Diffuse Optics:fundamentail y tissue applications

8/9/2019 Diffuse Optics:fundamentail y tissue applications

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U n i v e r s i t y o f P e n n s y l v a n i a

Diffuse Optics: Fundamentals& Tissue Applications

Diffuse Optics: Fundamentals

& Tissue ApplicationsArjun G. Yodh

Department of Physics & Astronomy

University of Pennsylvania

Acknowledgement: NIH, ARMY






The Dream.The Dream.

from: Minority Report

from: Star Trek




In-Vivo Optical BiopsyIn-Vivo Optical Biopsy

• Near Infrared Light

Penetrates Tissue

• Sensitivity to TissuePhysiology

• Unique Contrasts are

Complementary to OtherMedical Diagnostics

• Non-invasive, safe,rapid, portable,continuous,inexpensive ...




Imaging & MonitoringImaging & Monitoring


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Clinical ScenariosClinical Scenarios

• Stroke detection and monitoring

• Cancer Imaging and Diagnosis

• Cancer Therapy monitoring

• Mitochondial diseases

• Epilepsy• Brain Activation

• Muscle Activation

(Peripheral Vascular Disease)





Light TransportLight Transport

• How are photons lost from the

incident light beam?

E0, I0 ?



Absorption (linear response)Absorption (linear response)

μa = Absorption Coefficientμa = [Absorber Concentration] ε (λ)

ExtinctionCoefficient LightWavelength

L

It = I0 e -μ

aLI0



Absorption InformationAbsorption Information

I0 It = I0 e

L

-μaL

• What molecules are present?

(Hemoglobin, water, lipids, …)

• What are their concentrations?

• What is their local environment?

(spectral shifts & broadening)



Scattering (single scattering limit)Scattering (single scattering limit)

μs = Scattering Coefficientμs = [Scatterer Concentration]σs (λ)

ScatteringCross-section LightWavelength

I0

Is (θ)

LIt = I0 e

-μsLθ



Scattering (single scattering limit)Scattering (single scattering limit)

I0

It = I0 e

Is (θ)

L

-μsL

θ

μs’ = reduced scattering coefficient = μs (1-g)

(μs’)-1= photon random walk step length

σd (θ) = Differential Scattering Cross-section

σs = σd (θ) dΩ

Is (θ) = σd (θ) I0

σd (θ) cos (θ) dΩ)(g = anisotropy factor = σs

1= ⟨cos (θ)⟩)



Scattering InformationScattering Information

• What are the scatterers?(particles, organelles, cells, cell-networks)

• What are scatterer concentrations?• What is their local environment?

(surrounding fluids)

I0

It = I0 e

Is (θ)

L

-μsL

θ



Scattering: Temporal FluctuationsScattering: Temporal Fluctuations

• What is moving?

(organelles, red blood cells, …)• How much is moving, how fast &

what is the manner of motion?

(Blood flow)

time



Traditional Optical TechniquesTraditional Optical Techniques

• Rigorous• Tested

I0

L

It = I0 e -(μs Lθ

μa+ )



OutlineOutline

• Brief Introduction/Motivation• Light Transport

• Single Scattering

• Multiple Scattering (Linear Transport & Diffusion Equations)• Solutions (Homogeneous Turbid Media)• Solutions (‘Simple’ Heterogeneous Turbid Media)• Image Reconstruction

• Temporal Fluctuations: Diffuse Correlation Transport• Biomedical Motivations Revisited• Background on Hemodynamics• Oxygen Metabolism

• Validation of the Techniques• In-Vivo Biomedical Applications (recent)• Breast• Brain• Cancer Therapy Monitoring

• Summary/Future/Acknowledgements



Problem of Tissue: Multiple ScatteringProblem of Tissue: Multiple Scattering





Transport Theory: Convective Time DerivativeTransport Theory: Convective Time Derivative

dr



μt = μa + μs

dr

SourcesRadiance

Scattered into Ω

Transport Theory: Microscopic Sources & SinksTransport Theory: Microscopic Sources & Sinks

Absorption &

Scattering Losses



Linear Transport EquationLinear Transport Equationdr

μt = μa + μs



Photon Fluence Rate & FluxPhoton Fluence Rate & Flux

Fluence rate (W/cm2

)

Flux (W/cm2)



PN ApproximationPN Approximation



Fluence & Flux in PN ApproximationFluence & Flux in PN Approximation




Radiance in the P1 Approximation (N=1)Radiance in the P1 Approximation (N=1)

Substitute P1 form of L into the linear transport equation.




