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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
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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
The Dream.The Dream.
from: Minority Report
from: Star Trek
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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
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 ...
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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
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)
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Light TransportLight Transport
• How are photons lost from the
incident light beam?
E0, I0 ?
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Absorption (linear response)Absorption (linear response)
μa = Absorption Coefficientμa = [Absorber Concentration] ε (λ)
ExtinctionCoefficient LightWavelength
L
It = I0 e -μ
aLI0
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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)
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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
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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)⟩)
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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
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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
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Traditional Optical TechniquesTraditional Optical Techniques
• Rigorous• Tested
I0
L
It = I0 e -(μs Lθ
μa+ )
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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
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Problem of Tissue: Multiple ScatteringProblem of Tissue: Multiple Scattering
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Transport Theory: Convective Time DerivativeTransport Theory: Convective Time Derivative
dr
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μt = μa + μs
dr
SourcesRadiance
Scattered into Ω
Transport Theory: Microscopic Sources & SinksTransport Theory: Microscopic Sources & Sinks
Absorption &
Scattering Losses
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Linear Transport EquationLinear Transport Equationdr
μt = μa + μs
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Photon Fluence Rate & FluxPhoton Fluence Rate & Flux
Fluence rate (W/cm2
)
Flux (W/cm2)
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PN ApproximationPN Approximation
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Fluence & Flux in PN ApproximationFluence & Flux in PN Approximation
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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
Radiance in the P1 Approximation (N=1)Radiance in the P1 Approximation (N=1)
Substitute P1 form of L into the linear transport equation.
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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
Photon Diffusion EquationPhoton Diffusion Equation
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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
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’ )
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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
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
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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
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
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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
Ideal SolutionsIdeal Solutions
• Infinitehomogeneous
turbid media• Point sources
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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
Frequency Domain: Diffuse
Photon Density Waves*
Frequency Domain: Diffuse
Photon Density Waves*
*first suggested by Enrico Gratton
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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
Frequency Domain: Point
Sources & Green’s Functions
Frequency Domain: Point
Sources & Green’s Functions
= Green’s Function Solution.
for arbitrary source distribution
If ,
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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
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
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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
Diffusive Wave OpticsDiffusive Wave Optics
Boas, Oleary, Chance, Yodh. Physical Review E , 47(5) 1993.
Oleary, Boas, Chance, Yodh. Physical Review Letters, 69 1992.
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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
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
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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
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?
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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
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
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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
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
)
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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
Boundary Conditions: Semi-infinite MediaBoundary Conditions: Semi-infinite Media
• R( ) is a Fresnel Coefficient
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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
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
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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
Extrapolated Zero-boundary ConditionExtrapolated Zero-boundary Condition
≈
≈
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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
Solutions: Semi-infinite MediumSolutions: Semi-infinite Medium
• Method of images
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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
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
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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
Solutions: Slab MediumSolutions: Slab Medium
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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
Spectroscopy: Absorption Coefficients vs.Spectroscopy: Absorption Coefficients vs.
THC =
Total Hemoglobin Concentration = [HbO2] + [Hb] = THC
Tissue Oxygen Saturation = [HbO2] / THC = StO2
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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
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
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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
Image ReconstructionImage Reconstruction
Arridge SR, Optical tomography in medical imaging, Inverse Problems 15, R41-R93, 1999
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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
Image ReconstructionImage Reconstruction
= D0 + Δ D
(Born)
(Rytov)
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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
Basic Scattering Theory (Example)Basic Scattering Theory (Example)
Δ D = 0
Green’s Function
>> , Incident wave, Green’s function ~
,
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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
Inverting the DataInverting the Data
Discretize the Integral
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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
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
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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
Inverting the Data (iteratively)Inverting the Data (iteratively)
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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
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)
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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
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
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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
(Single) Dynamic Light Scattering(Single) Dynamic Light Scattering
s
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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
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).
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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
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.
