computed tomography diego dreossi silvia pani university of trieste and infn, trieste section

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Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

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Page 1: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

Computed Tomography

Diego DreossiSilvia Pani

University of Trieste and INFN, Trieste section

Page 2: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

Summary

• Computed Tomography– Purpose– How does it work?

• CT reconstruction– Filtered backprojection

• Synchrotron radiation CT• MicroCT at the SYRMEP beamline• Further applications

Page 3: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

The goal

• To provide a three-dimensional information (depth of the structures/organs)

• To provide information about the attenuation coefficient (tissue characterization)

Page 4: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

From planar imaging…to CT

Source: University of Arkansas Medical Schoolhttp://anatomy.uams.edu/HTMLpages/anatomyhtml/xraythorax.html

Page 5: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

How does it work?• Laminar beam and laminar detector• The object/patient is rotated in front of the beam• During the rotation, planar images (projections) are

acquired at different angles• The matrix of acquired data is called SINOGRAM: each line

of the sinogram corresponds to a projection at a given angle

• A map of the attenuation coefficients is reconstructed from the sinogram by means of proper algorithms

Page 6: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

Image acquisition

• The line integral of the attenuation coefficient determines the attenuation along each X-ray path

0I 1

0l

dx

eI

20

l

dx

eI

Page 7: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

Image reconstruction: backprojection

• Purpose: obtaining the distribution function f(x,y) given a complete set of projections P(,t)

• Fourier slice theorem: the FT of a projection P(,t) corresponds to a sampling of the FT of f(x,y) along a line tilted by an angle

yP(t)

f(x,y)

t

x

X-rays

F[P(t)]

v

u

F(u,v)

Page 8: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

Filtered backprojection• Each pixel of the matrix

is reconstructed by summing up all projections crossing it

• But: oversampling for low frequencies!

u

v

Filtering: each transformed projection is multiplied by a function reducing the contribution of low frequencies

Page 9: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

Filtered backprojection

• Ramp filter (RamLak): enhancement of high frequencies noise

• Gen-Hamming, Shepp-Logan: enhancement of intermediate frequencies

• Convolution theorem convolution in the direct space as an alternative to multiplication in the Fourier space

-0.2

-0.1

0

0.1

0.2

0.3

-10 -5 0 5 10

RamLakShepp-Logangen-Hamming

amp

iezz

a (u

.a.)

pixel

0

0.1

0.2

0.3

0.4

0.5

0 0.1 0.2 0.3 0.4 0.5

rampa

Shepp-Logan

gen-Hamming

amp

iezz

a (u

.a.)

frequenza (1/step)

Page 10: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

The advantages of SR CT – I• Parallel beam: – an acquisition across 180 degrees provides all necessary information (lower dose – shorter acquisition

time)– simplified reconstruction algorithms

divergentbeam

parallelbeam

+180°

+180°

Page 11: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

The advantages of SR CT – II

• Monochromatic beam:– No beam hardening artifacts– Possibility of reconstructing true

attenuation coefficients tissue characterization

Page 12: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

MicroCT at the SYRMEP beamline - I

BEAM

CCDdetector

rotationalstage

sample

Page 13: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

0 degrees 90 degrees 140 degrees

MicroCT at the SYRMEP beamline - II• A projection = a 2048 * 300 matrix• A height h is chosen• A line at height h is extracted from each

projection and a sinogram is formed

Page 14: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

MicroCT at the SYRMEP beamline - III

• The sinogram is trimmed in order to have the center of rotation coincident to the center of the matrix

Page 15: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

MicroCT at the SYRMEP beamline - IV• The sinogram is transformed in order to

obtain line integrals of the attenuation coefficients

=-ln ( )/I0

l

dx

l

dx

eI

0

0I

Page 16: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

MicroCT reconstructions at the SYRMEP beamline - V

• Each line of the logarithmic sinogram is filtered

• The final image is obtained by means of a backprojection

Volume rendering

Page 17: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

Volume rendering

• 2-d images are stacked• The 3-d structure of the object is

visualized

Page 18: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

A possible application: sintered replicas of biological samples• Why?

– Tool for enhanced visualization– Used in mechanical tests to assess the bone

architecture load bearing capabilities• Tests on biological samples cannot be repeated!

• How?– Built by rapid prototyping in a relatively

homogeneous and well characterized material

Page 19: Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section

Selected laser sintering

• The object is defined by means of a CAD file

• Layer Manufacturing Technology (LMT)• Parts are manufacturing layer by layer

from polyamide powder with a CO2 laser

• Complex structures, such as the trabecular architecture, can be built