Photon Diffusion EquationPhoton Diffusion Equation




Photon Diffusion Equation: AssumptionsPhoton Diffusion Equation: Assumptions

• Scattering length much smaller

than absorption length

• Fluence rate much greater than

Flux (radiance is largely isotropic)

• Isotropic sources(breaks down close to fiber sources)

•

•

(ω << υ μs’ )




Photon Diffusion Equation: AssumptionsPhoton Diffusion Equation: Assumptions

• OK for Tissues

• Scattering (on average)Independent of Incident Direction.

• Tissue Measurements are NOT PrecisionMeasurements .

( s ’)-1

~ 1 mm

( s )-1 ~ 0.01 - 0.1 mm

( a )-1 ~ 2 - 10 cm

υ( s ’) ~ 300 MHz




OutlineOutline



• Multiple Scattering (Linear Transport & Diffusion Equations)• Solutions (Homogeneous Turbid Media)• Solutions (‘Simple’ Heterogeneous Turbid Media)• Image Reconstruction

• Temporal Fluctuations: Diffuse Correlation Transport• Biomedical Motivations Revisited• Background on Hemodynamics• Oxygen Metabolism

• Validation of the Techniques• In-Vivo Biomedical Applications (recent)• Breast• Brain• Cancer Therapy Monitoring





Ideal SolutionsIdeal Solutions

• Infinitehomogeneous

turbid media• Point sources




Frequency Domain: Diffuse

Photon Density Waves*

Frequency Domain: Diffuse

Photon Density Waves*

*first suggested by Enrico Gratton




Frequency Domain: Point

Sources & Green’s Functions



= Green’s Function Solution.

for arbitrary source distribution

If ,








• Point Source at the Origin in Infinite Homogeneous Media

• Diffuse Photon Density Waves

• Frequency Dispersion




Diffusive Wave OpticsDiffusive Wave Optics

Boas, Oleary, Chance, Yodh. Physical Review E , 47(5) 1993.

Oleary, Boas, Chance, Yodh. Physical Review Letters, 69 1992.




Time Domain SolutionTime Domain Solution

Time Resolved Reflectance and Transmittance for The Noninvasive

Measurement of Tissue Optical-Properties, Patterson, MS, Chance, B,Wilson, BC, Applied Optics 28, 1989




What has been gained?What has been gained?

• Can separate scattering from

absorption.• Can measure absorption in turbid

media.

• Can measure scattering (photon

random walk step) in turbid media.

What about heterogeneous media?




OutlineOutline



• Multiple Scattering (Linear Transport & Diffusion Equations)• Solutions (Homogeneous Turbid Media)• Solutions (‘Simple’ Heterogeneous Turbid Media)• Image Reconstruction• Temporal Fluctuations: Diffuse Correlation Transport

• Biomedical Motivations Revisited• Background on Hemodynamics• Oxygen Metabolism

• Validation of the Techniques• In-Vivo Biomedical Applications (recent)

• Breast• Brain• Cancer Therapy Monitoring





Boundary Conditions: Semi-infinite MediaBoundary Conditions: Semi-infinite Media

• e.g. Air-Tissue Boundary

• Fiber Source Changed to Displaced Point Source

(l t ~ ( s ’)-1

)




Boundary Conditions: Semi-infinite MediaBoundary Conditions: Semi-infinite Media

• R( ) is a Fresnel Coefficient




Semi-infinite Media: Partial-flux Boundary ConditionSemi-infinite Media: Partial-flux Boundary Condition

• R eff depends on indices of refraction (easily calculated)

• Ls approximately ( s ’)-1




Extrapolated Zero-boundary ConditionExtrapolated Zero-boundary Condition

≈

≈




Solutions: Semi-infinite MediumSolutions: Semi-infinite Medium

• Method of images




Solutions: Semi-infinite MediumSolutions: Semi-infinite Medium

Danen, R.M., Wang, Y., Li, X.D., Thayer, W.S., and Yodh, A.G., Photochemistry and Photobiology.67, 33-40 (1998)

ρ2




Solutions: Slab MediumSolutions: Slab Medium




Spectroscopy: Absorption Coefficients vs.Spectroscopy: Absorption Coefficients vs.

