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Diffusion Tensor Imaging:  from Dicom to Nrrd Sonia Pujol, Ph.D. Randy Gollub, M.D., Ph.D. National Alliance for Medical Image Computing
Acknowledgments ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Goal of the Tutorial Training on how to convert DICOM DWI data to the Nrrd File format,  compatible with Slicer visualization and analysis Raw Data Raw Data Raw Data Nrrd Header Dicom Header Dicom Header Dicom Header Dicom Header Raw Data
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object]
Diffusion Weighted Imaging The signal is dimmer when the direction of the applied gradient is parallel to the principal direction of diffusion. Diffusion Sensitizing Gradients  Diffusion Weighted  Images
Diffusion Weighted Imaging (DWI) Example: Correlation between the orientation of the 11 th  gradient and the signal intensity in the Splenium of the Corpus Callosum
Diffusion Weighted Imaging (Stejskal and Tanner 1965, Basser 1994 ) {Si} represent the signal intensities in presence of the diffusion sensitizing gradients  gi  b  is the diffusion weighted parameter Diffusion Weighted  Images
Background ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Which image is correct ?
Which image is correct ?
The left one is correct
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object]
Nearly Raw Raster Data (Nrrd) ,[object Object],[object Object],Nrrd Header + Raw Data Raw Data Raw Data
Nrrd file format ,[object Object]
Nrrd file format ,[object Object]
Coordinate Frames Diffusion Weighted  Images Diffusion Sensitizing Gradients  Courtesy G.Kindlmann Courtesy G.Kindlmann (X,Y,Z) (I,J,K)
Coordinate Frames DWI Image Orientation (I,J,K)  Diffusion Sensitizing Gradients  (X,Y,Z) Patient Space Courtesy G.Kindlmann (X,Y,Z) (I,J,K)
Transformation matrices ,[object Object],(X,Y,Z) (I,J,K) T: XYZ  RAS (R,A,S) Courtesy G.Kindlmann
Nrrd Terminology ,[object Object],(X,Y,Z) (I,J,K) (R,A,S) T: IJK  RAS Courtesy G.Kindlmann
Nrrd requirements for DWI data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Nrrd requirements for DWI data ,[object Object],[object Object],[object Object],[object Object],[object Object]
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object]
Generating Nrrd Files ,[object Object],[object Object],[object Object],[object Object]
Unu syntax ,[object Object],[object Object],[object Object],[object Object],[object Object]
Unu syntax: ‘make’ command ,[object Object],[object Object],[object Object],[object Object]
Running unu on Windows ,[object Object],[object Object],[object Object],[object Object],[object Object]
Running unu on Mac/Linux/Solaris   ,[object Object],[object Object],[object Object],[object Object]
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object]
DICOM DWI Training Data ,[object Object],[object Object]
DWI Training Data Type the command  cd  and enter the path to your data in the Tk Console. Type  ls  to list all the data files.
DWI Training Data The dataset is composed of 504 images named S4.xxx
Unu command (Windows) unu make -h --input S4.%03d 1 504 1 2 --encoding raw  --byteskip -1   Type the unu command with the  input ,  encoding  and  byteskip  fields  Min index Max index Increment 2D Image Read backwards from end of file Do not hit Enter
Unu command (Mac/Linux) unu make -h --input S4.%03d 1 504 1 2 --encoding raw  --byteskip -1   Type the unu command with the  input ,  encoding  and  byteskip  fields  Min index Max index Increment 2D Image Read backwards from end of file slicer2.6-opt-darwin-ppc-2006-05-18/Lib/darwin-ppc/teem-build/bin
Numbers as file naming convention  (*) ,[object Object],[object Object],[object Object],[object Object],(*) Background information  unu make -h --input S4.%03d 1 504 1 2 --encoding raw  --byteskip -1
Read the DICOM Header Click on  AddVolume
Read the DICOM Header Select the Properties  Dicom The Props  panel appears.
Read the DICOM Header Click on  Select Dicom Volume  and browse to load the dataset located in the directory  dwi-dicom The Dicom Props  panel appears.
Read the Dicom Header Slicer displays the list of Dicom files in the directory. Click on  OK
Read the Dicom Header Click on  Extract Header  to display the content of the Dicom Header.
Read the Dicom Header Slicer displays the content of the  Dicom Header . This information will be used to generate the  Nrrd header .
