Representing and ProcessingScientific Data with Teem:

Applications to CT and DT-MRI

Gordon Kindlmann

Representing and ProcessingScientific Data with Teem:

Applications to CT and DT-MRI

Gordon Kindlmann Scientific Computing and Imaging Institute, School of Computing, University of Utah

The software: teem includes nrrd and ten

Application 1: Inspecting CT data

Application 2: Estimating and inspecting diffusion tensors

Outline

Teem overview

• Software I write to do research• Data processing, scientific visualization

• Open-source: http://teem.sourceforge.net• Get pre-compiled binaries, reproduce results

• Collection of ANSI C libraries• Function calls do what you need• Pseudo-object-oriented: nrrdNew(), nrrdCopy(), nrrdSlice(), nrrdNuke()

• Uncommon library names• Multiple interfaces

• Command-line: Repeatability, reproducability• API BioTensor GUI

Teem libraries

Basic utility functions + macros

Command-line parsing

Nearly Raw Raster Data: representing (in memory, on disk) and manipulating N-dimensional arrays

Filtered point sampling: interpolating scalar, vector, tensor fields; used for fiber tracking in Bio-Tensor

All things diffusion tensor

Two libraries, two utilities

Underlying library

nrrd

ten

Command-line utility

unu

tendalso BioPSE module name prefix

nrrd: what it is

• Nearly Raw Raster Data• No abstractions, no cleverness

• Representation and manipulation of N-dimensional raster data:• File format• Data structure• Large set of operations

• Used by SCIRun, ITK, others• Format and API are stable

nrrd: why it is• Information in scientific computation and

visualization often N-dimensional array:• 1D: histograms, time-varying scalar• 2D: gray-scale images, scatterplots,

collection of signals• 3D: color images, CT/MRI volumes• 4D: vector/tensor volumes, time-

varying scalar fields• 5D: time-varying vector/tensor fields

• Common format and tools for all of these

nrrd: capabilities• Subset, superset

• Cropping, slicing, padding, joining• Arithmetic

• Adding, multiplying• Converting

• Quantization, changing type• Rearranging

• Permute axes, flipping+shuffling slices, bricking (w/out overlap)

• Filtering• Up+downsampling, blurring, median

• Measuring: projection along an axis• Min, max, mean, L2, variance, etc.

• Histograms of all sorts

dimensiongenerality

Getting data in: NRRD file format

NRRD0001content: a dozen imagestype: floatdimension: 4sizes: 3 640 480 12encoding: raw endian: big

<raw data>• Detached headers possible:

• “data file”, “line skip”, “byte skip”• Different encodings possible:

• Raw, ASCII, gzip-compressed, hex• Can be generated by hand …

ASCII headerfor arraydescription

Blank line Data

Getting data in: “unu make”• unu make -i <data file> \

-s 3 640 480 12 -t float \ -e raw -en big -o images.nrrdNRRD0001type: floatdimension: 4sizes: 3 640 480 12encoding: raw endian: big…

• unu make -h -i <data file> \ -s 3 640 480 12 -t float \ -e raw -en big -o images.nhdr

• Pretty? No. Effective? Yes.

Concept: raster orderingN-D raster data has “raster ordering”(0, 0, 0)(1, 0, 0)(2, 0, 0)(0, 1, 0)(1, 1, 0)(2, 1, 0) …(0, 539, 0)(1, 539, 0)(2, 539, 0)(0, 0, 1)…(0, 0, 2)…(0, 539, 379)(1, 539, 379)(2, 539, 379)

linea

r or

derin

g in

mem

ory 2-D color image

3-D array color:X:Y

Each coordinate is an axisAxes ordered: fast to slow;contiguous to distant

Fast: colorMedium: XSlow: Y

color

color X

Y

The software: teem includes nrrd and ten

Application 1: Inspecting CT data

Application 2: Estimating and inspecting diffusion tensors

Outline

To inspect one slice:zcat c_vf1700.fre.Z \| unu make -t ushort -e raw \

-en big -s 512 512 -bs 3416 \| unu quantize -b 8 \| unu save -f pnm | xv -

(demo of this, and command list)

cp data/c_vf16{4,5,6,7,8,9}?.fre.Z .cp data/c_vf17??.fre.Z .cp data/c_vf18{0,1,2}?.fre.Z .uncompress *.Zunu make -i *.fre -t ushort -e raw -en big \

