobest
-
- skip #38 for twobest: too close to set #42
-
- save # 7 for twobest
-
- save #16 for twobest
-
- skip # 8 for twobest: too close to set #11
-
- save #20 for twobest
-
- save #19 for twobest
-
- save # 5 for twobest
-
- save #17 for twobest
-
- save #33 for twobest
-
- save #12 for twobest
-
- save # 6 for twobest
-
- save #23 for twobest
-
- save #15 for twobest
-
- save #43 for twobest
-
- save #44 for twobest
-
- save #13 for twobest
-
- Coarse startup search net CPU time = 74.5 s
++ Start refinement #1 on 12 coarse parameter sets
- Coarse startup search net CPU time = 74.5 s
-
- Enter alignment setup routine
-
- Smoothing base; radius=2.33
-
- Smoothing source; radius=2.33
- !source mask fill: ubot=0 usiz=267.5
-
- retaining old weight image
-
- using 148147 points from base image [use_all=0]
-
- Exit alignment setup routine
- 134205 total points stored in 1459 ‘RHDD(7.74001)’ bloks
-
- param set #1 has cost=-0.043121
- – Parameters = -9.9694 19.6426 -12.0330 -21.8889 32.6208 31.6563 0.9988 0.9978 0.9972 -0.0006 -0.0013 -0.0008
-
- param set #2 has cost=-0.041630
- – Parameters = -2.5993 10.7355 -1.2286 3.5636 24.5090 18.1499 0.9985 0.9997 1.0160 0.0002 -0.0013 0.0004
-
- param set #3 has cost=-0.051865
- – Parameters = -4.9751 18.2290 -1.2610 -2.9689 28.3231 20.8112 0.9918 0.9936 0.9892 -0.0017 -0.0004 -0.0012
-
- param set #4 has cost=-0.048052
- – Parameters = -14.8404 20.6154 -4.7928 -17.6563 28.9977 24.0110 1.0179 1.0160 0.9798 0.0086 0.0028 0.0085
-
- param set #5 has cost=-0.040196
- – Parameters = -10.0658 12.6635 -4.5300 -25.3428 31.3415 25.2382 0.9942 0.9881 0.9921 0.0004 -0.0001 -0.0010
-
- param set #6 has cost=-0.033409
- – Parameters = 25.1326 45.8854 5.5569 -36.8977 14.0327 -36.2070 1.0020 0.9979 0.9976 0.0003 -0.0002 -0.0017
-
- param set #7 has cost=-0.030861
- – Parameters = -17.5032 2.4168 5.4143 -30.5263 33.5889 -28.2097 1.0183 0.9966 0.9991 0.0013 -0.0018 -0.0031
-
- param set #8 has cost=-0.045340
- – Parameters = -15.7492 -7.7707 23.4522 -13.5180 -30.9793 13.9098 1.0027 1.0045 1.0075 0.0011 -0.0006 -0.0004
-
- param set #9 has cost=-0.042422
- – Parameters = 2.2223 18.7067 -14.2616 22.0933 31.0277 -27.4179 1.0180 0.9973 1.0001 -0.0101 0.0025 -0.0045
-
- param set #10 has cost=-0.046714
- – Parameters = -17.1995 -0.0310 16.9801 -13.6789 -12.6963 13.0761 1.0091 1.0014 0.9985 0.0004 0.0098 0.0004
-
- param set #11 has cost=-0.034054
- – Parameters = -16.1730 10.0147 11.3248 -20.6098 14.6952 14.3819 0.9994 1.0003 0.9994 0.0007 -0.0009 -0.0012
-
- param set #12 has cost=-0.007297
- – Parameters = -6.7736 14.6889 -7.1094 4.8006 -5.0657 -5.9875 1.0062 0.9947 0.9994 -0.0072 -0.0103 0.0053
-
- sorting parameter sets by cost
- – scanning for distances from #1
- — dist(#2,#1) = 0.163
- — dist(#3,#1) = 0.456
- — dist(#4,#1) = 0.659
- — dist(#5,#1) = 0.21
- — dist(#6,#1) = 0.536
- — dist(#7,#1) = 0.0937
- — dist(#8,#1) = 0.249
