volume ct with flat-panel detectors: j. prince (johns...

8/30/2010

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Volume CT with Flat-Panel Detectors:

From Image Scienceto Image-Guided Surgery

Volume CT with Flat-Panel Detectors:

From Image Scienceto Image-Guided Surgery

Jeff Siewerdsen, PhDDepartment of Biomedical Engineering

Johns Hopkins University

Johns Hopkins UniversitySchools of Medicine and Engineering

Acknowledgements

Consortium CollaboratorsJ. Prince (Johns Hopkins)A. Burgess (Harvard ret.)

R. Fahrig (Stanford University)A. Pineda (CSU Fullerton)

Funding SupportNational Institutes of Health

Siemens HealthcareCarestream Health

DisclosureAdvisory Board, Consultant

Carestream HealthElekta Oncology Systems

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CBCT

Conventional

Normal Excised

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OverviewModeling cone-beam CT imaging performance

- 3D cascaded systems analysis

Validation relative to human observers- Various imaging conditions and imaging tasks

Translation to IG interventions- IG radiation therapy and surgery

Improvement to interventional performance- Technology integration and assessment

NPS(fx,fy,fz)

J. H. Siewerdsen (Johns Hopkins University)

Flat-Panel Detectors• New base technology for x-ray

imaging (FPDs)- Advanced Applications

Tissue Discrimination (DE)Spatial Discrimination (3D)Spatio-Temporal (4D)

• New capabilities in:- Pre-clinical Imaging

Disease progression and response

- Diagnostic ImagingChest imagingBreast imaging

- Image-Guided InterventionsIG radiation therapyInterventional radiologyIG surgery


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Cone-Beam CT

Projection dataMultiple projections (~100-500)

over ~180o-360o

Volume reconstructionSub-mm spatial resolution

+ soft tissue visibilityJ. H. Siewerdsen (Johns Hopkins University)

Tornai et al.

Platforms for CBCT (examples)

Jaffray et al.Sukovic et al.

Siewerdsen et al.

Feldkamp et al.

Boone et al.J. H. Siewerdsen (Johns Hopkins University)

Experimental Platform for CBCT

Repeat for ~360o

X-ray Tube50 – 150 kVp, Pulsed fluoro

φcone ~5o-15o

Flat-Panel Detectora-Si:H PD + CsI:Tl(1536 x 2048 pixels, 1-30 fps)

Optical Bench + Motion Control System8-axis translation / rotation, 180o+fan – 360o


Stage Physical Process0 Incident quanta1 Interacting quanta2 Conversion to secondary quanta

A: Complete absorptionB: K x-ray escapeC: K x-ray reabsorption

3 Spread of secondary quanta4 Coupling of secondary quanta5 Integration by pixel aperture6 Sampling of pixel matrix7 Readout with additive noise










A CB

Modeling the Imaging Chain


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Modeling the Imaging Chain


Stage Physical Process0 Incident quanta

…

7 Projection readout

Stage Mathematical Process

8 Ramp Filter

9 Apodization Filter

10 Interpolation

11 Backprojection

12 Sampling

Projection

8T

12III

9T

10T

11Σ

Extension to Cone-Beam CT



fx fz

Projection

8T

12III

9T

10T

11Σ

NPSprojection(fx,fz)



fx

fz

NPS8(fx,fz)

12III

9T

10T

Projection

8T

11Σ

Ramp Filter

( )zx ffS ,7=( )zx ffS ,8 ( )xramp fT 2


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fxfz

NPS9(fx,fz)

12III

10T

Projection

8T8T

9T

11Σ

ApodizationWindow

( )zx ffS ,8=( )zx ffS ,9 ( )xwin fT 2



fxfz

NPS10(fx,fz)

12III

9T

Projection

8T8T

10T

11Σ

Interpolation

( )zx ffS ,9=( )zx ffS ,10 ( )zxinterp ffT ,2



8T

Back-Projection

Projection

12III

NPS11(fx,fy,fz)

9T

10T

8T

11Σ



fx fy

III12(fx ,fy ,fz)

fz 9T

10T

Projection

8T8T

11Σ

12III Sampling( ) ( )zz ffSffS ,, 1112 = ( )zff,III*** 12


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NPS(fx, fy, fz) Axial NPSS(fx, fy)

Sagittal NPSS(fx, fz)

The 3-D Noise-Power Spectrum

Broken Symmetry


NEQEffective number of quanta

used at each spatial frequency(Efficiency x Fluence)

DQEFraction of quanta

used at each each frequency.

Observations:3D DQE(0) ~ Projection DQE(0)

3D DQE(f) dependent on reconstruction parameters

The 3-D NEQ and DQE


Model Observer Detectability

Generalized NEQDetector TypeReconstruction FilterVoxel SizeBackground Noise

Task FunctionTask TypeObject sizeObject contrast

Model ObserversPrewhitening (PW)PW + Eye Filter + Internal Noise (PWEI)Non-Prewhitening (NPW)NPW + Eye Filter (NPW)NPWE + Internal Noise (NPWEI)J. H. Siewerdsen (Johns Hopkins University)

9AFC Tests

• Darkened reading room

• Diagnostic-quality display

• Fixed win / level [90%min, 110%max]

• 6 observers (physicists)

• Training set distinct from test set

• 5 repeats (distinct stimuli)

• Randomized reading order

• ~100 minutes for each observer

Human Observer Study


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Human Observer StudyUniform Background:Sphere detection

