Skin Microcirculation Imaging

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LightHOUSE – Centre
for Photonics & Imaging
June 6 – 12, 2012 www.nbipireland.ie
BIGSS’12 –[email protected]
Graduate
Summer
School
Outline
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Microcirculation Imaging
Laser Doppler monitoring and imaging
Laser Speckle
Diffuse Reflectance (TiVi)
MI State of the Art
PAT
OCT
Correlation mapping OCT (cmOCT)
Why BioPhotonics?
Courtesy of Frank Chuang
Label-free Imaging
Domains
1000
Standard US
MRI, X-ray, CT
Ultrasound
High frequency US
LDPI
TiVi
www.nbipireland.ie/education
Resolution (mm)
LSPI
100
DOT
PET
Radionucleotide
10
Optical
Coherence
Tomography
1
PAT
Confocal
Microscopy
1
10
Sampling depth (mm)
100
Image from www.biophonticsWorld.com
Why microcirculation?
Ryan
Ryan, TJ, 1966 The microcirculation of the skin in
old age. Gerontologia Clinica
Motivation
• Microcirculation serves key functions within
the body:
– Exchange nutrients and metabolic waste to body
– Regulate body temperature.
– Regulate blood pressure.
• Structural changes associated with disease
– Diabetes
– Raynaud’s syndrome
– Cancer
Sound theoretical base
Bonner &
Nossal
Bonner and Nossal, 1981. Model for laser Doppler measurements
of blood flow in tissue, Appl. Opt. 20, 2097–2107.
Martin Leahy, DPhil
Director of R&D
Dai Chaplin, Ph.D.
Head of Research and Development
& Chief Scientific Officer
British Journal of Cancer 74, 260 – 263 (1996)
Laser Doppler and
Combretastatin
Microcirculation Techniques
Imaging techniques
Laser Doppler perfusion imaging (LDPI)
Doppler Optical coherence tomography (OCT)
Optical Microangiography (OMAG)
source (PAT) detector
Photoacoustic Tomography
Sequential raster scanning of tissue creating a colour coded “perfusion”
image of underlying vasculature
Example during brachial
Cannot monitor real-time changes in microvasculature
artery occlusion
skin surface
Leahy, M.J., de Mul, F.F.M, Nilsson, G.E., and Maniewski, R
Principles and Practice of the laser Doppler perfusion technique,
TECHNOLOGY AND HEALTH CARE, pp 143-162 7, 1999
Laser Speckle
• The line scanner generates
quite good images that look
like ordinary LDPI images - a
new image can be generated
every 10 second or faster.
• “the image acquisition times
are much shorter - 50 x 64
pixels in 5 seconds!”
• The FLPI unit generates realtime images (or close to real
time)
• What the images really
display? -
How TiVi Works
G
B
white light
CR image
Light detector
LP
1
2
LP
SR≈7%
melanin layer
DP
BS≈46%
epidermis
capillary loops
1,2 = polarisers LP= Linear Polarised DP= Depolarised SR = surface
reflection BS = backscattered CR = cross polarised
O’Doherty PhD Thesis. University of Limerick , 2007
R
TiVi Algorithm
TiViindex  k gain
Where:
Rd ( g )
Rd ( r )
8m ( )  (8ma ( ))
8ma ( ) 

R d ( )  1  a
 

2
2
3ms ( )  (3ms ( ))
3ms ( ) 
2
1
2
ma    RBC f ma RBC    1  RBC f maTISSUE 
ms    RBC f ms RBC    1  RBC f msTISSUE  
J. Biomed. Opt. 14 (3) 2009
2 ( maEPID ( g ) maEPID ( r )) x
Rd ( r )  ke
Skin Res and Tech – 13 (4) 2007
Kubelka-Munk theory facilitates an algorithm which is
sensitive to RBCs only (absorption changes are large
between red (λ ≈ 600 - 700 nm) and green (λ ≈ 500 - 600
nm) light. Light in surrounding dermal tissue is absorbed to
approximately the same amount in red and green
• Radial analysis
– Variable isodose diameter
– Minimum of 0.1 mm (LDPI = 1 mm)
ITC6
O’Doherty, J., et al., 2011 .Arch Derm Res (2010)