- — dist(#9,#1) = 0.196
- — dist(#10,#1) = 0.634
- — dist(#11,#1) = 0.545
- — dist(#12,#1) = 0.371
++ Start refinement #2 on 12 coarse parameter sets -
- Enter alignment setup routine
-
- Smoothing base; radius=1.81
-
- Smoothing source; radius=1.81
- !source mask fill: ubot=0 usiz=267.5
-
- retaining old weight image
-
- using 214271 points from base image [use_all=2]
-
- Exit alignment setup routine
- 197716 total points stored in 1578 ‘RHDD(7.59978)’ bloks
-
- param set #1 has cost=-0.033294
- – Parameters = -3.8184 19.0032 -1.8176 -4.0699 27.9744 22.7102 0.9755 1.0045 0.9983 -0.0089 -0.0080 -0.0289
-
- param set #2 has cost=-0.034206
- – Parameters = -12.5026 18.3983 -4.3858 -18.4392 29.8676 23.9771 1.0178 1.0084 0.9788 0.0041 0.0006 0.0121
-
- param set #3 has cost=-0.037990
- – Parameters = -17.7464 -0.0225 17.7982 -13.9703 -12.2185 12.6734 1.0153 1.0076 0.9603 0.0096 0.0144 -0.0020
-
- param set #4 has cost=-0.033718
- – Parameters = -14.1873 -9.3466 23.4292 -12.5474 -31.2266 14.3433 0.9992 1.0115 1.0083 0.0042 -0.0020 0.0034
-
- param set #5 has cost=-0.037089
- – Parameters = -10.0067 19.4104 -10.9290 -21.1368 32.5227 31.0925 1.0011 0.9995 0.9946 -0.0044 -0.0030 0.0055
-
- param set #6 has cost=-0.033284
- – Parameters = 1.5649 18.8423 -15.0626 21.8053 30.2110 -28.0623 1.0216 0.9820 0.9908 -0.0120 -0.0082 0.0096
-
- param set #7 has cost=-0.034455
- – Parameters = -2.7269 10.7341 -1.0181 3.7795 24.2385 17.8604 0.9929 0.9984 1.0187 0.0025 -0.0032 0.0065
-
- param set #8 has cost=-0.030560
- – Parameters = -10.7979 12.1044 -4.5482 -26.5220 33.9561 25.3831 0.9922 0.9856 0.9922 0.0005 -0.0012 -0.0016
-
- param set #9 has cost=-0.035608
- – Parameters = -15.2296 11.9613 11.7811 -20.2914 15.0675 11.8668 0.9128 1.0058 0.8637 -0.0316 0.0120 -0.0443
-
- param set #10 has cost=-0.026236
- – Parameters = 27.5441 45.8854 5.5569 -36.8977 14.0327 -36.2070 1.0020 0.9979 0.9976 0.0003 -0.0002 -0.0017
-
- param set #11 has cost=-0.026951
- – Parameters = -20.4037 1.3127 5.2475 -30.0338 34.2486 -28.8468 1.0106 0.9986 1.0045 -0.0015 -0.0041 -0.0128
-
- param set #12 has cost=-0.016754
- – Parameters = -5.7663 16.2245 -7.0513 10.5477 -6.2983 -6.6435 0.9566 0.9625 0.9881 -0.0186 -0.0300 0.0019
-
- sorting parameter sets by cost
- – scanning for distances from #1
- — dist(#2,#1) = 0.497
- — dist(#3,#1) = 0.303
- — dist(#4,#1) = 0.405
- — dist(#5,#1) = 0.468
- — dist(#6,#1) = 0.211
- — dist(#7,#1) = 0.447
- — dist(#8,#1) = 0.471
- — dist(#9,#1) = 0.513
- — dist(#10,#1) = 0.516
- — dist(#11,#1) = 0.574
- — dist(#12,#1) = 0.311
++ Start refinement #3 on 12 coarse parameter sets -
- Enter alignment setup routine
-
- Smoothing base; radius=1.41
-
- Smoothing source; radius=1.41
- !source mask fill: ubot=0 usiz=267.5
-
- retaining old weight image
-
- using 214271 points from base image [use_all=2]
-
- Exit alignment setup routine
