Transcript Reconstructing oxygen consumption and blood flow in

Hemodynamically Constrained Dynamic Diffuse Optical Tomography Under
Mammographic Compression
Eleonora
1
Vidolova ,
Dana
1
Brooks ,
Eric
2
Miller ,
Stefan
3
Carp ,
David
3
Boas
1. Northeastern University; 2. Tufts University; 3. Massachusetts General Hospital;
1. Introduction
3. Reconstruction
• DOT taken along x-ray mammogram provide valuable functional
information.
Invert data using Tikhonov regularization
• Hemodynamic model describing the relation between
hemoglobin content, OC, SO2 and F in the breast during
mammographic compression.
• X-ray mammography only gives structural information → hard to
distinguish between benign and malignant masses.
• Oxygen consumption (OC) and blood flow (F) contrasts observed
between malignant and normal tissue[3, 4].
• OC and F could become novel breast cancer optical
markers [2].
• Blood flow, oxygen saturation(SO2), water and lipid distributions could
specify the degree of malignancy of a tumor.
• Indirect method: First reconstruct hemoglobin content and
then OC and F.
• Physiological changes in the breast due to mammographic
compression are significant → should be taken into account[2].
2. Forward Model and Simulation Data
• Generated data for background and tumor regions using equation (2),
representing 90s of mammographic compression at 6 lbs
Parameter
Value
Background OC
0.448 µmol/L/s
• Mass balance of HbO within a volume gives [2],
Tumor OC
0.672 µmol/L/s
Background blood flow,
Tumor blood flow,
Total tissue hemoglobin
0.000275 L/L/s
0.0006875 L/L/s
a = 18 µmol/L
b = a/600

Parameter Fitting - Oxygen Consumption and Blood Flow Reconstruction
10% change over 60sec.
• Factor of 4 accounts for 4 O2 molecules bound to each Hb molecule.
Assumptions:
• SO2 = mean (SaO2, SvO2)
SO2 =HbO/HbT, with initial condition, SO2,init
• HbTtissue = a+bt (Clinical data suggest that HbT changes over time,
previous research assumed HbT constant [1,2])
Total blood hemoglobin, HbTbl
700 µmol
Arterial blood oxygen saturation, 0.98
SaO2
Initial background oxygen
0.7 (70%)
saturation, SO2.init
Initial tumor oxygen saturation,
0.85 (85%)
SO2,init
0.35
|SO2original – SO2ranging|

d[HbO2 ]
OC
F
HbTblood S a,O2  S v,O2
=
+
(1)
dt
4Vtissue Vtissue
• Where, SaO2 and SvO2 are arterial and venous oxygen saturation; OC is
oxygen consumption, F is blood flow and V is volume.
(2)
0.3
0.25
0.2
0.15
0.1
0.05
0
0
1
120
OC and Flow reconstruction when no noise used in data generation
Simulation Geometry
Hemoglobin Time Curves
• 10X10X6 cm volume of
tissue, (infinite slab).
• Spherical absorption
inhomogeneity at
(2,2.25,3.25); radius 3 cm.
• Reduced scattering
coefficient fixed at 7cm-1.
OC and Flow reconstruction when 60dB SNR used in data generation
100
80
60
60
OC
0.5
40
20
20
•Transmissive geometry:
–16 sources at z=0 boundary;
4X4 array.
– 32 detectors at z=6cm; 2
4X4 arrays, offset by 1.5cm.
–Inter-source & inter-detector
separation of 3cm.
40
0.00015
80
0.0003
F
0
• Fitting of OC and Flow is very dependent on the SNR of
the generated data
• Two wavelengths – 690nm
and 830 nm. Continuous
wave.
• If fitting for OC only we get a better fit if we know which
region we are in
• 1024 measurements
(source-detector pairs) &
1452 voxels.
OC reconstruction if we use the tumor flow value
(60dB SNR for generated data)
OC reconstruction if we use the background flow value
(60dB SNR for generated data)
4. Future Work
Forward Data
• Added electronic noise, modeled as i.i.d Gaussian random variables.
• Simulations done in PMI toolbox developed at MGH.
Acknowledgements
• This work was supported by CenSSIS, under the Engineering
Research Centers Program of the National Science Foundation (Award
# EEC-9986821).
• D. Boas and S. Carp acknowledge support from NIH grants R01CA97305 and 54-CA105480.
• This work is a continuation of the work done by Dibo Ntuba on her
Masters degree project.
• Compare reconstruction results to the constant HbT model used
before
• Use the reconstructed SO2 data do differentiate between regions and
use that information when fitting for oxygen consumption
• Analyze how small perturbations in the reconstructed HbO and HbR
affect the oxygen consumption and blood flow reconstructions
References
1. D. T. Ntuba, S. A. Carp, G. Boverman, E. L. Miller, D. A. Boas, D. H. Brooks. “Reconstructing oxygen
consumption and blood flow in diffuse optical tomographic breast imaging under mammographic
compression.” CenSSIS RICC 2006 Student Poster Session.
2. S. A. Carp, T. Kauffman, Q. Fang, E. Rafferty, R. Moore, D. Kopans, D. Boas. “Compression-induced
changes in the physiological state of the breast as observed through frequency domain photon
migration measurements.” Journal of Biomedical Optics, Vol. 11(6), Nov./Dec. 2006
3. T. Durduran, R. Choe, G. Yu, C. Zhou, “Diffuse optical measurement of blood flow in breast tumors”, Optics
Letters 30(21), 2915-17 (2005).
4. R. Beaney, A. Lammertsma, T. Jones, C. Mckenzie, K. Halnan, “Positron emission tomography for in-vivo
measurement of regional blood flow, oxygen utilisation, and blood volume in patients with breast carcinoma”,
The Lancet, 1(8369), 131-134 (1984).