Minimal erythemal
dose
Swelling reduces TiVi index value
18% reduction in averaged value
of dashed box while the edges
increase
ITC6
J. Physiological Measurement 31 (11) N79-N83 (2010)
Mobile platform
J. Biophotonics 4 (5) 293-296.
Mobile platform
ITC6
Nokia
Photoacoustic Computed
Tomography
(1) Laser pulse (<ANSI limit:
e.g., 20 mJ/cm2)
(2) Light absorption &
heating (~ mK)
1 mK  8 mbar
= 800 Pa
(4) Ultrasonic detection
(optical scatter/1000)
Nature Biotech. 21, 803 (2003).
(3) Ultrasonic emission
(~ mbar)
Photoacoustic Microscopy
of Human Palm
Max amplitude projection
5
4
3
2
1
B-Scan @ 584 nm
Epiderm.-derm.
junction
Stratum corneum
1 mm
Epidermis
1
2
Dermis
3
4
Subpapillary plexus
5
1 mm
C. Favazza, unpublished.
Collaboration: L. Cornelius
• Correlation
mapping OCT
• 8 sequential
frames
• 2-D correlation
map average
correlation value
for a square grid
measuring 7x7
ITC6
Enfield, J. 2011 Biomedical Optics Express 2 (5) 1184-1193.
cmOCT
Jonathan et al. 2011 J. Biophotonics 4 (5)
cmOCT
Background
• Optical Coherence Tomography (OCT) is a technique that allows
imaging of highly scattering mediums with micron resolution.
• It is analogous to ultrasound except is uses reflections of light.
• The contrast mechanism is scattering.
10 mm x 10 mm x 3 mm
1 mm x 1 mm x 0.1 mm
OCT System
• All work has been performed on a
commercial OCT system (Thorlabs
OCS1300).
• Specifications:
–
–
–
–
–
Wavelength : 1325 nm
Axial Resolution : 9 µm (water)
Lateral Resolution : 25 µm
Ascan Rate : 16 kHz
Volume Capture (1024x1024x512) : 70-80 sec
Background
• Optical Coherence Tomography (OCT) can
visualize structural features within the skin
• Several Technologies have been developed
to enable extraction of flow information using
OCT.
– Doppler OCT (DOCT)
– Speckle variance OCT (svOCT)
– Optical Micro-angiography (OMAG)
• However each have associated limitations.
Background
• Recently a new technique has been developed
within the group for flow extraction from OCT
datasets.
• Based on correlation statistics and called “Cross
correlation OCT” or cmOCT.
• Addresses key issues with existing technologies
– Angle Dependence
– Speed of processing
Jonathan, E., Enfield, J. and Leahy, M. J. , “Correlation mapping method for generating microcirculation morphology
from optical coherence tomography (OCT) intensity images”. Journal of Biophotonics, Online Dec. 2010 .
doi: 10.1002/jbio.201000103
Principle of cmOCT
200 µm embedded capillary
tube with flowing fluid
Excised section of Pig Skin
Principle of cmOCT
• To compare the images, the correlation
between subsequent frames is
determined.
• Correlation provides a measure of the
similarity between datasets.
• Correlation values range from -1 to +1.