Extracting the volume characteristics ,[object Object],[object Object],[object Object],[object Object]
Extracting the volume characteristics - Data Type:  Short - Endianess:  Little
Unu Command Add the fields  endian  and  type  to the unu command  --endian little --type short
Extracting the volume characteristics The dataset was acquired with Nb=2 Baselines and Ng=12 Gradients  Image Dimensions: 256 pixels x 256 pixels
DICOM DWI Training Data ,[object Object],[object Object]
Extracting the volume characteristics The dataset was acquired with Nb=2 Baselines and Ng=12 Gradients  ,[object Object],[object Object],Image Dimensions: 256 pixels x 256 pixels
Unu Command  --size 256 256 36 14 --centering cell cell cell none Medical images are cell-centered samples Add the fields  size  and  centering  to the unu command
Slice Thickness  Extract the slice thickness from the Dicom header
Slice Thickness  slice thickness = 3.00 mm
Slice Thickness ,[object Object],Add the  field  thickness to the unu command
Building the transformation matrices ,[object Object],[object Object],[object Object],DICOM: LPS SLICER: RAS
Space Directions ,[object Object],--space  right-anterior-superior
Space Directions Extract the pixel size from the Dicom Header.
Space Directions Pixel size = 0.9375 mm x  0.9375 mm The dataset was acquired with Superior-Inferior slice ordering
Space Directions --directions  “ ( - 0.9375,0,0) (0, - 0.9375,0) (0,0,-3) none “ Add the fields  directions  and  unit  to the unu command DICOM: LPS SLICER: RAS
Space Origin Courtesy G.Kindlmann The space origin is the position of the first pixel in the first image.  This information is contained in the Dicom Header of the first slice.
Space Origin The space origin information is located in the Dicom header  [ 0020,0032, Image Position Patient ]  Courtesy G.Kindlmann
Space Origin ,[object Object],[object Object],[object Object],[object Object],Click on  Cancel  to come back to the Main menu
Space Origin Click  Add Volume select the tab  Props,  and the format  DICOM
Space Origin Click on  Select DICOM Volume Select the directory / FirstSlice containing the first slice
Space Origin Click on  List Headers  to display the content of the header of the first image.
Space Origin Slicer displays the content of the header of the first image.
Space Origin Scroll down to display the value of the tag  [0020,0032, Image Position Patient ]
Space Origin [0020,0032, Image Position Patient ]   = -125.0, -124.09, 79.30
Space Origin Click on OK to close the  Dicom Header Window
Space Origin --origin "( + 125.0, + 124.10,79.30)" Add the field  origin  to the unu command DICOM: LPS SLICER: RAS
Measurement Frame
Measurement Frame
Measurement Frame ,[object Object],Add the field  measurement frame  to the unu command
Axis Ordering Courtesy G.Kindlmann
Axis Ordering ,[object Object],Add the field  kinds  to the unu command Axis Ordering: columns, rows, slices, intensity values
Output File Add the field  output  to the unu command --output  myNrrdDWI.nhdr
Output File Type ls in the  Tk Console The file myNrrdDWI.nhdr is listed in the directory
Acquisition parameters Open the file  MyNrrdDWI.nhdr  with a text Editor
Acquisition parameters Open a web browser at the location  http://www.na-mic.org/Wiki/index.php/Dartmouth-DWI-parameters
Acquisition parameters Copy the acquisition parameters from this wiki page to the end of the file  MyNrrdDWI.nhdr, hit Enter  and save the resulting file
Result Final result of the tutorial: Nrrd header for the DWI training dataset
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object]
Loading the Nrrd Volume Click on  Cancel  to come back to the Main Menu
Loading the Nrrd Volume Click on  Add Volume  to load the DWI training dataset using the Nrrd header
Loading the Nrrd Volume Select  Nrrd Reader  in the  Properties  field The  Props Panel  of the module Volumes appears.
Loading the Nrrd Volume Click on  Apply Check that the path to the file  myNrrdDWI.nhdr  is correct. If needed, manually enter it Browse to load the file  myNrrdDWI.nhdr
Loading the Nrrd Volume Slicer loads the Nrrd DWI  dataset  Left-click on  Or  and change the orientation to  Slices
Loading the Nrrd Volume Change the  FOV  to 2000
Loading the Nrrd Volume The sagittal and coronal viewers display the 14 DWI volumes: 2 baselines and 12 gradients
Loading the Nrrd Volume Display the axial and sagittal slices inside the viewer. Use the axial slider to observe the baselines and gradient volumes.
