ACCURATE EXTRACTION OF TISSUE PARAMETERS FOR MONTE

CARLO SIMULATIONS USING MULTI-ENERGY CT

Arthur Lalonde and Hugo Bouchard

Département de physique, Université de Montréal

2

THE IMPORTANCE OF MC IN RT

Monte Carlo (MC) simulations offer many

advantages over conventional algorithms for dose

calculat ion:

• In brachytherapy, dose deposition depends

strongly on Z due to the dominance of

photoelectric effect at low photon energies.

• In particle therapy, accurate beam range

calculation is critical for optimal planning and

patient safety.0 50 100 150 200

Depth [mm]

0

20

40

60

80

100

Re

lative

do

se

[%

Dm

ax

]

183 MeV protons

Muscle

Adipose

3

PATIENT GEOMETRY TO MC INPUTS

• One of the key steps in the preparation of a MC simulation is the

creation of the patient geometry, including the assignment of

material composit ion in each voxel.

• Complete elemental composit ion and mass density is necessary to

calculate the exact cross sect ions for all interactions considered.

• Great at tent ion must be paid to this step as it influences all results

generated by the simulation: « Rubbish in, Rubbish out ».

4

THE SCHNEIDER METHOD

To extract MC inputs from single energy CT (SECT) data, the gold

standard is the method of Schneider et al. (2000). The CT is calibrated to

construct a segmented look-up table (LUT) that links every possible HU

to a certain set of MC inputs.

ALLO ALLOASTRO

ALLO ALLO

Reference

dataset

-1000 -500 0 500 1000 1500 2000 2500 3000

HU

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

SP

R

Com

posi

tion 1

Com

posi

tion 2

Com

posi

tion 2

3

Com

posi

tion 3

Com

posi

tion 4

…

THE SCHNEIDER METHOD

To extract MC inputs from single energy CT (SECT) data, the gold

standard is the method of Schneider et al. (2000). The CT is calibrated to

construct a segmented look-up table (LUT) that links every possible HU

to a certain set of MC inputs.

ALLO ALLOASTRO

ALLO ALLO

Reference

dataset

-1000 -500 0 500 1000 1500 2000 2500 3000

HU

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

SP

R

-150 -100 -50 0 50 100

HU

0.94

0.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

SP

R

Adiposetissue3

Adiposetissue2

Yellowmarrow

Adiposetissue1

Mammarygland1

Mammarygland2

Adrenalgland

Redmarrow

BrainCerebrospinalfluid

SmallintestinewallGallbladderbile

Pancreas

LymphProstate

Urine

Mammarygland3

Eyelens

Kidney1

Liver2 Spleen

Trachea

Skin1

Liver3

Skin2

Skin3

Thyroid

Connectivetissue

Up to 4.4%

errors

Com

posi

tion 1

Com

posi

tion 2

3

Com

posi

tion 2

Com

posi

tion 3

Com

posi

tion 4

…

5

6

DUAL AND MULTI-ENERGY CT

• With dual- or mult i-energy CT,

empirical LUT are obsolete, as

more information can be extracted

directly from MECT data

HU1

{

HU2

{

http://www.healthcare.siemens.com/.

7

DUAL AND MULTI-ENERGY CT

• With dual- or mult i-energy CT,

empirical LUT are obsolete, as

more information can be extracted

directly from MECT data

• Still not enough information to

derive directly MC inputs

• How can we use opt imally the

added informat ion to improve

the quality of MC inputs?

HU1

{

HU2

{

http://www.healthcare.siemens.com/.

8

CT DATA TO MONTE CARLO INPUTS

• We want to extract full atomic composit ion and mass density, but we have

only limited information (# of energies) per voxel.

• Tissue characterization for Monte Carlo dose calculat ion from CT data is an

underdetermined problem

• We propose to use principal component analysis (PCA) on reference dataset

to extract a new basis of variables that can describe human tissues

composition more efficient ly by reducing the dimensionality of the problem.

• We call these variables Eigent issues (ET)

9

CT DATA TO MONTE CARLO INPUTS

• We want to extract full atomic composit ion and mass density, but we have

only limited information (# of energies) per voxel.

• Tissue characterization for Monte Carlo dose calculat ion from CT data is an

underdetermined problem

• We propose to use principal component analysis (PCA) on reference dataset

to extract a new basis of variables that can describe human tissues

composition more efficient ly by reducing the dimensionality of the problem.

• We call these variables Eigent issues (ET)

10

EIGENTISSUE REPRESENTATION OF HUMAN BODY

• All informat ion relevant for dose calculat ion can be stocked in a

vector of partial electron densities:

• The ET representation consists of a linear t ransformat ion of x:

x = ⇢e [λ1 λ2 ... λM ]

= [x1 x2 ... xM ]

x = y1 ·ET1 + y2 ·ET2 + ... + yM ·ETM

Density of electrons Fraction of electrons of element M in the tissue

Vector of the partial densities in the M th eigentissue

14

APPLYING PCA TO HUMAN TISSUES

• Human tissues are composed of a limited number of elements.