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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
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
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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
Measurements of Blood FlowMeasurements of Blood Flow
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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
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)⟩
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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
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
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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
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
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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
Circulatory
System
Circulatory
System
Images from Human
Physiology by Vander,Sherman and Luciano,
Chapter 13.
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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
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.
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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
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] )
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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
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
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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
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
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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
Cerebral Oxygen Metabolism: CMRO2Cerebral Oxygen Metabolism: CMRO2
from DOS/NIRS from DCS
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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
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
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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
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)
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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
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:
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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
Validating DCS Across Spatial Scales in Brain
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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
DCS vs Laser Doppler: Rat Brain (3cm)
Hypocapnia byHyperventilation.
(Flow decreases
during activation
period.)
Li D d Pi l ( )
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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
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
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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
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 )
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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
Hypercapnia (Whole Brain Response)Hypercapnia (Whole Brain Response)
Two-layer model
(S )H i (S l R )
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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
Hypercapnia (Scalp Response)Hypercapnia (Scalp Response)
Small (if any) scalp flow change detected during measurement!
DCS V lid ti ith X CT
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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
DCS Validation with Xenon-CTDCS Validation with Xenon-CT
with Kofke, Levine, Grady, Detre, Greenberg
E l P iE l P ti t
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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
Example PatientExample Patient
DCS X CT B d Sid C i
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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
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
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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
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
Diff O i l T h f BDiff O ti l T h f B t
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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 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
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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
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
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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
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
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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
3D Diffuse Optical Tomography3D Diffuse Optical Tomography
I i D t l C iInvasive Ductal Carcinoma
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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
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
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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
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
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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
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)
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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
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
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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
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
Functional Activation In BrainFunctional Activation In Brain
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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
Functional Activation In BrainFunctional Activation In Brain
Functional Activation In BrainFunctional Activation In Brain
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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
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
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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
Motor Stimulus: OpticalMotor Stimulus: Optical
Motor Stimulus: OpticalMotor Stimulus: Optical
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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
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
Population Average (n 5)Population Average (n=5)
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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
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
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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
Clinic: Relevant Cerebral PhysiologyClinic: Relevant Cerebral Physiology
ICP
MAP
CPP = MAP - ICP
Cerebral Blood Flow AutoregulationCerebral Blood Flow Autoregulation
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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
Cerebral Blood Flow AutoregulationCerebral Blood Flow Autoregulation
CPP = MAP - ICP
Intracranial Pressure (ICP) MonitoringIntracranial Pressure (ICP) Monitoring
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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
Intracranial Pressure (ICP) MonitoringIntracranial Pressure (ICP) Monitoring
Other CBF Monitoring SchemesOther CBF Monitoring Schemes
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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
Other CBF Monitoring SchemesOther CBF Monitoring Schemes
• Xenon – CT
• Arterial-Spin-Labeled MRI (ASL-MRI)
• Transcranial Doppler Ultrasound (TCD)
Opportunities for OpticsOpportunities for Optics
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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
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
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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
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.
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Cerebral Blood Flow vs Head of Bed Angle:Cerebral Blood Flow vs Head of Bed Angle:
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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
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
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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
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
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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
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
Tumor Therapy MonitoringTumor Therapy Monitoring
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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
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
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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
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
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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 Optical Measurements of Tumor
Response Before, During & After PDT
pResponse Before, During & After PDT
Measurement ProtocolMeasurement Protocol
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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
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
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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
Before/After PDTBefore/After PDT
Significant decreases in blood flow and oxygen saturation
Responses During PDTResponses During PDT
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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
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
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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
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
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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 Probes Physiology of
Deep Tissues.
• Breast Tumors, Brain, Head & Neck
Tumors, Muscle ...
• Animal Model Research (Pre-clinical)
Summary/FutureSummary/Future
Summary/FutureSummary/Future
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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
• 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
CollaboratorsCollaborators
8/9/2019 Diffuse Optics:fundamentail y tissue applications
http://slidepdf.com/reader/full/diffuse-opticsfundamentail-y-tissue-applications 115/115
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