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Nicolas DUCROS, Simon RIT, Juan FPJ ABASCAL, Bruno SIXOU and Françoise PEYRIN

nicolas.ducros@creatis.insa-lyon.fr

Biomedical Imaging Research Center

Nonlinear regularized decomposition of spectral

x-ray projection images

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

SPECTRAL CT IMAGING

Conventional vs. Spectral = Grey level vs Color!

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[Cormode et al., Radiology, 256 (3), 2010]

Spectral CT Traditional CT

+ Chemical components + Density (g.cm-3) + K-edge contrast imaging

- Average attenuation - Arbitrary units

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RECONSTRUCTION STRATEGIES

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Sinogram

object

Tomographic Reconstruction

Linear Attenuation Coefficient

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RECONSTRUCTION STRATEGIES

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Sinogram

object

Tomographic Reconstruction

Photon Counting Detector

Linear Attenuation Coefficient

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RECONSTRUCTION STRATEGIES

3

Sinogram

object

Tomographic Reconstruction

Photon Counting Detector

Linear Attenuation Coefficient

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RECONSTRUCTION STRATEGIES

3

Sinogram

object

Tomographic Reconstruction

Photon Counting Detector

Linear Attenuation Coefficient

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RECONSTRUCTION STRATEGIES

3

Sinogram

object

Tomographic Reconstruction

Photon Counting Detector

Linear Attenuation Coefficient

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RECONSTRUCTION STRATEGIES

3

Sinogram

object

Tomographic Reconstruction

Spectral

[Mendoca, et al., IEEE TMI, 2014] [Long and Fessler, IEEE TMI, 2014]

[Zhang et al., IEEE TMI, 2014] [Barber, Fully 3D, 2015]

Photon Counting Detector

Material Mass Density

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RECONSTRUCTION STRATEGIES

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Sinogram

object

Material sinogram

Decomposition

[Alvarez et al., PMB, 1976] [Brody et al., Med. Phys, 1981] [Schlomka et al., PMB, 2007] [Roessl et al., PMB, 2007]

Tomographic Reconstruction

Spectral

[Mendoca, et al., IEEE TMI, 2014] [Long and Fessler, IEEE TMI, 2014]

[Zhang et al., IEEE TMI, 2014] [Barber, Fully 3D, 2015]

Photon Counting Detector

Material Mass Density

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RECONSTRUCTION STRATEGIES

3

Sinogram

object

Material sinogram

Decomposition

[Alvarez et al., PMB, 1976] [Brody et al., Med. Phys, 1981] [Schlomka et al., PMB, 2007] [Roessl et al., PMB, 2007]

Tomographic Reconstruction

[Schirra et al., IEEE TMI, 2014] [Xu et al., PMB, 2014] [Sawatzky et al., IEEE TMI, 2014]

Tomographic Reconstruction

Spectral

[Mendoca, et al., IEEE TMI, 2014] [Long and Fessler, IEEE TMI, 2014]

[Zhang et al., IEEE TMI, 2014] [Barber, Fully 3D, 2015]

Photon Counting Detector

Material Mass Density

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

GOAL AND MOTIVATION

Why material decomposition of the sinogram? 1. Embeds all the nonlinearities of spectral CT 2. Naturally parallelizable across the projection views 3. Applicable to both CT and interventional radiography

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Sinogram Material sinogram

Decomposition

[Alvarez et al., PMB, 1976] [Brody et al., Med. Phys, 1981] [Schlomka et al., PMB, 2007] [Roessl et al., PMB, 2007]

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

GOAL AND MOTIVATION

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Sinogram Material sinogram

Decomposition

[Alvarez et al., PMB, 1976] [Brody et al., Med. Phys, 1981] [Schlomka et al., PMB, 2007] [Roessl et al., PMB, 2007]

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

GOAL AND MOTIVATION

5

Sinogram Material sinogram

Decomposition

[Alvarez et al., PMB, 1976] [Brody et al., Med. Phys, 1981] [Schlomka et al., PMB, 2007] [Roessl et al., PMB, 2007]

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

GOAL AND MOTIVATION

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Sinogram Material sinogram

Decomposition

[Alvarez et al., PMB, 1976] [Brody et al., Med. Phys, 1981] [Schlomka et al., PMB, 2007] [Roessl et al., PMB, 2007]

