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Pseudo-­‐CT  generation  using  MRI  images  from  undersampled,  single-­‐acquisition  UTE-­‐mDixon  

Kuan-Hao (Dylan) Su, Jung-Wen (Gloria) Kuo, Lingzhi Hu, Christian Stehning, Michael Helle, Gisele C. Pereira, David W. Jordan , Pengjigng Qian, Cheryl L. Thompson, Karin A. Herrmann, Raymond F. Muzic, Jr., Melanie Traughber, Bryan J. Traughber

Presenter: Kuan-Hao (Dylan) Su, PhD

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Introduction

Photon Attenuation and Absorption info.

CT

MR

pseudo-­CT

T1, T2, PD, UTE…UTE-­mDixon

Fuzzy c-­means clustering(FCM)

pseudo-­CT

PET/MR

MR  Linac

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Outline

MR CT

Acquire  informative  MR  data

MR  data  correction

Tissue  clustering

CT  formation

4

Materials  and  Methods

MR  UTE-­‐mDixon acquisition

MR  UTE  reconstruction

Point  spread  function  evaluation

Are  point  spread  functions  optimized?  

FID  spatial  scaling  optimization

Fuzzy  c-­‐means

Tissue   assignment

Resolution  matching

yes

noMR  image

optim

izatio

nclu

stering

R2*  and  Dixon  reconstruction

Fig.  1

pseudo-­‐CT

Flow  chartpseudo-­‐CT  generation

MR

CT

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MR  Sign

al

TE

Soft

Bone

Air

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Materials  and  Methods    -­‐-­‐-­‐ multi-­‐echo  UTE-­‐mDixon  sequence

FID  (TE  =  0.1 ms)   echo1  (TE  =  1.5 ms)   echo2 (TE  =  2.8 ms)  

MR  UTE-­‐mDixon acquisition

MR  UTE  reconstruction

Point  spread  function  evaluation

Are  point  spread  functions  optimized?  

FID  spatial  scaling  optimization

Fuzzy  c-­‐means

Tissue   assignment

Resolution  matching

yes

noMR  image

optim

izatio

nclu

stering

R2*  and  Dixon  reconstruction

Fig.  1

pseudo-­‐CT

Flow  chartpseudo-­‐CT  generation

MR

CT

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Materials  and  Methods    -­‐-­‐-­‐ UTE  trajectory  delay  correctionNMR  rod

without  correction with  correction  (+1.18  μs)

Eddy-­‐current  induced  gradient  delay

MR  UTE-­‐mDixon acquisition

MR  UTE  reconstruction

Point  spread  function  evaluation

Are  point  spread  functions  optimized?  

FID  spatial  scaling  optimization

Fuzzy  c-­‐means

Tissue   assignment

Resolution  matching

yes

noMR  image

optim

izatio

nclu

stering

R2*  and  Dixon  reconstruction

Fig.  1

pseudo-­‐CT

Flow  chartpseudo-­‐CT  generation

MR

CT

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UTE-­mDixon sequence

FID  (TE  =  0.1 ms)   echo1  (TE  =  1.5 ms)   echo2 (TE  =  2.8 ms)  

Dixon-­‐fat Dixon-­‐waterR2*

Dixon  separation:R2*  (1/T2*)  estimation:I(p) =  I0 exp[  -­‐ R2* x  TE(p)  ]

(190  seconds)

10Su  et  al.  ,  Medical  Physics,  42(8),  2015

MR  UTE-­‐mDixon acquisition

MR  UTE  reconstruction

Point  spread  function  evaluation

Are  point  spread  functions  optimized?  

FID  spatial  scaling  optimization

Fuzzy  c-­‐means

Tissue   assignment

Resolution  matching

yes

noMR  image

optim

izatio

nclu

stering

R2*  and  Dixon  reconstruction

Fig.  1

pseudo-­‐CT

Flow  chartpseudo-­‐CT  generation

MR

CT

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Fuzzy  c-­‐means  clustering

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Feature  1

Feature  2

Height

Weight

90%  O +  10%  of  O

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Clustering

Membership  function  (  five clusters)  

class1

FCM

Dixon-­‐fat Dixon-­‐waterR2*

MR  features

R2*

Dixon-­‐fat

Dixon-­‐water

Mapclass1class2

class4

class3

class5

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Pseudo-­‐CT  composition

×  CTair + ×  CTfat + ×  CTfluid

+ ×  CTbrain + ×  CTbone

= pseudo-­‐CT

(-­‐1000  HU) (-­‐98  HU) (-­‐13  HU)

(40  HU) (-­‐1524  HU)

Schneider  et  al.,  Phys  Med  Biol,  vol.  45,  2000

Membership  function  (  five clusters)  

MR  UTE-­‐mDixon acquisition

MR  UTE  reconstruction

Point  spread  function  evaluation

Are  point  spread  functions  optimized?  

FID  spatial  scaling  optimization

Fuzzy  c-­‐means

Tissue   assignment

Resolution  matching

yes

noMR  image

optim

izatio

nclu

stering

R2*  and  Dixon  reconstruction

Fig.  1

pseudo-­‐CT

Flow  chartpseudo-­‐CT  generation

MR

CT

Resolution  matching

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MR

Resolution  mismatched

Low-­‐dose  CTfor  Attenuation  correction

axial coronal sagittal

Steel  beads  (size  1.00  mm)

GEMINI  CT  600  mm  recon.  FOVslice  thickness:  5  mmStandard,  filter  B  Pitch  =  0.813

ACR  CT  phantom  (GAMMEX  464)

Resolution  matching -­‐-­‐-­‐ measure  CT  resolution

Resolution  matching  -­‐-­‐-­‐ measure  MR  resolution

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MR  phantom

Step  response   function

MR  phantom

Resolution  matching

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MR

Resolution  mismatched

1.5 x  1.5 x  1.5 mm3 1.7 x  1.7 x  6.3 mm3

Spatial  resolution  (FWHM):

Matched

Low-­‐dose  CTfor  Attenuation  correction

MR  UTE-­‐mDixon acquisition CT  acquisition

MR  UTE  reconstruction

Point  spread  function  evaluation

Are  point  spread  functions  optimized?  