- 200029 total points stored in 1661 ‘RHDD(7.51369)’ bloks
-
- param set #1 has cost=-0.031640
- – Parameters = -17.4781 -1.1867 19.3804 -13.4607 -13.4822 11.6862 1.0165 1.0013 0.9584 0.0058 0.0219 -0.0017
-
- param set #2 has cost=-0.032614
- – Parameters = -9.6189 20.1433 -10.7745 -21.0669 32.1527 31.1101 1.0002 0.9993 1.0005 -0.0045 -0.0025 0.0065
-
- param set #3 has cost=-0.030774
- – Parameters = -14.9719 12.4497 11.9636 -20.6430 15.0753 12.3127 0.9272 1.0046 0.8628 -0.0323 0.0123 -0.0439
-
- param set #4 has cost=-0.027454
- – Parameters = -2.9902 11.1126 -1.3846 3.4951 23.9161 17.5728 0.9903 0.9960 1.0234 0.0058 -0.0026 0.0026
-
- param set #5 has cost=-0.030288
- – Parameters = -10.9560 17.9728 -3.7029 -18.2166 30.4516 24.3528 1.0144 1.0088 0.9684 0.0033 -0.0024 0.0096
-
- param set #6 has cost=-0.030396
- – Parameters = -14.2582 -9.5287 22.8284 -13.3167 -31.1844 13.2422 0.9987 1.0081 1.0047 0.0080 -0.0030 0.0049
-
- param set #7 has cost=-0.025539
- – Parameters = -3.5477 18.5784 -2.0867 -4.4372 28.1996 24.9459 0.9742 1.0035 0.9941 -0.0076 -0.0092 -0.0290
-
- param set #8 has cost=-0.030909
- – Parameters = 1.5149 19.2815 -15.3015 21.4615 30.6321 -27.9194 1.0607 0.9836 1.0197 -0.0168 0.0125 0.0376
-
- param set #9 has cost=-0.023220
- – Parameters = -11.2217 12.3240 -4.3522 -26.9986 33.7929 25.7639 0.9812 0.9869 0.9905 -0.0004 -0.0000 -0.0017
-
- param set #10 has cost=-0.027451
- – Parameters = -19.5986 0.5165 7.0534 -29.7898 35.0601 -30.5226 1.0057 1.0004 1.0004 -0.0010 -0.0047 -0.0143
-
- param set #11 has cost=-0.022302
- – Parameters = 27.5039 46.2583 5.5854 -36.6678 14.0445 -35.9381 1.0093 0.9978 0.9969 0.0007 -0.0005 -0.0014
-
- param set #12 has cost=-0.014453
- – Parameters = -3.5636 16.6200 -6.7698 11.2696 -7.3720 -7.1708 0.9519 0.9504 0.9833 -0.0168 -0.0511 -0.0239
-
- sorting parameter sets by cost
- – scanning for distances from #1
- — dist(#2,#1) = 0.507
- — dist(#3,#1) = 0.656
- — dist(#4,#1) = 0.375
- — dist(#5,#1) = 0.704
- — dist(#6,#1) = 0.0884
- — dist(#7,#1) = 0.273
- — dist(#8,#1) = 0.685
- — dist(#9,#1) = 0.185
- — dist(#10,#1) = 0.0977
- — dist(#11,#1) = 0.745
- — dist(#12,#1) = 0.439
-
- Total coarse refinement net CPU time = 42.5 s; 3151 funcs
++ *** Fine pass begins ***
- Total coarse refinement net CPU time = 42.5 s; 3151 funcs
-
- Enter alignment setup routine
-
- Smoothing base; radius=1.00
-
- Smoothing source; radius=1.00
- !source mask fill: ubot=0 usiz=267.5
-
- retaining old weight image
-
- Exit alignment setup routine
++ Picking best parameter set out of 13 cases
- Exit alignment setup routine
- 204116 total points stored in 1791 ‘RHDD(7.44744)’ bloks
-
- cost(#1)=-0.024551 *
- – Parameters = -9.6189 20.1433 -10.7745 -21.0669 32.1527 31.1101 1.0002 0.9993 1.0005 -0.0045 -0.0025 0.0065
-
- cost(#2)=-0.026364 *
- – Parameters = -17.4781 -1.1867 19.3804 -13.4607 -13.4822 11.6862 1.0165 1.0013 0.9584 0.0058 0.0219 -0.0017
