Ge Wang, PhD, Director SBES Division & ICTAS Center for Biomedical Imaging VT-WFU School of Biomedical Engineering & Sciences Virginia Tech, Blacksburg, VA, USA ge-wang@ieee.org

daughterduckUrban and Civil

Nov 15, 2013 (3 years and 6 months ago)

62 views



Ge Wang, PhD, Director

SBES Division & ICTAS Center for Biomedical Imaging

VT
-
WFU School of Biomedical Engineering & Sciences

Virginia Tech, Blacksburg, VA, USA

ge
-
wang@ieee.org


October 1, 2010



Interior Tomography (2007)

Object

Beam

Source

ROI

Object

Beam

ROI

Object

Beam

Known

Sub
-
region

ROI

Object

Beam

Sparsity

Model

Regular Reconstruction

Interior Problem

Landmark
-
based

Interior Tomography

Sparsity
-
based

Interior Tomography



First Paper (May 2007)



Independent Work (Oct. 2007)



Interior CT Patent



Literature Analysis



Outline



Less Is Deeper



Less Is Larger



Less Is Faster



Less Is Less



Less Is More



Less Is Deeper

Use of
less

projection data for accurate
image reconstruction demands
deeper

insight, more advanced theory and more
powerful tools.



Computed Tomography (Wholesale)

t



Sinogram

X
-
rays

t



y

x

p

Measurement

Reconstruction

Object

)
,
(
y
x
f
)
,
(
t
P



Inner Vision with Local Data (Retail)

t

Sinogram

X
-
rays

t

y

x

Measurement

Reconstruction

Object

)
,
(
y
x
f
)
,
(
t
P

X

X





Earliest BPF Formula (1991)





Half
-
PI
-
Line Reconstruction (2006)

Field of View (FOV)

Partial
-
PI
-
Line



Extrapolation from a Known Point (2006)

FOV

?



Curved Filtering Path (2003)

?



Interior Reconstruction

(a)

(b)

50
100
150
200
-1000
-500
0
500


(HU)

(Pixel)

(c)

50
100
150
200
-1000
-500
0
500


(HU)

(Pixel)

(d)

Global

FBP

Local

FBP

Local

SART

Interior

Recon



HOT



Sparsity
-
based Interior Recon



Outline



Less Is Deeper



Less Is Larger



Less Is Faster



Less Is Less



Less Is More



Less Is Larger

Acquisition of
less

projection data is
achieved with a narrower beam, and an
object
larger

than the beam width is not a
concern.



Preclinical Nano
-
CT

X
-
ray
Beam

Central
Stop

Condense
Lens

Sample

Zone

Plate

Phase Ring

Rotation Axis

ROI

Sample Stage



Potential for Study on Earliest Life

Hagadorn

JW, et al. (2006) Cellular and
subcellular

structure of
Neoproterozoic

embryos.


Science 314:291

294



Big Patient Problem



Outline



Less Is Deeper



Less Is Larger



Less Is Faster



Less Is Less



Less Is More



Less Is Faster

Less

data means smaller detector size,
faster

frame rate, and more imaging chains,
all of which contribute to accelerate the
data acquisition process.



Spiral Cone
-
beam CT



Dual
-
source Clinical CT (2005)



Multi
-
source Interior Tomography

ROI

Wang G, Yu H, Ye YB. Virginia Tech Patent Disclosure on May 15, 2007,


US Patent Application 12/362,979 allowed on October 21, 2009

Ye YB, Yu HY, Wei YC, Wang G. International Journal of Biomedical Imaging, Article ID:63634, 2007

Wang G, Yu H, Ye Y. Medical Physics. 36:3575
-
3581, 2009



From Scanning to Roaming



Outline



Less Is Deeper



Less Is Larger



Less Is Faster



Less Is Less



Less Is More



Less Is Less

Less

data is equivalent to
less

radiation
dose, because of not only a narrower beam
but also a more relaxed angular sampling
requirement in the longitudinal studies or
multi
-
scale scenarios.



Reduced Angular Sampling Rate

Need 2 Projections

Need 4 Projections



Statistical Interior Tomography

Work in progress from Qiong
Xu

& Xuanqin Mou
(China) in collaboration with Wang G & Yu HY

Phantom [0.9 1.1]
ROI
Hilbert Region
Known sub
-
Region
200,000 photons

50,000 photons

ITHT ML



Outline



Less Is Deeper



Less Is Larger



Less Is Faster



Less Is Less



Less Is More



Less Is More

Use of
less

data is advantageous in
more

modalities beyond CT, such as other
straight
-
ray tomographic techniques and
even in small
-
angle curvilinear geometry,
and
more

applications of various types.
Furthermore, less data means
more

computational time!



Interior
-
MRI

……………………………………





Interior
-
MRI

Zhang J, Yu HY,
Corum

C, Garwood M, Wang G: Exact and stable
interior ROI reconstruction for radial MRI. SPIE 7258: 2585G, 8 pages,
Feb. 2009, Orlando, FL, USA

Traditional MRI
Interior MRI



Interior Electron Tomography

Ge
Wang,
Hengyong

Yu



Limited Angle Interior Tomography



Interior SPECT



Interior
-
SPECT

Support

FOV

Known

Ideal data

Yu HY, Yang JS, Jiang M, Wang G:
Interior SPECT
-

Exact and stable ROI
reconstruction from uniformly attenuated
local projections; Communications in
Numerical Methods in Engineering,
25(6):693
-
710, 2009

µ
a

=
0cm
-
1

µ
a

=
0.15cm
-
1

µ
a

=
0.3cm
-
1

Noisy data



Practical Implications



Conclusion



Less Is Deeper



Less Is Larger



Less Is Faster



Less Is Less



Less Is More



Less Is Not Always Better



Link of Localities

Pictures from http://www.bing.com





Multi
-
scale Interior Tomography



Multi
-
parameter Interior Tomography



Multi
-
energy Interior Tomography

Future Work



SBES Advanced Multi
-
scale CT Facility



Smaller Scales?



Larger Scales?



Multi
-
parameter CT



Grating
-
based Imaging



Wang G, Cong W,
Shen

H,
Zou

Y: Varying Collimation for Dark
-
Field Extraction.
International Journal of Biomedical Imaging. 2009, Article ID 847537, 2010

Dark
-
field Tomography



Multi
-
energy CT









Theoretical Extension



Computational Optimization



Systematic Evaluation



Biomedical Applications



Interdisciplinary Collaboration

Future Work



Acknowledgment

The results in this presentation are of collaborative
nature.
Major collaborators include Drs. Hengyong Yu, Yangbo
Ye, Jiangsheng Yang, Ming Jiang, Steve Wang, Michael
Fesser
, Erik
Ritman, Deepak Bharkhada, Bruno DeMan,
Guohua Cao, Otto Zhou, Alexander Katsevich, et al.
The
work was partially supported by National Institutes of
Health/National Institute of Biomedical Imaging and
Bioengineering Grants EB002667, EB009275, and
EB011785 as well as National Science Foundation
NSF/CMMI 0923297.



Thank You!