Including trace elements, only 13 different chemical components are

reported in the literature.

• Also, the weight fraction of these elements is often strongly

correlated (ex: P & Ca) or ant icorrelated (ex: C & O).

• The eigentissues allow to characterize human t issues with less than 13

variables without losing much accuracy.

1414

15

ADAPTATION TO CT DATA

• Using a suitable stoichiometric calibrat ion, the photon at tenuat ion of

each ET can be estimated for any spect rum or imaging protocol.

ALLO ALLOASTRO

ALLO ALLO µ(Ei ,x) ⇡ f⇣k

(i )1 ,k

(i )2 , ...

⌘µ(Ei ,ET1)

µ(Ei ,ET2)...

µ(Ei ,ETM )

15

16

ADAPTATION TO CT DATA

• Once their attenuation coefficient is estimated, the ETs are treated as

virtual materials.

• If K information is available (i.e. K energies), decomposit ion is

performed to extract the fract ion of the K more meaningful ETs in

each voxel.

0

B@

y1

...

yk

1

CA ⌘

0

B@

µ(E1,ET1) . . . µ(EK ,ET1)...

......

µ(E1,ETK ) . . . µ(EK ,ETK )

1

CA

− 1 0

B@

µ(E1)...

µ(EK )

1

CA

1616

17

APPLICATION TO DECT: BENCHMARKING WITH OTHER

METHODS

• Comparison with two recently published

methods for the characterization of 43

reference soft tissues using DECT:

• Water-Lipid-Protein (WLP)

decomposition (Malusek et al. 2013)

• Parameterizat ion (Hünemohr et al. 2014)

• Simulated HU for 80 kVp and 140/Sn kVp

spectra of the SOMATOM Definition Flash

DSCT

EAC SPR

0

0.5

1

1.5

2

2.5

3

RM

S e

rro

r [p

.p.]

WLP

Parametrization

PCA

17

RMS error Mean error-0.2

0

0.2

0.4

0.6

Re

lative

err

or

on

SP

R [

%]

Schneider 2000

3 Materials

Parametrization

ETD

18

POTENTIAL EXTENSION TO MECT

• Separating a 140 kVp

spectrum in five

energy bins, the

method shows

improvement in

extracting elemental

weights with more

than two informat ion.

H C N O P Ca

Element

0

5

10

15

20

RM

S e

rro

r [p

.p.]

1 Energy bin

2 Energy bins

3 Energy bins

4 Energy bins

5 Energy bins

1818

19

VALIDATION OF ETD ON PATIENT GEOMETRY

• A virtual pat ient generated from real anatomical

data is used as ground truth for MC dose calculation

• A reference t issue with known composit ion is

assign to each voxel, while the density is allowed to

vary.

• SECT and DECT images are simulated using

Matlab

• Dose calculation is performed using the EGSnrc

user-code BrachyDose for Brachytherapy and

TOPAS for proton therapy

Adipose

Air

Calcifications

Femur spherical head

Femur conical trochanter

Muscle

Prostate

Red marrow

Small intestinewall

Bladder

0.9

1

1.1

1.2

1.3

1.4

3

Mass

den

sity

(g.c

m−

3)

19

20

ETD FOR BRACHYTHERAPY: RESULTS

No

nois

e

SECT DECT

-30 %

-20 %

-10 %

0 %

10 %

20 %

30 %

No

nois

eSECT DECT

-30 %

-20 %

-10 %

0 %

10 %

20 %

30 %

SECT - Schneider DECT - ETD

Relative

error on

dose

20See Poster #144 - Remy et al.

21

GT

ETD

GT

SECT

ETD FOR PROTON THERAPY: RESULTS

GT

ETD

GT

SECT

22

ETD FOR PROTON THERAPY: RESULTS

Range error up to 1.5 mm using SECT

23

CONCLUSION

• Eigent issues representation of human body composit ion minimizes the number of parameters needed for accurate

characterizat ion

• Adapting this representation to material decomposit ion of CT data allows extracting high quality Monte Carlo

inputs from only few measurements

• The method is accurate and versat ile:

• Not limited to only two parameters (EAN and ED)

• Valid through the whole range of X-ray energies (e.g. kV and MV)

• More accurate dose calculation for both low-kV photons and protons than the gold-standard SECT approach

• Associated Publicat ions:

• A. Lalonde and H. Bouchard (2016), A general method to derive tissue parameters for Monte Carlo dose

calculation with dual- and multi-energy CT, Phys. Med. Biol.

• A. Lalonde, E. Bär and H. Bouchard (2017). A Bayesian approach to solve proton stopping powers from noisy

multi-energy CT data, Med. Phys.

23

24

THANK YOU FOR YOUR ATTENTION

24

Acknowledgements:

• Charlotte Remy

• Esther Bär

• Jean-François Carrier

• Dominic Béliveau-

Nadeau

• Mikaël Simard