Cluttered Background:Large sphere

Small sphere

Cube / sphere discrimination

Encapsulated sphree



1010oo4040oo9090oo360360ooTotal Orbit (Total Orbit (TomoTomo Angle) Angle) θθ::

xx

zz


1 'A (1 ( ))2 2Z

derf= +

[ ]2

11 ( )( ', ) exp ( )22

Mcorr

x dP d M x dxφπ

∞ −

−∞

−= −

∫

Theoretical calculation(cascaded systems + task + model observer)

AZ

Measured directly from human observer MAFC tests

Pcorr d'


J. H. Siewerdsen (Johns Hopkins University) 0 60 120 1800.5

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NPWEi

PW, PWEi, NPW, NPWE

θtot

AzHuman

Observer

NPWEi

PW, PWEi, NPW, NPWE

θtot

Az

PW, PWEi

Human Observer

θtot

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PW, PWEi, NPWE

Human Observer

θtot

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PW, PWEi, NPWE

Human Observer

Uniform BackgroundSphere Detection

NPWE

… and it Works!

Human Observer

NPWEi

NPW

NPWEi

NPW

NPWEi

NPW

Cluttered BackgroundLarge Sphere Small Sphere

Cube vs. Sphere Encapsulated Sphere


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Taking Image Science to Task:

Applications

J. H. Siewerdsen (Johns Hopkins University) Dose (mGy)

Det

ecta

bilit

y In

dex

(d’)

Detectability versus Dose: Tomosynthesis and Cone-Beam CT

10-5 10-4 10-3 10-2 0.1 1 10

40o

90o

150oθtot = 180o

10o

Det

ecta

bilit

y In

dex

(d’) Anatomical noise dominates over

quantum noise at high dose.

Including anatomical noise is essential to meaningful performance modeling.

Increasing dose

Improved detectability≠

Application: Dose ReductionApplication: Dose Reduction

Quantum Noise Limited

Anatomical Noise Limited


Application: Orbital Extent

For a given dose (Nproj), identify the minimum orbital extent to achieve a given detectability.

For a given total orbit, identify minimum Nproj (dose) to achieve a given detectability.

Non-monotonic behavior (optima) reveals tradeoffs in:

- Anatomical Noise- Quantum Noise- View aliasing


0.00

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1 2 3Magnification

SF’=0

SF’=0.5

SF’=0.95

Det

ecta

bilit

y In

dex Gaussian Detection

atask = 1mm

William Beaumont Hospital

SDD = 55 cm

SAD = 100 cm

M* = 1.55

*

CBCT on a radiation therapy linac

Application: IG Radiation Therapy


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Application: IG Radiation TherapyHead-and-Neck Lung

Liver Metastases

Sarcoma

Prostate

kV CBCT ImagingMV Treatment Beam


Multiple projection images acquired over ~180o

2D Image acquisition- Nominal: 60 s- High-speed: 10-20 s

3D Image reconstruction- Nominal: 60 s- High-speed : 10 s

Radiation dose- ~1/10th that of Dx CT

Application: Mobile C-Arm for IGI


Mobile Isocentric C-ArmCone-Beam CT-Capable C-Arm

Pre-clinical platform for multi-mode Fluoro / CBCT guidance

Image Acquisition3D Reconstruction

Control System


Orthopedics

Spine

Head and Neck Ear

LungBrachytherapy

Cone-Beam CT-Guided Interventions


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CBCT-Guided Spine Surgery

needle tip


Facial Nerve

C-Spine

Cochlea Stapes Crura

Image-Guided Head and Neck Surgery


ChondrosarcomaTumorresection

Tumormargins

Scan 1 Scan 2

Image-Guided Skull Base Surgery


Intra-Operative CBCT

TARGET volumeNORMAL volume

Critical



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Intra-Operative CBCT

TARGET volumeNORMAL volume

Post-Operative CBCT

TARGET RemainingNORMAL Remaining

Critical Critical

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Unguided(conventional)

1-Specificity(Fraction of Normal Excised)

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Imaging Physics Approach to Technology Development- Understanding physical factors that govern image quality- Key to optimization and accelerated translation

Ongoing Work in CBCT Image Quality- X-ray scatter and patient motion- Detector readout speed, modes, and electronic noise- Statistical reconstruction methods (+prior information)

Translation of CBCT to “Specialty” Imaging Applications- Image-guided radiation therapy

Integration with adaptive therapy delivery processNovel fractionation / dose escalation schedules

- Image-guided surgeryVascular and nonvascular interventionsIntegration of navigation subsystems

- Pervasive questions in various applicationsCT or not CT? – drives a host of regulatory issuesTechnology assessment: IQ, dose, clinical performance

From Image Science to IGI


Thank You!I-STAR Laboratory

J. Web Stayman, W. ZbijewskiSebastian Schafer, Paul De Jean

Yoshi Otake, Junghoon LeeSajendra Nithiananthan, Ali Uneri

Daniel Mirota, Prakhar PrakashDaniel Tward, Grace Gang

Hopkins CollaboratorsD. Reh (Head and Neck Surgery)

G. Gallia (Neurosurgery)J. Khanna (Spine Surgery)J. Carrino (MSK Radiology)

J. Wong (Radiation Oncology)R. Taylor (Computer Science)G. Hager (Computer Science)

J. Prince (Electrical Engineering)

Funding SupportNational Institutes of Health

Siemens HealthcareCarestream Health


volume ct with flat-panel detectors: j. prince (johns...

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