– Higher correlation indicates images are same
– Lower correlation indicates images are
different
Principle of cmOCT
Frame A
𝑀
𝑁
Correlation Image
Frame B
𝐹𝐴 𝑥 + 𝑝, 𝑦 + 𝑞 − 𝐹𝐴 (𝑥, 𝑦) 𝐹𝐵 𝑥 + 𝑝, 𝑦 + 𝑞 − 𝐹𝐵 (𝑥, 𝑦)
𝐶𝐶(𝑥, 𝑦) =
𝑝=0
𝑞=0
𝐹𝐴 𝑥 + 𝑝, 𝑦 + 𝑞 − 𝐹𝐴 (𝑥, 𝑦)
 Where M,N are the kernel size
2
+ 𝐹𝐵 𝑥 + 𝑝, 𝑦 + 𝑞 − 𝐹𝐵 (𝑥, 𝑦)
2
Principle of cmOCT
Frame A
𝑀
𝑁
Correlation Image
Frame B
𝐹𝐴 𝑥 + 𝑝, 𝑦 + 𝑞 − 𝐹𝐴 (𝑥, 𝑦) 𝐹𝐵 𝑥 + 𝑝, 𝑦 + 𝑞 − 𝐹𝐵 (𝑥, 𝑦)
𝐶𝐶(𝑥, 𝑦) =
𝑝=0
𝑞=0
𝐹𝐴 𝑥 + 𝑝, 𝑦 + 𝑞 − 𝐹𝐴 (𝑥, 𝑦)
 Where M,N are the kernel size
2
+ 𝐹𝐵 𝑥 + 𝑝, 𝑦 + 𝑞 − 𝐹𝐵 (𝑥, 𝑦)
2
Principle of cmOCT
Static region (>0.7)
Flow regions
(<0.2)
Cross
Correlation
Background
(<0.2)
-1
+1
Principle of cmOCT
Threshold and binarize image
Frame n
Correlation Image
Mask Image
Cross Correlation
Mask Image
+0.6
-0.6
Frame n+1
cmOCT image
-0.6
+0.6
Principle of cmOCT
• This cmOCT algorithm is capable of
extracting the flow information from the
OCT data.
Structural Image
Flow Image
Principle of cmOCT
• To generate 3D vessel maps, a spatial
separation between frames is required.
• If spatial separation is too high, correlation
is lost for static structure.
Principle of cmOCT
1
Static Region
Corellation
0.8
Flow Region
0.6
Axial resolution of lens
0.4
0.2
0
0
5
10
15 20 25 30 35
Frame Separation (µm)
40
45
• A frame separation of < 5 µm will provide
sufficient oversampling and is an
instrumentation limit.
50
Principle of cmOCT
• cmOCT has been implemented using
customised java code optimized for
multithread processors.
• Processing speed for volume of 1024 x 1024
x 512 voxels
– 3x3 kernel : 28 s
– 5x5 kernel : 71 s
– 7x7 kernel : 119 s
Multi-layered Phantom
• 3D phantom (3 x 3 x 3 mm)
• 200 µm capillary tubes
embedded in a static
scattering matrix.
• Tubes filled with intralipid
solution moving under
Brownian motion.
• Brownian motion can be
clearly seen.
In-vivo Human Results
• The technique is capable of mapping large regions of the tissue
2 mm
Jonathan, E. Enfield, J., and Leahy, M.J. 2010. Correlation mapping method for generating
microcirculation morphology from optical coherence tomography (OCT) intensity images. J.
Biophotonics (published online 17 December 2010).
http://onlinelibrary.wiley.com/doi/10.1002/jbio.201000103/abstract
In-vivo Human Results
• Limited results published on
in-vivo human imaging to
date.
2.5 mm
• OCT (gray) and cmOCT of
volar forearm is shown.
– Capture : 70 s
– Processing : 119 s (7x7 ker)
2.5 mm
• Clear vessel structure
extracted : What vessels
are seen?
Vascular Supply
Papillary Layer
Epidermis
Dermis
Sub-cutaneous
Capillaries/superficial
plexus (3-10 µm)
arterioles/venules
(12-35 µm)
arteries/veins
tissue
Image from : http://www.scf-online.com/
In-vivo Human Results
• To determine location depth
slices can be examined
In-vivo Human Results
In-vivo Human Results
Oral Mucosa Imaging
• The following images show preliminary results of
imaging the oral mucosa using correlation mapping
optical coherence tomography (cmOCT).
• Due to the system used, the lip was imaged.