Converting the DWI data to tensors Select the module DTMRI and click on the tab  Conv Select the Input volume  myNrrdDWI.nhdr  and click on  ConvertVolume
Converting the DWI data to tensors Slicer displays the anatomical views of the  Average Gradient  volume.
Glyphs Select the panel  Glyphs  in the DTMRI module Select the Active DTMRI volume  myNrrdDWI-nhdr_Tensor Select  Glyphs on Slice  for the axial (red) view Set  Display Glyphs On
Glyphs Orientation of the glyphs in the Corpus Callosum
Conclusion ,[object Object],[object Object],[object Object]

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ITK Tutorial Presentation Slides-946ITK Tutorial Presentation Slides-946
ITK Tutorial Presentation Slides-946
 
ITK Tutorial Presentation Slides-945
ITK Tutorial Presentation Slides-945ITK Tutorial Presentation Slides-945
ITK Tutorial Presentation Slides-945
 
ITK Tutorial Presentation Slides-944
ITK Tutorial Presentation Slides-944ITK Tutorial Presentation Slides-944
ITK Tutorial Presentation Slides-944
 

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Nrrd to Dicom Conversion-3769

  • 1. Diffusion Tensor Imaging: from Dicom to Nrrd Sonia Pujol, Ph.D. Randy Gollub, M.D., Ph.D. National Alliance for Medical Image Computing
  • 2.
  • 3. Goal of the Tutorial Training on how to convert DICOM DWI data to the Nrrd File format, compatible with Slicer visualization and analysis Raw Data Raw Data Raw Data Nrrd Header Dicom Header Dicom Header Dicom Header Dicom Header Raw Data
  • 4.
  • 5. Diffusion Weighted Imaging The signal is dimmer when the direction of the applied gradient is parallel to the principal direction of diffusion. Diffusion Sensitizing Gradients Diffusion Weighted Images
  • 6. Diffusion Weighted Imaging (DWI) Example: Correlation between the orientation of the 11 th gradient and the signal intensity in the Splenium of the Corpus Callosum
  • 7. Diffusion Weighted Imaging (Stejskal and Tanner 1965, Basser 1994 ) {Si} represent the signal intensities in presence of the diffusion sensitizing gradients gi b is the diffusion weighted parameter Diffusion Weighted Images
  • 8.
  • 9. Which image is correct ?
  • 10. Which image is correct ?
  • 11. The left one is correct
  • 12.
  • 13.
  • 14.
  • 15.
  • 16. Coordinate Frames Diffusion Weighted Images Diffusion Sensitizing Gradients Courtesy G.Kindlmann Courtesy G.Kindlmann (X,Y,Z) (I,J,K)
  • 17. Coordinate Frames DWI Image Orientation (I,J,K) Diffusion Sensitizing Gradients (X,Y,Z) Patient Space Courtesy G.Kindlmann (X,Y,Z) (I,J,K)
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30. DWI Training Data Type the command cd and enter the path to your data in the Tk Console. Type ls to list all the data files.
  • 31. DWI Training Data The dataset is composed of 504 images named S4.xxx
  • 32. Unu command (Windows) unu make -h --input S4.%03d 1 504 1 2 --encoding raw --byteskip -1 Type the unu command with the input , encoding and byteskip fields Min index Max index Increment 2D Image Read backwards from end of file Do not hit Enter
  • 33. Unu command (Mac/Linux) unu make -h --input S4.%03d 1 504 1 2 --encoding raw --byteskip -1 Type the unu command with the input , encoding and byteskip fields Min index Max index Increment 2D Image Read backwards from end of file slicer2.6-opt-darwin-ppc-2006-05-18/Lib/darwin-ppc/teem-build/bin
  • 34.
  • 35. Read the DICOM Header Click on AddVolume
  • 36. Read the DICOM Header Select the Properties Dicom The Props panel appears.
  • 37. Read the DICOM Header Click on Select Dicom Volume and browse to load the dataset located in the directory dwi-dicom The Dicom Props panel appears.
  • 38. Read the Dicom Header Slicer displays the list of Dicom files in the directory. Click on OK
  • 39. Read the Dicom Header Click on Extract Header to display the content of the Dicom Header.
  • 40. Read the Dicom Header Slicer displays the content of the Dicom Header . This information will be used to generate the Nrrd header .
  • 41.