-s 512 512 190 -bs 3416 \ -sp 0.9375 0.9375 1.0 \| unu crop -min 324 84 0 -max 499 270 M \

-o vfrhand.nhdrrm -rf *.fre

Unu application: Visible human CT

(link)

Unu application: Visible human CT

The software: teem includes nrrd and ten

Application 1: Inspecting CT data

Application 2: Estimating and inspecting diffusion tensors

Outline

Diffusion in Biological Tissue

Motion of water through tissue

Faster in some directions than others

Kleenex newspaper

• Anisotropy: diffusion rate depends on direction

isotropic anisotropic

Diffusion in 3D

isotropic

anisotropic

linear

planar

spherical• Diffusion Tensor: 3x3 semi-positive definite symmetric matrix

• Ellipsoids represent variety of possible diffusion patterns

• Ellipsoid is image of unit sphere under diffusion tensor matrix

Diffusion MRI of brain• Anisotropy high along white matter fiber tracts

2.1 -0.1 -0.2

-0.1 2.0 -0.0

-0.2 -0.0 2.1

3.7 0.3 -0.8

0.3 0.6 -0.1

-0.8 -0.1 0.8

1.7 0.1 -0.1

0.1 2.3 -0.3

-0.1 -0.3 0.3

Dxx

Dxy

Dxz

Dxy Dxz

DyzDyy

DzzDyz

Inspecting DWI slices

unu make -i 02728/003/I.050 -t ushort \

-e raw -en big -s 256 256 -bs -1 \

| unu quantize -b 8 -o slice.png

Inspecting multiple DWI slicesunu make -i 003/I.0{5,6,7}? -t ushort \ -e raw -en big -s 256 256 20 -bs -1 \ | unu axsplit -a 2 -s 5 4 \ | unu permute -p 0 2 1 3 \ | unu axmerge -a 0 | unu axmerge -a 1 \ | unu quantize -b 8 -o mosaic.png

Making DWI volumes

foreach IMG ( 003 004 005 006 007 \ 008 009 010 011 012 013 \ 014 015 016 017 018 019 \ 020 021 022 )

unu make -i 02728/$IMG/I.* -bs -1 \ -t ushort -e raw -en big \ -s 256 256 136 -sp 0.39 0.39 1.00 \ -o dwi/$IMG.nrrd

unu resample -I dwi/$IMG.nrrd \ -s 100 100 = -k hann:8 \ -o dwi/$IMG-small.nrrdend

Meddling with B-matrix list

Brr Brp Brs 0 3 5 0 1 2

. Bpp Bps 1 4 3 4

. . Bss 2 5

105.92862 0.00003 33746.47739 0.04822 -1.07717 -1484.482576868.88229 1.60591 31849.32096 -104.73389 226.02398 -14714.5424116140.57145 21695.45998 1893.56463 -18690.76297 -6346.26912 5492.96913...

unu shuffle -i B-orig.txt \ -a 0 -p 0 3 5 1 4 2 -o B.txt

Estimating diffusion tensors

tend estim -i dwi/0??.nrrd -knownB0 false \ -B B.txt -t 48 -o ten.nrrd

confidence

Dxx Dxy Dxz

Dyz

Dzz

Dyy

Inspecting diffusion tensors

R=| v1.x |G=| v1.y |B=| v1.z |

v1

xy

zforeach SLC ( 30 40 50 60 ) tend slice -i ten.nrrd -a 2 -p $SLC \ | tend evecrgb -c 0 -a fa \ | unu axdelete -a -1 \ | unu quantize -b 8 \ -min 0 -max 1 -o $SLC-rgb.pngend

1 = 2 = 3

1 + 2 + 3 = T

Space of tensor shape

1

2

3

1

2

3

v1

v2

v3

linear planar

spherical1 >= 2 >= 3

Glyph shapes

1 glyph = 1 mm3

Backdrop: FA

Color: RGB(v1)

http://teem.sourceforge.net