• Two regions are shown
1. Oral Mucosa
2. Minor Salivary Gland
2
1
3D Rendering
• Oral mucosa sweat gland
Minor Salivary gland
Minor Salivary gland
Minor Salivary gland
Minor Salivary gland
Minor Salivary gland
Minor Salivary gland
Minor Salivary gland
Minor Salivary gland
Minor Salivary gland
Minor Salivary gland
Minor Salivary gland
• C-Scan for Structural OCT and cmOCT at a depth of 350 µm
•
cmOCT clearly provides a new contrast mechanism.
Human Wound Healing
Human Wound Healing
Human Wound Healing
1 mm
1 mm
Human Optical Clearing
Before
400 µm
2 cm
2 mm
In Vivo Human
Optical Clearing
T = 0 min
400 µm
2 cm
2 mm
In Vivo Human
Optical Clearing
T = 20 min
400 µm
2 cm
2 mm
In Vivo Human
Optical Clearing
T = 40 min
400 µm
2 cm
2 mm
In Vivo cmOCT and
Optical Clearing
Before Clearing
After Clearing
Region shown is a 3x3 mm region, before and after 40 min clearing
In Vivo cmOCT and
Optical Clearing
Before Clearing
After Clearing
Ex Vivo OCT and Optical Clearing
After 30 min clearing
Before Clearing
3 mm
3 mm
5 mm
5 mm
OCT Reflectance (AU)
1
Before Clearing
After Clearing
0.8
0.6
1.50 mm
0.4
2.15 mm
0.2
0
0.00
0.50
1.00
1.50
Position (mm)
2.00
2.50
3.00
In Vivo cmOCT and
Optical Clearing
Before Clearing
After Clearing
After Clearing
Before Clearing
OCT Signal (AU)
1
0.8
0.6
0.4
0.2
0
-200
0
200
400
600
800
1000
Depth (µm)
1200
1400
1600
1800
2000
In Vivo cmOCT and
Optical Clearing
• Using the model, the measured scattering coefficients (µs) are :
– Before : 9.40±0.31 mm -1
– After : 8.29±0.23 mm-1
• From before, the mean free path between scattering events
(ls)
• This indicates an increase by 13% occurs after clearing.
• There is thus an increased penetration depth of OCT signal
achieved.
Human Reactive
hyperaemia
1.5 mm
1.5 mm
Human Reactive
hyperaemia
• To improve this a 256x256
region can be acquired in 5 s.
• The scanning area is reduced
to 500x500 µm so a small
region of microcirculation is
imaged.
500 µm
500 µm
Human Reactive
hyperaemia
Depth Resolved RH
Summary
• MI at clinically relevant speeds and depths is
close
• cmOCT is a new power technology for flow
extraction from structural OCT images.
• The algorithm can be applied to any structural
OCT images to extract flow.
• The technique is still being developed and
enhanced, however the initial results show great
promise for microcirculation imaging.
NBIPI: Tissue Optics and Microcirculation Imaging Facility
University of Surrey
Dr Jim O’Doherty
Imperial College London
Dr Neil Clancy
Washington U., St. Louis
Prof. Lihong Wang
OHSU, Portland
Profs. Steve Jacques, Ricky Wang
University Hospital Linköping
Dr Chris Anderson
Joachim Henricson
Wheelsbridge AB
Prof Folke Sjöberg
Prof. Gert Nilsson
NUI Galway & U. Limerick
Prof Martin Leahy
Prof Valery Tuchin (adjunct)
Prof Terence Ryan (adjunct)
Dr Marie-Louise O’Connell
Dr Azhar Zam
Dr Hrebesh Subhash
Dr Sergey Alexandrov
Dr Joey Enfield
Paul McNamara
Dennis Warncke
Kate Lawlor
Olga Zhernovaya
Susan McElligott
Roshan Dsouza
Haroon Zafar
Gillian Lynch
August 28 - Sept 1 www.nbipireland.ie
BIGSS’12 –[email protected]
Graduate
Summer
School