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

GOAL AND MOTIVATION

2D projection images at a fixed view

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Sinogram Material sinogram

Decomposition

[Alvarez et al., PMB, 1976] [Brody et al., Med. Phys, 1981] [Schlomka et al., PMB, 2007] [Roessl et al., PMB, 2007]

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

GOAL AND MOTIVATION

2D projection images at a fixed view

Notations

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Sinogram Material sinogram

Decomposition

[Alvarez et al., PMB, 1976] [Brody et al., Med. Phys, 1981] [Schlomka et al., PMB, 2007] [Roessl et al., PMB, 2007]

Projected mass (in g.cm-2) Photon numbers (no units)

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

GOAL AND MOTIVATION

2D projection images at a fixed view

Notations

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Sinogram Material sinogram

Decomposition

[Alvarez et al., PMB, 1976] [Brody et al., Med. Phys, 1981] [Schlomka et al., PMB, 2007] [Roessl et al., PMB, 2007]

Projected mass (in g.cm-2) Photon numbers (no units)

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

CONTRIBUTION

Variational framework Nonlinear decomposition Regularized decomposition

Gauss-Newton algorithm

Gold standard Superlinear convergence rates

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N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

CONTRIBUTION

Descent direction computation

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N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

CONTRIBUTION

Descent direction computation

Study case o M = 3 o P = 256 x 256

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Size

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

CONTRIBUTION

Descent direction computation

Study case o M = 3 o P = 256 x 256

Storage of the full Hessian is intractable!

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Size

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

CONTRIBUTION

Descent direction computation

Study case o M = 3 o P = 256 x 256

Storage of the full Hessian is intractable! Hessian is block diagonal with only PM2 non zero entries

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Size

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

NUMERICAL SIMULATIONS

Forward model

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[Schlomka et al., PMB, 2008]

[Poludniowski et al., Med Phys, 2007]

4 bins

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

NUMERICAL SIMULATIONS

Forward model

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[Schlomka et al., PMB, 2008]

[Poludniowski et al., Med Phys, 2007]

4 bins

Elekta Synergy

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

NUMERICAL SIMULATIONS

Forward model

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[Schlomka et al., PMB, 2008]

[Poludniowski et al., Med Phys, 2007]

4 bins

Elekta Synergy

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

NUMERICAL SIMULATIONS

Numerical Phantom 3-material Thorax Phantom

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3D atlas (labels) [Kéchichian et al., IEEE TIP, 2013]

3D volumes (g.cm-3)

Projection (RTK) http://www.openrtk.org/

2D image (g.cm-2)

3D CT scan (a.u) [3D-IRCADb data set]

Materials • Soft tissue • Bone • Gadolinium

(Portal vein )

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RESULTS

A typical decomposition Weighted least squares

Material-dependent regularization

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N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RESULTS

A typical decomposition Weighted least squares

Material-dependent regularization

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N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RESULTS

Convergence of the algorithm

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N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RESULTS

Convergence of the algorithm

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N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RESULTS

Influence of noise

Poisson noise

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N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RESULTS

Influence of noise

Poisson noise

Error vs noise

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N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RESULTS

Influence of noise

Poisson noise

Error vs noise

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N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RESULTS

Influence of noise

Poisson noise

Error vs noise

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N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RESULTS

Influence of the marker concentration

Concentration range: 0.01 to 1 g.cm-3

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N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RESULTS

Influence of the marker concentration

Concentration range: 0.01 to 1 g.cm-3

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N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RESULTS

Influence of the marker concentration

Concentration range: 0.01 to 1 g.cm-3

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N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

CONCLUSION

Material decomposition

We proposed a GN algorithm Weighted least squares L2/L1 material dependent

regularization

Thorax phantom with portal vein marked with gadolinium Different number of counts Different marker concentrations

Encouraging results

Fast convergence Good reconstruction quality

Thanks to the spectral CT team Juan FPJ ABASCAL Tom HOHWEILLER Jean-Michel LÉTANG Cyril MORY Françoise PEYRIN Odran PIVOT Simon RIT Bruno SIXOU Gloria VILCHES FREIXAS

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THANK YOU FOR

YOUR ATTENTION

N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RESULTS

Convergence Stopping criteria

o Step Length

o Cost function decrease

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N. Ducros, 19.07.2017 | CT Meeting – Spectral Session

RESULTS

Choice of the regularization parameter

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