Mobile C-arm CT reconstruction based on prior knowledge

on intra-operation

KAIST ISL & MIRLAB

Mobile C-arm CT

• C-arm system for image-guided radiotherapy and surgery

– 2D fluoroscopic imaging

– 3D CT imaging

• Depiction of complex anatomic structure

• Mobile C-arm CT

– Head and neck surgery, thoracic surgery

– Advantages

• Compactness and convenient use

• Affordability

– Limitations

• Limited angular scan range

• Small scan field of view

• Long scan time

Mobile C-arm

Rotation scan for 3D CT

Applications of mobile C-arm CT

Image guidance using mobile C-arm CT

J. H Siewerden, et al., Proc. Of SPIE Vol. 7261(2009)

Case courtesy of Dr Dylan Kurda, Radiopaedia.org, rID: 36795

Chordoma in clivus

Chondrosarcoma located inside head

K.W. Yeom, et al., American Journal of Neuroradiology (2013)

The trend of mobile C-arm CT

- Trade-off between “scan time + # of projection” and scan angle

- Truncation artifact caused by small FOV (11cm~17cm)

• Mobile C-arm CT recon. for surgeon updated image

– Head & neck surgery on intra-operation

• Problems of mobile C-arm CT

– Super short scan

– Truncation artifact

Research direction

XCAT phantom FDK reconstructionFOV: 13.25 cm

Scan angle: 135°

Ziehm imaging mobile C-arm CT system simulation

Horizontal profile

Vertical profile

Research direction

Reconstructed image

from pre-operative data

Reconstructed image

in intra-operation

Projection data

in intra-operation

135°

360°

Surgeon updated image

• Approach for reconstruction of head and neck surgery

– Pose estimation of pre-operative image for head

– Prior knowledge based reconstruction

Prior knowledge

based reconstruction

Pose estimation

Intra operative

scan data

Pre-operative

image

Reconstructed

image

Initial

reconstruction

Rigidly transformed

image

Simulation study

Case courtesy of Dr Dylan Kurda, Radiopaedia.org, rID: 36795

Chordoma in clivus

고품질의 MD CT 영상 + 가상의 척색종(Chordoma) 삽입

- Truncated data

- 100 projection views

- 135° scan angle

- CBCT forward projection

Transformed! - Motion consideration

• Scanning condition (all directional truncation)

– Cone-beam CT : 240x240 (94mmx94mm) plat panel detector

– Distance between source to object : 1000mm

– Distance between source to detector : 1500mm

– Number of projection views : 100

– Total scan angle : 135°

• Problems of mobile C-arm CT

– Super short scan ⇒ Noo et al 2002 weighting method

– Truncation artifact ⇒ Ohnesorge et al 2000 mirroring method

Simulation study

• Super short scan ⇒ Noo et al 2002 weighting method

Simulation study

• Truncation artifact ⇒ Ohnesorge et al 2000 mirroring method

Simulation study

>0.01<0.03

• Truncation artifact ⇒ Ohnesorge et al 2000 mirroring method

Simulation study

Simulation study

Reference (FDK 267 views)

• Sub-problem for using preoperative image

– Needs a rigid registration technique

– Pose of 3D rigid-body head in CT scan

• 4x4 transformation matrix, P

– Three rotations followed by three translations.

Pose estimation

z z

Pre-operative image acquisition Intra-operative image acquisition

P

P-1

1000

coscossincossin

sincoscossinsincoscossinsinsincossin

sinsincossincoscossinsinsincoscoscos

z

y

x

t

t

t

P

Pose estimation

• Rigid registration

– Estimation of registration parameter, Θ describing the pose

– Cost function

• SSD (Sum of squared differences)

• NMI (Normalized mutual information)

– Measure based on the joint entropy

))(),;((maxarg xΘxΘ

gfS

f(x): pre-operative image

g(x): initial recon. image

S(∙): similarity measure

Θ: registration parameter

x

xΘx2

)();( gf

H(A), H(B): Marginal entropy

H(A,B): Joint entropy

M(A,B): Mutual information),(

)()(),(

BAH

BHAHBAY

H(A) H(B)

M(A,B)

),()()(),( BAHBHAHBAM

Fig. Diagram for MI

Pose estimation

• Rigid registration

– Cost function

• Mutual information using local structure matrix*

• Local structure matrix

xxΘx

xΘ dghfh

Vhhp

V

C

21

21

;1,

m

CC hh xxx 21

,exp

,,ln,1

2

2121

N

n

hhnhh CCCC xxxx nhnhn xCxC21

rqpzzyzx

zyyyx

zxyxx

IIIII

IIIII

IIIII

rqpwzyxC

,, 2

2

2

,,,,

: Gaussian kernelV : overlap region : width of window(x) : weighting function∆(,) : distance between two matrices

w: Gaussian function centered on (x, y, z)p, q, r: neighborhood of (x, y, z)

Weightingfunction

Distance betweentwo matrices

*: J. Woo, et al., TIP, 2015

Pose estimation

• Rigid registration

– Cost function

• CEIO (conditional entropies of intensities and edge orientation)*

– Intensity of two images: statistical information

– Edge orientation coincidence function: local spatial information

» Remove discrepancy due to intensity variation.

– Similarity metric

)(cos2)}2cos(1{)( 2 o

∆ : difference between orientation angles in image A and B

H(A) H(B)

H(O)

B)(A,O)B,(A,

B)}(A,(B)(A){O)B,(A,

B)A,|(OA)|(BB)|(AB)(A,

MH

HHHH

HHHE

M(A, B): mutual information

*: Y. S. Kim, et al., Pattern Recognition, 2015

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