-
- cost(#3)=-0.023197
- – Parameters = 1.5149 19.2815 -15.3015 21.4615 30.6321 -27.9194 1.0607 0.9836 1.0197 -0.0168 0.0125 0.0376
-
- cost(#4)=-0.025450
- – Parameters = -14.9719 12.4497 11.9636 -20.6430 15.0753 12.3127 0.9272 1.0046 0.8628 -0.0323 0.0123 -0.0439
-
- cost(#5)=-0.024396
- – Parameters = -14.2582 -9.5287 22.8284 -13.3167 -31.1844 13.2422 0.9987 1.0081 1.0047 0.0080 -0.0030 0.0049
-
- cost(#6)=-0.023482
- – Parameters = -10.9560 17.9728 -3.7029 -18.2166 30.4516 24.3528 1.0144 1.0088 0.9684 0.0033 -0.0024 0.0096
-
- cost(#7)=-0.024989
- – Parameters = -2.9902 11.1126 -1.3846 3.4951 23.9161 17.5728 0.9903 0.9960 1.0234 0.0058 -0.0026 0.0026
-
- cost(#8)=-0.022376
- – Parameters = -19.5986 0.5165 7.0534 -29.7898 35.0601 -30.5226 1.0057 1.0004 1.0004 -0.0010 -0.0047 -0.0143
-
- cost(#9)=-0.018711
- – Parameters = -3.5477 18.5784 -2.0867 -4.4372 28.1996 24.9459 0.9742 1.0035 0.9941 -0.0076 -0.0092 -0.0290
-
- cost(#10)=-0.016973
- – Parameters = -11.2217 12.3240 -4.3522 -26.9986 33.7929 25.7639 0.9812 0.9869 0.9905 -0.0004 -0.0000 -0.0017
-
- cost(#11)=-0.015961
- – Parameters = 27.5039 46.2583 5.5854 -36.6678 14.0445 -35.9381 1.0093 0.9978 0.9969 0.0007 -0.0005 -0.0014
-
- cost(#12)=-0.009028
- – Parameters = -3.5636 16.6200 -6.7698 11.2696 -7.3720 -7.1708 0.9519 0.9504 0.9833 -0.0168 -0.0511 -0.0239
-
- cost(#13)=0.023259
- – Parameters = -2.4176 15.7199 -8.5441 0.0000 0.0000 0.0000 1.0000 1.0000 1.0000 0.0000 0.0000 0.0000
- -num_rtb 99 ==> refine all 13 cases
-
- cost(#1)=-0.024896 *
- – Parameters = -9.7829 20.0871 -10.7721 -21.2364 32.0925 31.1476 1.0014 0.9984 0.9995 -0.0045 0.0029 0.0070
-
- cost(#2)=-0.030977 *
- – Parameters = -17.7076 -1.4052 19.4125 -13.6137 -14.6140 11.6963 1.0083 1.0206 0.9361 0.0135 0.0675 0.0076
-
- cost(#3)=-0.026791
- – Parameters = 2.1642 19.3772 -15.7187 21.4927 31.1107 -27.5342 1.0991 0.9746 1.0169 -0.0087 0.0085 0.0325
-
- cost(#4)=-0.028027
- – Parameters = -15.7257 12.6563 12.6770 -19.9074 15.5180 13.2991 0.9345 1.0069 0.8630 -0.0325 0.0112 -0.0448
-
- cost(#5)=-0.028490
- – Parameters = -15.1690 -10.4450 22.7957 -14.1746 -31.0491 13.6641 1.0050 1.0107 1.0079 0.0308 -0.0038 0.0054
-
- cost(#6)=-0.026533
- – Parameters = -11.1656 18.4155 -2.7680 -20.0012 30.6984 25.1680 1.0111 1.0062 0.9707 0.0022 -0.0011 0.0106
-
- cost(#7)=-0.028392
- – Parameters = -3.8049 10.6419 -2.6452 3.0703 23.4652 17.5073 0.9908 0.9956 1.0249 0.0054 -0.0029 0.0022
-
- cost(#8)=-0.025849
- – Parameters = -19.3958 0.1670 7.2500 -29.5120 35.6962 -30.7729 1.0270 1.0067 0.9973 0.0028 -0.0075 -0.0136
-
- cost(#9)=-0.023990
- – Parameters = -1.3474 15.6812 -1.7815 -4.2001 28.6886 24.9509 0.9893 1.0032 0.9915 -0.0059 0.0034 -0.0280
-
- cost(#10)=-0.020305
- – Parameters = -11.5921 10.7530 -4.3842 -28.1283 33.3356 27.9673 0.9657 0.9730 0.9894 0.0045 -0.0054 0.0036
-
- cost(#11)=-0.026108