THC =

Total Hemoglobin Concentration = [HbO2] + [Hb] = THC

Tissue Oxygen Saturation = [HbO2] / THC = StO2




OutlineOutline











Image ReconstructionImage Reconstruction

Arridge SR, Optical tomography in medical imaging, Inverse Problems 15, R41-R93, 1999




Image ReconstructionImage Reconstruction

= D0 + Δ D

(Born)

(Rytov)




Basic Scattering Theory (Example)Basic Scattering Theory (Example)

Δ D = 0

Green’s Function

>> , Incident wave, Green’s function ~

,




Inverting the DataInverting the Data

Discretize the Integral




Inverting the Data (one-step)Inverting the Data (one-step)

[φ] = W [δμa] (Set of linear equations)

[δμa]= W

-1[φ]

Principles of Computerized Tomographic Imaging by Avinash C. Kak, Malcolm Slaney




Inverting the Data (iteratively)Inverting the Data (iteratively)




3D Image Reconstruction3D Image ReconstructionFinite difference forward calculation, parallel processor implementation.

Culver, J.P., Choe, R., Holboke, M.J., Zubkov, L., Durduran, T., Slemp, A., Ntziachristos,V., Pattanayak, D.N., Chance, B., and Yodh, A.G., Medical Physics 30, 235-247 (2003)




OutlineOutline











(Single) Dynamic Light Scattering(Single) Dynamic Light Scattering

s




Correlation Transport EquationCorrelation Transport Equation

~ ⟨E*(r,t+τ) E(r,t)⟩

B. J. Ackerson, R. L. Dougherty, N. M. Reguigui, and U.Nobbman, "Correlation transfer: application of radiative transfer

solution methods to photon correlation problems," J. Thermophys. Heat Transfer 6, 577–588 (1992).

R. L. Dougherty, B. J. Ackerson, N. M. Reguigui, F. Dorri-Nowkoorani, and U. Nobbmann, "Correlation transfer:

development and application," J. Quant. Spectrosc. Radiat. Transfer. 52, 713–727 (1994).




P1 Approximation (Again)P1 Approximation (Again)

Correlation Diffusion Equation

is Light Diffusion Coefficient.

D. A. Boas, L. E. Campbell, and A. G. Yodh, Phys. Rev. Lett. 75, 1855–1858 (1995).

Differential Form of Diffusing-Wave Spectroscopy (DWS)

G. Maret and P. E. Wolf, Z. Phys. B 65, 409–413 (1987); D. J. Pine, D. A. Weitz,P. M. Chaikin, and E. Herbolzheimer, Phys. Rev. Lett. 60, 1134–1137 (1988).

α

α = fraction of scatterers that move.




Remainder Analysis Formally Same

as Photon Diffusion Equation

Remainder Analysis Formally Same

as Photon Diffusion Equation

• Solutions ~ ,

• Diffuse Correlation Imaging &

Spectroscopy

(k 0)2 α

3




Measurements of Blood FlowMeasurements of Blood Flow




Blood Flow Index (BFI)Blood Flow Index (BFI)

⟨Δr 2 (τ)⟩ ~ D b τ

rBFI = relative blood flow change

α = fraction of scatterers moving

D b = effective diffusion constant

αD b = BFI

Γ gives α⟨Δr 2 (τ)⟩




OutlineOutline











Sensitivity to Tissue PhysiologySensitivity to Tissue Physiology

1. Absorption Variations [μa(λ )]

- Access to tissue chromophore concentrations

- Hemoglobin Concentration (Hb), Blood Volume

- Blood Oxygen Saturation (HbO2/[Hb + HbO2])- Water, Lipids

2. Exogenous Contrast Agents

- Absorption Contrast, Drugs,… [μa

(λ )]

- Fluorescence [c], τlifetime

- Uptake & Clearance [μa(λ )], [c(t)]

3. Scattering Variations [μs,(λ )]

- Organelle Concentrations (mitochondria,…)- Background fluids, n(λ ,t).

