Alignment and Atlases

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-22-Atlas - segmentation info.ATLAS DEFINITIONSExamples: TTatlas+tlrc, TT_N27_EZ_ML+tlrc, roidset+orig.TT_N27_EZ_ML+tlrc
-23-Registration To St<strong>and</strong>ard SpacesTransforming Datasets to Talairach-Tournoux Coordinates• The original purpose of AFNI (circa 1994 A.D.) was to perform the transformation ofdatasets to Talairach-Tournoux (stereotaxic) coordinates• The transformation can be manual, or automatic• In manual mode, you must mark various anatomical locations, defined in Jean Talairach <strong>and</strong> Pierre Tournoux “Co-Planar Stereotaxic Atlas of the Human Brain” Thieme Medical Publishers, New York, 1988 Marking is best done on a high-resolution T1-weighted structural MRI volume• In automatic mode, you need to choose a template to which your data arealigned. Different templates are made available with AFNIʼs distribution. You canalso use your own templates.• Transformation carries over to all other (follower) datasets in the same directory This is where the importance of getting the relative spatial placement of datasetsdone correctly in to3d really matters You can then write follower datasets, typically functional or EPI timeseries, to diskin Talairach coordinates➥ Purpose: voxel-wise comparison with other subjects➥ May want to blur volumes a little before comparisons, to allow for residualanatomic variability: AFNI programs 3dmerge or 3dBlurToFWHM
Page 4 and 5: -4-Alignment tools in AFNI (continu
Page 6 and 7: -6-• Most image registration meth
Page 8 and 9: -8-• AFNI program 3dvolreg is for
Page 11 and 12: -11-Motion correction - caveats•
Page 13 and 14: -13-align_epi_anat.py• Goal: Want
Page 15 and 16: -15-Alignment Visualization in AFNI
Page 17 and 18: -17-Rat BrainsAlignmentof12hourMang
Page 19 and 20: -19-ATLASES
Page 21: -21-ATLAS DEFINITIONSTemplate Space
Page 25 and 26: -25-• Hidden in GUI - right click
Page 27 and 28: -27-• Stage 2: Scaling to Talaira
Page 29 and 30: -29-Automatic Talairach Transformat
Page 31 and 32: -31-Templates included with AFNITT_
Page 33 and 34: -33-Applying a transform to followe
Page 35 and 36: -35-@auto_tlrc Results are Comparab
Page 37 and 38: -37-Atlas/Template Spaces Differ In
Page 39 and 40: -39-From Space To SpaceTLRCMNIMNI-A
Page 41 and 42: -41-Atlases Distributed With AFNITT
Page 43 and 44: -43-Atlases Distributed With AFNI:A
Page 45 and 46: -45-[Where am I?]Shows you where yo
Page 47 and 48: -47-• whereami can report on the
Page 49 and 50: -49-The Future …• New atlases -
Page 51 and 52: -51-Neighborhoods in Space -Transfo
Page 53 and 54: -53-• Histogram cartoons:IIIJJJ
Page 55 and 56: -55-• grayscale underlay = J(x) =
Page 57 and 58: -57-• The future for 3dAllineate:
Page 59 and 60: -59-Appendix AInter-subject, inter-
Page 61 and 62: -61-• Anatomical coverage differs
Page 63 and 64: -63- Solutions to these problems:
Page 65 and 66: -65- Recipe above does not address
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Page 69 and 70: -69-Algorithmic Features• Goal is
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-72-Mutual Information• Entropy o
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-74-Hellinger Metric• MI can be t
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-76-3dAllineateʼs Symmetrical CR
Page 78 and 79:
-78-Test: Monkey EPI - Anat6 DOFHEL
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-80-Test: Monkey EPI - Anat11 DOFHE
Page 82 and 83:
-82-• Listen up folks, IMPORTANT
Page 84 and 85:
-84- First goal is to mark top midd
Page 86 and 87:
-86-• AC-PC Markers Cheat Sheet T
Page 88 and 89:
-88-Notes on positioning AC/PC mark
Page 90 and 91:
-90-• Scaling to Talairach-Tourno
Page 92 and 93:
-92-Using the same methods as befor
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-94-• Automatic creation of “fo
Page 96 and 97:
-96-“Warp on demand” viewing of
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-98-• Class example - Writing ana
Page 100 and 101:
-100- Examination of time series fr
Page 102 and 103:
-102-Real-Time 3D Image Registratio
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-104-• Screen capture fromexample
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