- – Parameters = 31.6014 49.9067 2.1083 -34.4208 13.1960 -36.4570 0.9953 0.9988 0.9756 -0.0087 0.0065 -0.0040
-
- cost(#12)=-0.011078
- – Parameters = -3.1561 16.7127 -5.6458 11.5729 -7.3110 -7.6079 0.9521 0.9517 0.9781 -0.0165 -0.0519 -0.0252
-
- cost(#13)=-0.007627
- – Parameters = -3.9526 19.2341 -6.1913 9.7916 -8.7999 -3.0997 0.9931 1.0095 1.0204 -0.0014 -0.0128 -0.0269
-
- case #2 is now the best
-
- Initial cost = -0.030977
-
- Initial fine Parameters = -17.7076 -1.4052 19.4125 -13.6137 -14.6140 11.6963 1.0083 1.0206 0.9361 0.0135 0.0675 0.0076
-
- Finalish cost = -0.032912 ; 820 funcs
-
- Final cost = -0.032923 ; 412 funcs
- Final fine fit Parameters:
x-shift=-17.4384 y-shift= -0.5699 z-shift= 19.8901 … enorm= 26.4583 mm
z-angle=-13.5306 x-angle=-14.3576 y-angle= 9.9125 … total= 22.7851 deg
x-scale= 0.9993 y-scale= 1.0308 z-scale= 0.9282 … vol3D= 0.9562 = base bigger than source
y/x-shear= 0.0053 z/x-shear= 0.0923 z/y-shear= 0.0146 -
- Fine net CPU time = 35.7 s
++ Computing output image
++ image warp: parameters = -17.4384 -0.5699 19.8901 -13.5306 -14.3576 9.9125 0.9993 1.0308 0.9282 0.0053 0.0923 0.0146
++ Output dataset ./__tt_P05_2_t1_al+orig.BRIK
++ Wrote -1Dmatrix_save ./P05_2_t1_al_mat.aff12.1D
++ 3dAllineate: total CPU time = 157.2 sec Elapsed = 60.8
++ ###########################################################
++ # Please check results visually for alignment quality #
++ ###########################################################
++ # ‘-autoweight’ is recommended when using -lpc or -lpa #
++ # If your results are not good, please try again. #
++ ###########################################################
#++ Applying alignment for epi to anat
#++ Inverting anat to epi matrix
#Script is running (command trimmed):
cat_matvec -ONELINE ./P05_2_t1_al_mat.aff12.1D -I > ./P05_vwfa_1_al_mat.aff12.1D
#++ Concatenating volreg and epi to anat transformations
#Script is running (command trimmed):
cat_matvec -ONELINE ./P05_2_t1_al_mat.aff12.1D -I ./__tt_P05_vwfa_1_tsh_vr_mat.aff12.1D > ./P05_vwfa_1_al_reg_mat.aff12.1D
#++ Applying transformation of epi to anat
#Script is running (command trimmed):
3dAllineate -base ./P05_2_t1+orig -1Dmatrix_apply ./P05_vwfa_1_al_reg_mat.aff12.1D -prefix ././P05_vwfa_1_al -input ./__tt_P05_vwfa_1_tsh+orig -master BASE -mast_dxyz 1.500000 -weight_frac 1.0 -maxrot 6 -maxshf 10 -VERB -warp aff -source_automask+4 -twobest 11 -twopass -VERB -maxrot 45 -maxshf 40 -fineblur 1 -source_automask+2
++ 3dAllineate: AFNI version=AFNI_19.1.19 (Jun 13 2019) [64-bit]
++ Authored by: Zhark the Registrator
++ Source dataset: ./__tt_P05_vwfa_1_tsh+orig.HEAD
++ Base dataset: ./P05_2_t1+orig.HEAD
++ Loading datasets
++ NOTE: base and source coordinate systems have different handedness
- Fine net CPU time = 35.7 s
-
Orientations: base=Left handed (ASR); source=Right handed (RAI)
-
- It is nothing to worry about: 3dAllineate aligns based on coordinates.