4. Motions of Scatterers [⟨Δr2(τ)⟩], Γ, BFI

- Average Blood Flow Density

- Brownian Dynamics




Circulatory

System

Circulatory

System

Images from Human

Physiology by Vander,Sherman and Luciano,

Chapter 13.




Circulatory SystemCirculatory System

At any given time, some of theHemoglobin carried in the red

blood cells is oxygenated (HbO2)and some is deoxygenated (Hb).

Images from Human

Physiology by Vander,Sherman and Luciano,

Chapter 13.




Oxygen ExchangeOxygen Exchange

• 98% of Oxygen in Blood isbound reversibly to hemoglobin.

• O2 (dissolved gas) + Hb

HbO2

• “Blood Volume/Concentration”: [Hb] + [HbO2]

• Blood Oxygen Saturation (SO2): [HbO2] / ([Hb] + [HbO2] )




Hypoxia: Deficiency of Oxygen at Tissue LevelHypoxia: Deficiency of Oxygen at Tissue Level

• Arterial Oxygen too low.

• Blood flow too slow (ischemic hypoxia).

• Local Tissue metabolism too large.

O2

IN O2

OUT

O2

OUT

(Metabolism)

Arterioles Venules

Tissues




Clinical Scenarios (revisited)Clinical Scenarios (revisited)

• Stroke detection and monitoring

• Cancer Imaging and Diagnosis

• Cancer Therapy monitoring

• Mitochondial diseases

• Epilepsy

• Brain Activation

• Muscle Activation

(Peripheral Vascular Disease)

[Hb] , [HbO2] , THC, StO2 , BFI , rBFI




Cerebral Oxygen Metabolism: CMRO2Cerebral Oxygen Metabolism: CMRO2

from DOS/NIRS from DCS




OutlineOutline











DOS: Oxyhemoglobin Dissociation CurveDOS: Oxyhemoglobin Dissociation Curve

Mouse erythrocytes in tissue

phantom over the course of

phantom deoxygenation.

Diffuse optics get oxygen saturation (SO2).

Oxygen electrodes get pO2.

Wang, H.-W., Putt, M.E., Emanuele, M.J., Shin, D.E.,Glatstein, E., Yodh, A.G., and Busch, T.M.,

Treatment-induced changes in tumor oxygenation

predict photodynamic therapy outcome. Cancer

Research 64, 7553-7561 (2004)




Validation of DCSValidation of DCS

• against ASL-MRI

• against Xenon-CT

• against Transcranial Doppler Ultrasound

• against Color Doppler Ultrasound

• against Fluorescent Microspheres

• against Laser Doppler• by comparison to Literature

• in Phantoms

DCS has been validated:




Validating DCS Across Spatial Scales in Brain




DCS vs Laser Doppler: Rat Brain (3cm)

Hypocapnia byHyperventilation.

(Flow decreases

during activation

period.)

Li D d Pi l ( )




Live vs Dead Piglet (25 cm)Live vs Dead Piglet (25 cm)

Chao Zhou, Stephanie A. Eucker, Turgut Durduran, Guoqiang Yu, Jill Ralston, Stuart H.

Friess, Rebecca N. Ichord, Susan S. Margulies, and Arjun G. Yodh. Journal of BiomedicalOptics, 14(3):034015, 2009.

DCS Fl t Mi h




DCS vs Fluorescent Microspheres:

Neonatal Piglet Brain (25 cm)Flow decrease

measured versus

time after ~200Radian/sec

rotational head

injury to mimictraumatic brain

injury in babies.

H i (Wh l B i R )




Hypercapnia (Whole Brain Response)Hypercapnia (Whole Brain Response)

Two-layer model

(S )H i (S l R )




Hypercapnia (Scalp Response)Hypercapnia (Scalp Response)

Small (if any) scalp flow change detected during measurement!