  • 42. Extracting the volume characteristics - Data Type: Short - Endianess: Little
  • 43. Unu Command Add the fields endian and type to the unu command --endian little --type short
  • 44. Extracting the volume characteristics The dataset was acquired with Nb=2 Baselines and Ng=12 Gradients Image Dimensions: 256 pixels x 256 pixels
  • 45.
  • 46.
  • 47. Unu Command --size 256 256 36 14 --centering cell cell cell none Medical images are cell-centered samples Add the fields size and centering to the unu command
  • 48. Slice Thickness Extract the slice thickness from the Dicom header
  • 49. Slice Thickness slice thickness = 3.00 mm
  • 50.
  • 51.
  • 52.
  • 53. Space Directions Extract the pixel size from the Dicom Header.
  • 54. Space Directions Pixel size = 0.9375 mm x 0.9375 mm The dataset was acquired with Superior-Inferior slice ordering
  • 55. Space Directions --directions “ ( - 0.9375,0,0) (0, - 0.9375,0) (0,0,-3) none “ Add the fields directions and unit to the unu command DICOM: LPS SLICER: RAS
  • 56. Space Origin Courtesy G.Kindlmann The space origin is the position of the first pixel in the first image. This information is contained in the Dicom Header of the first slice.
  • 57. Space Origin The space origin information is located in the Dicom header [ 0020,0032, Image Position Patient ] Courtesy G.Kindlmann
  • 58.
  • 59. Space Origin Click Add Volume select the tab Props, and the format DICOM
  • 60. Space Origin Click on Select DICOM Volume Select the directory / FirstSlice containing the first slice
  • 61. Space Origin Click on List Headers to display the content of the header of the first image.
  • 62. Space Origin Slicer displays the content of the header of the first image.
  • 63. Space Origin Scroll down to display the value of the tag [0020,0032, Image Position Patient ]
  • 64. Space Origin [0020,0032, Image Position Patient ] = -125.0, -124.09, 79.30
  • 65. Space Origin Click on OK to close the Dicom Header Window
  • 66. Space Origin --origin "( + 125.0, + 124.10,79.30)" Add the field origin to the unu command DICOM: LPS SLICER: RAS
  • 69.
  • 70. Axis Ordering Courtesy G.Kindlmann
  • 71.
  • 72. Output File Add the field output to the unu command --output myNrrdDWI.nhdr
  • 73. Output File Type ls in the Tk Console The file myNrrdDWI.nhdr is listed in the directory
  • 74. Acquisition parameters Open the file MyNrrdDWI.nhdr with a text Editor
  • 75. Acquisition parameters Open a web browser at the location http://www.na-mic.org/Wiki/index.php/Dartmouth-DWI-parameters
  • 76. Acquisition parameters Copy the acquisition parameters from this wiki page to the end of the file MyNrrdDWI.nhdr, hit Enter and save the resulting file
  • 77. Result Final result of the tutorial: Nrrd header for the DWI training dataset
  • 78.
  • 79. Loading the Nrrd Volume Click on Cancel to come back to the Main Menu
  • 80. Loading the Nrrd Volume Click on Add Volume to load the DWI training dataset using the Nrrd header
  • 81. Loading the Nrrd Volume Select Nrrd Reader in the Properties field The Props Panel of the module Volumes appears.
  • 82. Loading the Nrrd Volume Click on Apply Check that the path to the file myNrrdDWI.nhdr is correct. If needed, manually enter it Browse to load the file myNrrdDWI.nhdr
  • 83. Loading the Nrrd Volume Slicer loads the Nrrd DWI dataset Left-click on Or and change the orientation to Slices
  • 84. Loading the Nrrd Volume Change the FOV to 2000
  • 85. Loading the Nrrd Volume The sagittal and coronal viewers display the 14 DWI volumes: 2 baselines and 12 gradients
  • 86. Loading the Nrrd Volume Display the axial and sagittal slices inside the viewer. Use the axial slider to observe the baselines and gradient volumes.
  • 87. Converting the DWI data to tensors Select the module DTMRI and click on the tab Conv Select the Input volume myNrrdDWI.nhdr and click on ConvertVolume
  • 88. Converting the DWI data to tensors Slicer displays the anatomical views of the Average Gradient volume.
  • 89. Glyphs Select the panel Glyphs in the DTMRI module Select the Active DTMRI volume myNrrdDWI-nhdr_Tensor Select Glyphs on Slice for the axial (red) view Set Display Glyphs On
  • 90. Glyphs Orientation of the glyphs in the Corpus Callosum
  • 91.