-
- But it is always important to check the alignment visually to be sure.
- Range param#4 [z-angle] = -6.000000 … 6.000000
- Range param#5 [x-angle] = -6.000000 … 6.000000
- Range param#6 [y-angle] = -6.000000 … 6.000000
- Range param#1 [x-shift] = -10.000000 … 10.000000
- Range param#2 [y-shift] = -10.000000 … 10.000000
- Range param#3 [z-shift] = -10.000000 … 10.000000
- Range param#4 [z-angle] = -45.000000 … 45.000000
- Range param#5 [x-angle] = -45.000000 … 45.000000
- Range param#6 [y-angle] = -45.000000 … 45.000000
- Range param#1 [x-shift] = -40.000000 … 40.000000
- Range param#2 [y-shift] = -40.000000 … 40.000000
- Range param#3 [z-shift] = -40.000000 … 40.000000
++ changing output grid spacing to 1.5000 mm
++ OpenMP thread count = 4
++ ========== Applying transformation to 113 sub-bricks ==========
++ ========== sub-brick #0 ========== [total CPU to here=0.0 s] -
- Enter alignment setup routine
-
- copying base image
-
- copying source image
-
- no weight image
-
- using 11 points from base image [use_all=0]
-
- Exit alignment setup routine
++ using -1Dmatrix_apply
++ Computing output image
++ image warp: parameters = 0.2164 -0.2419 0.9249 11.3952 0.9107 -0.2125 -0.2966 17.1153 -0.1278 -0.5140 -0.1302 76.0942
++ ========== sub-brick #1 ========== [total CPU to here=0.4 s]
- Exit alignment setup routine
-
- Enter alignment setup routine
-
- copying source image
-
- no weight image
-
- Exit alignment setup routine
++ using -1Dmatrix_apply
++ Computing output image
++ image warp: parameters = 0.2164 -0.2418 0.9249 11.3890 0.9107 -0.2123 -0.2966 17.0952 -0.1277 -0.5141 -0.1302 76.0968
…
++ ========== sub-brick #112 ========== [total CPU to here=34.9 s]
- Exit alignment setup routine
-
- Enter alignment setup routine
-
- copying source image
-
- no weight image
-
- Exit alignment setup routine
++ using -1Dmatrix_apply
++ Computing output image
++ image warp: parameters = 0.2163 -0.2416 0.9250 11.3641 0.9106 -0.2130 -0.2966 17.3162 -0.1280 -0.5140 -0.1300 76.1327
++ Output dataset ././P05_vwfa_1_al+orig.BRIK
++ 3dAllineate: total CPU time = 36.1 sec Elapsed = 29.1
++ ###########################################################
#++ Creating final output: epi data aligned to anat
- Exit alignment setup routine
copy is not necessary - both paths are same
#++ Saving history
#Script is running (command trimmed):
3dNotes -h “align_epi_anat.py -anat P05_2_t1+orig -epi P05_vwfa_1+orig
-epi_base 0 -giant_move -epi2anat”
./P05_vwfa_1_al+orig
#++ Removing all the temporary files
#Script is running:
\rm -f ./__tt_P05_vwfa_1*
#Script is running:
\rm -f ./__tt_P05_2_t1*
Finished alignment successfully
Do you have any suggestion to fix the problem?
Thank you!
-Joy