DCS V lid ti ith X CT




DCS Validation with Xenon-CTDCS Validation with Xenon-CT

with Kofke, Levine, Grady, Detre, Greenberg

E l P iE l P ti t




Example PatientExample Patient

DCS X CT B d Sid C i




DCS vs Xenon-CT: Bed-Side ComparisonDCS vs Xenon-CT: Bed-Side Comparison

Good correlation, good agreementwith Kofke, Levine, Grady, Detre, Greenberg

O tliO tli




OutlineOutline





• Validation of the Techniques

• In-Vivo Biomedical Applications (recent)• Breast• Brain• Cancer Therapy Monitoring


Diff O i l T h f BDiff O ti l T h f B t




Diffuse Optical Tomography of BreastDiffuse Optical Tomography of Breast

Regine Choe, Soren D. Konecky, Alper Corlu, Kijoon Lee, Turgut Durduran, David R. Busch, Saurav Pathak, Brian J. Czerniecki,

Julia Tchou, Douglas L. Fraker, Angela DeMichele, Britton Chance, Simon R. Arridge, Martin Schweiger, Joseph P. Culver,

Mitchell D. Schnall, Mary E. Putt, Mark A. Rosen, and Arjun G. Yodh, Journal of Biomedical Optics, 14(2):024020, 2009.

P i l i h f DOT i B CP t ti l i h f DOT i B t C




Potential niches for DOT in Breast CancerPotential niches for DOT in Breast Cancer

P ll l Pl DOT I t tP ll l Pl DOT I t t




Parallel-Plane DOT InstrumentParallel-Plane DOT Instrument

Culver, Choe, Holboke, Zubkov, Durduran, Slemp, Ntziachristos, Chance, Yodh, Medical Physics 30 2003

3D Diff O ti l T h3D Diff se Optical Tomograph




3D Diffuse Optical Tomography3D Diffuse Optical Tomography

I i D t l C iInvasive Ductal Carcinoma




Invasive Ductal CarcinomaInvasive Ductal Carcinoma

• 53-year-old post-menopausal female, 2.2 cm invasive ductal carcinoma

C t & I i D t l C iCyst & Invasive Ductal Carcinoma




Cyst & Invasive Ductal CarcinomaCyst & Invasive Ductal Carcinoma

• 47-year-old pre-menopausal female, 6 cm cyst & 1.3 cm invasive ductal carcinoma

E l M li t B iExample: Malignant vs Benign




Example: Malignant vs BenignExample: Malignant vs Benign

Region of Interest

Optical IndexrTHC

Malignant: Invasive Ductal CarcinomaRegion of Interest

Benign: Fibroadenoma

MRI axial slice rTHC Optical Index

rStO 2

rStO 2

MRI axial slice

T /N l E d C t t (N 51)Tumor/Normal Endogenous Contrast (N 51)




Tumor/Normal Endogenous Contrast (N=51)Tumor/Normal Endogenous Contrast (N=51)

(A) Benign,(B) Malignant measured before core biopsy, (C) Malignant measured after core biopsy

OutlineOutline




OutlineOutline








Functional Activation In BrainFunctional Activation In Brain










THC = Total Hemoglobin Concentration

StO2 = Blood Oxygen SaturationrBF = Relative Blood Flow

CMRO2 = Rate of Cerebral Oxygen Metabolism

Motor Stimulus: OpticalMotor Stimulus: Optical









Durduran, T., Yu, G., Burnett, M.G., Detre,

J.A., Greenberg, J.H., Wang, J., Zhou, C.,

and Yodh, A.G., Diffuse optical

measurement of blood flow, blood

oxygenation and metabolism in human

brain during sensorimotor cortex activation.

Optics Letters 29, 1766-1768 (2004).


