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 Applications

Arjun G. YodhDepartment of Physics & Astronomy

University of Pennsylvania

Acknowledgement: NIH, ARMY

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

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

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

The Dream.The Dream.

from: Minority Report

from: Star Trek

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

In-Vivo Optical BiopsyIn-Vivo Optical Biopsy• Near Infrared Light

Penetrates Tissue• Sensitivity to Tissue

Physiology• Unique Contrasts are

Complementary to Other Medical Diagnostics

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

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

Imaging & MonitoringImaging & Monitoring

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

Clinical Scenarios Clinical Scenarios

• Stroke detection and monitoring• Cancer Imaging and Diagnosis• Cancer Therapy monitoring• Mitochondial diseases• Epilepsy• Brain Activation• Muscle Activation

(Peripheral Vascular Disease)

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

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

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

Light TransportLight Transport

• How are photons lost from the incident light beam?

E0, I0 ?

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

Absorption (linear response)Absorption (linear response)

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

ExtinctionCoefficient

LightWavelength

L

It = I0 e -μaLI0

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

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)

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

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

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

ScatteringCross-section

LightWavelength

I0

Is (θ)

L

It = I0 e -μsLθ

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

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

I0It = 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)⟩)

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

Scattering InformationScattering Information

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

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

(surrounding fluids)

I0It = I0 e

Is (θ)

L

-μsL

θ

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

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

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

Traditional Optical TechniquesTraditional Optical Techniques

• Rigorous• Tested

I0

L

It = I0 e -(μs Lθ

μa+ )

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

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

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

Problem of Tissue: Multiple ScatteringProblem of Tissue: Multiple Scattering

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

Linear Transport TheoryLinear Transport Theory

= Radiance [W/(cm2 sr)]

Power/Area-Angle at traveling in direction .

L ~ ⟨E*(r,t) E(r,t)⟩

Radiance is balanced in each small volume of medium.

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

Transport Theory: Convective Time DerivativeTransport Theory: Convective Time Derivative

dr

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

μt = μa + μs

dr

SourcesRadianceScattered into Ω

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

Absorption &Scattering Losses

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

Linear Transport EquationLinear Transport Equationdr

μt = μa + μs

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

Photon Fluence Rate & FluxPhoton Fluence Rate & Flux

Fluence rate (W/cm2)

Flux (W/cm2)

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

PN ApproximationPN Approximation

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

Fluence & Flux in PN ApproximationFluence & Flux in PN Approximation

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

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

Substitute P1 form of L into the linear transport equation.

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

Photon Diffusion EquationPhoton Diffusion Equation

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

Photon Diffusion Equation: AssumptionsPhoton Diffusion Equation: Assumptions

• Scattering length much smallerthan absorption length

• Fluence rate much greater thanFlux (radiance is largely isotropic)

• Isotropic sources(breaks down close to fiber sources)

•

•

(ω << υ μs’ )

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

Photon Diffusion Equation: AssumptionsPhoton Diffusion Equation: Assumptions

• OK for Tissues

• Scattering (on average)Independent of Incident Direction.

• Tissue Measurements are NOT Precision Measurements.

(μs’)-1 ~ 1 mm

(μs)-1 ~ 0.01 - 0.1 mm

(μa)-1 ~ 2 - 10 cm

υ(μs’) ~ 300 MHz

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

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

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

Ideal SolutionsIdeal Solutions

• Infinite homogeneous turbid media

• Point sources

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

Frequency Domain: Diffuse Photon Density Waves*

Frequency Domain: Diffuse Photon Density Waves*

*first suggested by Enrico Gratton

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

Frequency Domain: Point Sources & Green’s Functions

Frequency Domain: Point Sources & Green’s Functions

= Green’s Function Solution.

for arbitrary source distribution

If ,

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

Frequency Domain: Point Sources & Green’s Functions

Frequency Domain: Point Sources & Green’s Functions

• Point Source at the Origin in Infinite Homogeneous Media• Diffuse Photon Density Waves• Frequency Dispersion

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

Diffusive Wave OpticsDiffusive Wave Optics

Boas, Oleary, Chance, Yodh. Physical Review E, 47(5) 1993.Oleary, Boas, Chance, Yodh. Physical Review Letters, 69 1992.

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

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

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

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?