FID  spatial  scaling  optimization

Fuzzy  c-­‐means

Tissue   assignment

Resolution  matching

Low-­‐dose  CT

Rigid-­‐body   transformation

yes

noMR  image

optim

izatio

nclu

stering

R2*  and  Dixon  reconstruction

Fig.  1

pseudo-­‐CT

Flow  chartpseudo-­‐CT  generation

MR

CT

(n  =  9)

Results  and  Discussion

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Results -­‐-­‐-­‐ views  in  feature  domain

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Results -­‐-­‐-­‐ views  in  feature  domain  (n  =  9)

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air

brain fluid

bone

fat

Dixon-­‐fat  (a.u.)

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0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

air brain fat fluid bone

Feature  Intensity

 

Tissue  Type

R2*    (ms)

Dixon-­‐fat  (a.u.)

Dixon-­‐water  (a.u.)

Results -­‐-­‐-­‐ views  in  feature  domain(N  =  9)

Class  1 Class  2 Class  3

FCM  Membership  functions

FCM  clustering  and  tissue  assignment

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Class  4 Class  5

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

air brain fat fluid bone

Feature  Intensity

 

Tissue  Type

R2*    (ms)

Dixon-­‐fat  (a.u.)

Dixon-­‐water  (a.u.)

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Pseudo-­‐CT  generationMembership  function    (five clusters)  

×  CTair+ ×  CTfat + ×  CTfluid

+ ×  CTbrain + ×  CTcbone

= pseudo-­‐CT

(-­‐1000  HU) (-­‐98  HU) (-­‐13  HU)

(40  HU) (-­‐1524  HU)

Schneider  et  al.,  Phys  Med  Biol,  vol.  45,  2000

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CTLow-­‐dose  

Pseu

do-­‐CT

Pseu

do-­‐CT

(resolutio

n  matching)

1200

0

-­‐1000

HU

axial coronal sagittal

Results -­‐-­‐-­‐ CT  vs.  pseudo-­‐CT

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Results -­-­-­ computation  cost

l pseudo-­CT generation timeØ FCM clustering:

~ 60 seconds

Ø Tissue assignment and CT generation< 1 second

Computer:Windows  7    64-­‐bit16  GB  RAMIntel®    i7    3.4  GHz

COMKAT:    http://comkat.case.edu

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ConclusionsThe  UTE-­‐mDixon, FCM  approach is  an  accurate,  clinically  practical  method  for  pseudo-­‐CT  generation  and  can  be  used  to  improve  the  accuracy  of  MR-­‐ACand  MR-­‐RTP.

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Thank you~~~

-­-­ Dylan Su

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FID echo1 echo2

Dixonfat Dixonwater R2*

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PET  bias  (%) Frontal Occipital

Uniform  mask -­‐10.1  % -­‐15.2  %

FCM 0.0  % -­‐9.4  %

SVM 1.4  % 9.9  %

ANN -­‐0.7  % -­‐5.6  %

MR

VOI analysis -­‐-­‐ Bias  of  PET  (%) Normalized  by  brain  Act.

PET

Frontal

Occipital

Frontal

Occipital

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Histogram analysis -­‐-­‐ Bias  of  PET  (%)

010000200003000040000500006000070000

-­‐50 -­‐40 -­‐30 -­‐20 -­‐10 0 10 20 30 40 50

Uniform  vs  CT

010000200003000040000500006000070000

-­‐50 -­‐40 -­‐30 -­‐20 -­‐10 0 10 20 30 40 50

FCM  vs  CT

010000200003000040000500006000070000

-­‐50 -­‐40 -­‐30 -­‐20 -­‐10 0 10 20 30 40 50

SVM  vs  CT

010000200003000040000500006000070000

-­‐50 -­‐40 -­‐30 -­‐20 -­‐10 0 10 20 30 40 50

ANN  vs  CT

Bias  (%)

Bias  (%)

Bias  (%)

Bias  (%)

SD  =  3.1  %

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VOI analysis -­‐-­‐ Bias  of  PET  (%)

PET  bias  (%) mean SD range

Uniform  mask -­‐9% 3% -­‐13.5%  ~  -­‐4.7%FCM -­‐1% 3% -­‐3.4%  ~  +4.1%SVM 2% 3% -­‐0.7%  ~  +7.7%ANN 1% 1% -­‐0.7%  ~  +3.0%

Berker’s  paper(JNM,  2012)range:-­‐4.8%  ~  +7.6%

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Results

fuzzy c–means(FCM)

C5

ANNw/o spatial

features

ANNwith spatial

features

Bias (HU) -­‐22  ± 29 6  ± 57 28  ± 21

|error|(HU) 130  ± 16 138  ± 41   113  ± 18

R 0.78  ± 0.05 0.83  ± 0.06 0.87  ± 0.04

Figure of Merit

-­‐-­‐-­‐mean ± SD     (n  =  9)

* The ANN results were generated in the leave-one-out fashion.

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CT histogram

Attenuation  coefficients

coun

ts skin-­‐air  interface

air

fat

Soft  tissue

bone

Pseudo-­‐CT  -­‐ CT+1500

-­‐1500

0

axial coronal sagittal

axial coronal sagittal

+1500

0

-­‐1500