Population Average (n 5)Population Average (n=5)




Population Average (n=5)Population Average (n=5)

Durduran, Yu, Burnett, Detre, Greenberg, Wang, Zhou, Yodh, Optics Letters, 2004

Clinic: Relevant Cerebral PhysiologyClinic: Relevant Cerebral Physiology




Clinic: Relevant Cerebral PhysiologyClinic: Relevant Cerebral Physiology

ICP

MAP

CPP = MAP - ICP

Cerebral Blood Flow AutoregulationCerebral Blood Flow Autoregulation




Cerebral Blood Flow AutoregulationCerebral Blood Flow Autoregulation

CPP = MAP - ICP

Intracranial Pressure (ICP) MonitoringIntracranial Pressure (ICP) Monitoring




Intracranial Pressure (ICP) MonitoringIntracranial Pressure (ICP) Monitoring

Other CBF Monitoring SchemesOther CBF Monitoring Schemes




Other CBF Monitoring SchemesOther CBF Monitoring Schemes

• Xenon – CT

• Arterial-Spin-Labeled MRI (ASL-MRI)

• Transcranial Doppler Ultrasound (TCD)

Opportunities for OpticsOpportunities for Optics




Opportunities for OpticsOpportunities for Optics

• Continuous CBF monitoring at the

bedside.

• Direct measurement of Tissue

Microvasculature.

• Combine with NIRS/DOS to get

cerebral metabolism.

Acute Ischemic Stroke Study ProtocolAcute Ischemic Stroke Study Protocol




Acute Ischemic Stroke Study ProtocolAcute Ischemic Stroke Study Protocol

Turgut Durduran, Chao Zhou, Brian L. Edlow,

Guoqiang Yu, Regine Choe, Meeri N. Kim, Brett L.

Cucchiara, Mary E. Putt, Qaisar Shah, Scott E. Kasner,

Joel H. Greenberg, Arjun G. Yodh, and John A. Detre,

Opt. Express, 17(5):3884-3902, 2009.



Cerebral Blood Flow vs Head of Bed Angle:Cerebral Blood Flow vs Head of Bed Angle:




Cerebral Blood Flow vs. Head of Bed Angle:

Healthy Subjects vs. Stroke Patients

Cerebral Blood Flow vs. Head of Bed Angle:

Healthy Subjects vs. Stroke Patients

Paradoxical ResponseParadoxical Response

Injured hemisphere doesnInjured hemisphere doesn’’tt autoregulateautoregulate..

ResultsResults




ResultsResults

• HOB position was found to be a significant factor in both

hemispheres (healthy and stroke groups).

• HOB was a stronger factor in the infarcted area which also

showed a larger variation (stroke group).

• “Paradoxical Response” (25% of stroke group): the maximal

CBF occurred at an elevated angle. Therefore, standard clinical

practice of “HOB flat” might not be optimal for all stroke

patients.

Turgut Durduran, Chao Zhou, Brian L. Edlow, Guoqiang Yu, Regine Choe, Meeri N. Kim, Brett L. Cucchiara, Mary E. Putt,Qaisar Shah, Scott E. Kasner, Joel H. Greenberg, Arjun G. Yodh, and John A. Detre,Opt. Express, 17(5):3884-3902, 2009.

OutlineOutline




OutlineOutline


• Single Scattering• Multiple Scattering (Linear Transport & Diffusion Equations)• Solutions (Homogeneous Turbid Media)• Solutions (‘Simple’ Heterogeneous Turbid Media)• Image Reconstruction• Temporal Fluctuations: Diffuse Correlation Transport





Tumor Therapy MonitoringTumor Therapy Monitoring




Tumor Therapy MonitoringTumor Therapy Monitoring

NeoadjuvantNeoadjuvant chemotherapychemotherapy

Choe, Corlu, Lee, Durduran, Konecky, Grosicka-Koptyra, Arridge, Czerniecki,Fraker, DeMichele, Chance, Rosen, Yodh, Medical Physics, 32, 2005.