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

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

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

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

• e.g. Air-Tissue Boundary• Fiber Source Changed to Displaced Point Source

(lt ~ (μs’)-1 )

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

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

• R(θ) is a Fresnel Coefficient

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

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

• Reff depends on indices of refraction (easily calculated)• Ls approximately (μs’)-1

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

Extrapolated Zero-boundary ConditionExtrapolated Zero-boundary Condition

≈

≈

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

Solutions: Semi-infinite MediumSolutions: Semi-infinite Medium

• Method of images

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

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

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

Solutions: Slab MediumSolutions: Slab Medium

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

Spectroscopy: Absorption Coefficients vs. λSpectroscopy: Absorption Coefficients vs. λ

THC =

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

Tissue Oxygen Saturation = [HbO2] / THC = StO2

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

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

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

Image ReconstructionImage Reconstruction

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

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

Image ReconstructionImage Reconstruction

= D0 + ΔD

(Born)

(Rytov)

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

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

ΔD = 0

Green’s Function

>> , Incident wave, Green’s function ~

,

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

Inverting the DataInverting the Data

Discretize the Integral

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

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

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

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

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

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)

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

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

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

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

s

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

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).

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

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.

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

Remainder Analysis Formally Same as Photon Diffusion Equation

Remainder Analysis Formally Same as Photon Diffusion Equation

• Solutions ~ ,

• Diffuse Correlation Imaging & Spectroscopy

(k0)2 α3

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

Measurements of Blood FlowMeasurements of Blood Flow

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

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

⟨Δr2 (τ)⟩ ~ Db τ

rBFI = relative blood flow change

α = fraction of scatterers moving

Db = effective diffusion constant

αDb = BFI

Γ gives α⟨Δr2 (τ)⟩

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

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

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

Sensitivity to Tissue PhysiologySensitivity to Tissue Physiology1. 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

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

Circulatory System

Circulatory System

Images from Human Physiology by Vander, Sherman and Luciano, Chapter 13.

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

Circulatory SystemCirculatory System

At any given time, some of the Hemoglobin 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.

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

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] )

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

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

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

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

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

Cerebral Oxygen Metabolism: CMRO2Cerebral Oxygen Metabolism: CMRO2

from DOS/NIRS from DCS

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

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

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

DOS: Oxyhemoglobin Dissociation CurveDOS: Oxyhemoglobin Dissociation Curve

Mouse erythrocytes in tissue phantom over the course ofphantom 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)

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

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:

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

Validating DCS Across Spatial Scales in Brain

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

DCS vs Laser Doppler: Rat Brain (3cm)

Hypocapnia byHyperventilation.(Flow decreases during activation period.)

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

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 Biomedical Optics, 14(3):034015, 2009.

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

DCS vs Fluorescent Microspheres: Neonatal Piglet Brain (25 cm)

Flow decrease measured versus time after ~200 Radian/sec rotational head injury to mimic traumatic brain injury in babies.

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

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

Two-layer model

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

Hypercapnia (Scalp Response)Hypercapnia (Scalp Response)

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

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

DCS Validation with Xenon-CTDCS Validation with Xenon-CT

with Kofke, Levine, Grady, Detre, Greenberg

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

Example PatientExample Patient

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

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

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

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

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

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 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.

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

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

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

Parallel-Plane DOT InstrumentParallel-Plane DOT Instrument

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

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

3D Diffuse Optical Tomography3D Diffuse Optical Tomography

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

Invasive Ductal CarcinomaInvasive Ductal Carcinoma

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

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

Cyst & Invasive Ductal CarcinomaCyst & Invasive Ductal Carcinoma

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

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

Example: Malignant vs BenignExample: Malignant vs Benign

Region of Interest

Optical IndexrTHC

Malignant: Invasive Ductal CarcinomaRegion of Interest

Benign: FibroadenomaMRI axial slice rTHC Optical Index

rStO 2

rStO 2

MRI axial slice

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

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

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

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

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

Functional Activation In BrainFunctional Activation In Brain

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

Functional Activation In BrainFunctional Activation In Brain

THC = Total Hemoglobin ConcentrationStO2 = Blood Oxygen SaturationrBF = Relative Blood FlowCMRO2 = Rate of Cerebral Oxygen Metabolism

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

Motor Stimulus: OpticalMotor Stimulus: Optical

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

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).