Photodynamic TherapyPhotodynamic Therapy




Photodynamic TherapyPhotodynamic Therapy

Laser

Injection of

photosensi tizer

Illuminated

by light

Photo-

activa ted

drug

induced

single t

oxygen

destroys

tum or

Tumor

Abs

o

r pt i on

F l u or esc

enc

e

S1 Intersystem

Crossing

T1

Excited

Trip let Type I

Type I I

1

O 2

O 2 -

Single t

3

O 2

Diffuse Optical Measurements of TumorDiffuse Optical Measurements of Tumor




Diffuse Optical Measurements of Tumor

Response Before, During & After PDT

pResponse Before, During & After PDT

Measurement ProtocolMeasurement Protocol




Measurement ProtocolMeasurement Protocol

Radiation-Induced Fibrosarcoma (RIF) mice tumors

Control group = light (135J/cm2 at 75 mW/cm2)

Treated group = light + Photofrin (5 mg/Kg)Treatment efficacy

Days after PDT for tumor growth to a volume of 400 mm3

(starting volume ~100 mm3)

Before/After PDTBefore/After PDT




Before/After PDTBefore/After PDT

Significant decreases in blood flow and oxygen saturation

Responses During PDTResponses During PDT




Responses During PDTResponses During PDT

Large slope → Poor treatment efficacy

Yu, Durduran, Zhou, Wang, Putt, Saunders, Sehgal, Glatstein, Yodh, Busch,Clinical Cancer Research 11, (2005)

Oxygenation Response Just After PDTOxygenation Response Just After PDT




Oxygenation Response Just After PDTOxygenation Response Just After PDT

(n = 12)

Low relative - SO2 immediately after PDT → Poor treatment efficacy

Wang, Putt, Emanuele, Shin, Glatstein, Yodh, Busch, Cancer Research 64, (2004)

Summary/FutureSummary/Future




• Diffuse Optics Probes Physiology of

Deep Tissues.

• Breast Tumors, Brain, Head & Neck

Tumors, Muscle ...

• Animal Model Research (Pre-clinical)






• Image Reconstruction (large data sets)

• Image/Data Processing(composite indices, automated segmentation)

• Flow plus Oxygen gives Metabolism

• Contrast Agents (fluorescence)

• Multi-modal Imaging & Diagnosis• Near Surface (skin)

• Dosimetry• Microscopic Origins of Signals(molecular, tissue level)

• Identify New Applications


CollaboratorsCollaborators



PhD Students & Post-docs

Collaborators

Boas, David

Cheung, Cecil

Cheung,Rex

Corlu, Alper

Culver, Joseph

Danen, RobertFisher, Jonathan A. N.

Giammarco, Joe

Gonatas, Dinos

Ripoll, Jorge

Slemp, Alison

Solonenko, Michael

Sunar, Ulas

Vulcan, Teodor

Wang, Hsing-WenYu, Guoqiang

Zhou, Chao

Zubkov, Leonid

Simon Arridge, University College London, UK

Larry Campbell, Hobart & Williams College

Mark Burnett, University of Pennsylvania

Theresa Busch, University of Pennsylvania

Britton Chance, University of Pennsylvania

Brian Czerniecki, University of Pennsylvania

Angela DeMichele, University of Pennsylvania

John Detre, University of PennsylvaniaJared Finlay, University of Pennsylvania (HUP)

Tom Floyd, University of Pennsylvania

Doug Fraker, University of Pennsylvania

Joe Friedberg, University of Pennsylvania

Eli Glatstein University of Pennsylvania

Chandrakala (Kala) Menon, University of Pennsylvania

Emile Mohler III, University of Pennsylvania

Shoko Nioka, Johnson Foundation, Penn/HUP

Deva Pattanayak, Vishay Intertechnology Inc.

Mary Putt, University of Pennsylvania

Harry Quon, University of Pennsylvania

Nimi Ramanujam, Duke University

Robert (Bob) Rogers, University of DelawareMark Rosen, University of Pennsylvania

Mitch Schnall, University of Pennsylvania

Martin Schwieger, University College London, UK

Chandra (Sandy) Sehgal, University of Pennsylvania

Bruce Tromberg University of California at Irvine

Baker, Wes

Ban, Han Yong

Buckley, Erin

Busch, David

Kim, Meeri

Xing, XiaomanChoe, Regine

Durduran, Turgut

Patak, Saurav

Holboke, Monica

Intes, Xavier

Konecky, Sore

Lee, Kijoon

Li, Xingde

Liu, HanliMeglinsky, Igor

Ntziachristos, Vasilis

O'Leary, Maureen

Senior Collaborators

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Diffuse Optics:fundamentail y tissue applications