Motor Stimulus: OpticalMotor Stimulus: Optical

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

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

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

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

Clinic: Relevant Cerebral PhysiologyClinic: Relevant Cerebral Physiology

ICP

MAP

CPP = MAP - ICP

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

Cerebral Blood Flow AutoregulationCerebral Blood Flow Autoregulation

CPP = MAP - ICP

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

Intracranial Pressure (ICP) MonitoringIntracranial Pressure (ICP) Monitoring

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

Other CBF Monitoring SchemesOther CBF Monitoring Schemes

• Xenon – CT

• Arterial-Spin-Labeled MRI (ASL-MRI)

• Transcranial Doppler Ultrasound (TCD)

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

Opportunities for OpticsOpportunities for Optics

• Continuous CBF monitoring at the bedside.

• Direct measurement of Tissue Microvasculature.

• Combine with NIRS/DOS to get cerebral metabolism.

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

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.

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

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

Common ResponseCommon Response

Injured hemisphere doesnInjured hemisphere doesn’’t t autoregulateautoregulate..

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

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’’t t autoregulateautoregulate..

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

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.

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

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

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

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.

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

Photodynamic TherapyPhotodynamic Therapy

Laser

Injection ofphotosensi tizer

Illuminatedby light

Photo-activa ted

druginducedsingle toxygen

destroystum or

Tumor

Abs

o

rption

Fluoresc

enc

e

S1 IntersystemCrossing

T1

ExcitedTrip let

Type I

Type I I

1O2

O2-

Single t

3O2

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 Optical Measurements of Tumor Response Before, During & After PDT

Diffuse Optical Measurements of Tumor Response Before, During & After PDT

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

Measurement ProtocolMeasurement Protocol

Radiation-Induced Fibrosarcoma (RIF) mice tumorsControl group = light (135J/cm2 at 75 mW/cm2)Treated group = light + Photofrin (5 mg/Kg)

Treatment efficacyDays after PDT for tumor growth to a volume of 400 mm3

(starting volume ~100 mm3)

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

Before/After PDTBefore/After PDT

Significant decreases in blood flow and oxygen saturation

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

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)

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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)

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 Probes Physiology of Deep Tissues.

• Breast Tumors, Brain, Head & Neck

Tumors, Muscle ...

• Animal Model Research (Pre-clinical)

Summary/FutureSummary/Future

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

• 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

Summary/FutureSummary/Future

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

PhD Students & Post-docs

CollaboratorsCollaborators

Boas, DavidCheung, CecilCheung,RexCorlu, AlperCulver, JosephDanen, RobertFisher, Jonathan A. N.Giammarco, JoeGonatas, Dinos

Ripoll, Jorge Slemp, AlisonSolonenko, MichaelSunar, UlasVulcan, TeodorWang, Hsing-WenYu, GuoqiangZhou, ChaoZubkov, Leonid

Simon Arridge, University College London, UKLarry Campbell, Hobart & Williams CollegeMark Burnett, University of PennsylvaniaTheresa Busch, University of PennsylvaniaBritton Chance, University of PennsylvaniaBrian Czerniecki, University of PennsylvaniaAngela DeMichele, University of PennsylvaniaJohn Detre, University of PennsylvaniaJared Finlay, University of Pennsylvania (HUP)Tom Floyd, University of PennsylvaniaDoug Fraker, University of Pennsylvania Joe Friedberg, University of PennsylvaniaEli Glatstein, University of PennsylvaniaJoel Greenberg, University of PennsylvaniaSteve Hahn, University of Pennsylvania Daniel Licht, Children's Hospital of Philadelphia (CHOP)

Chandrakala (Kala) Menon, University of PennsylvaniaEmile Mohler III, University of PennsylvaniaShoko Nioka, Johnson Foundation, Penn/HUPDeva Pattanayak, Vishay Intertechnology Inc.Mary Putt, University of PennsylvaniaHarry Quon, University of PennsylvaniaNimi Ramanujam, Duke UniversityRobert (Bob) Rogers, University of DelawareMark Rosen, University of Pennsylvania Mitch Schnall, University of Pennsylvania Martin Schwieger, University College London, UKChandra (Sandy) Sehgal, University of Pennsylvania Bruce Tromberg, University of California at IrvineQing Zhu, University of Connecticut Tim Zhu, University of Pennsylvania

Baker, Wes Ban, Han YongBuckley, ErinBusch, DavidKim, MeeriXing, XiaomanChoe, RegineDurduran, TurgutPatak, Saurav

Holboke, MonicaIntes, XavierKonecky, SoreLee, KijoonLi, XingdeLiu, HanliMeglinsky, IgorNtziachristos, VasilisO'Leary, Maureen

Senior Collaborators