1. Hemodynamically Constrained Dynamic Diffuse Optical Tomography Under Mammographic Compression Eleonora Vidolova 1, Dana Brooks 1, Eric Miller 2, Stefan Carp 3, David Boas 3 1. Northeastern University; 2. Tufts University; 3. Massachusetts General Hospital; 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 R01- CA97305 and 54-CA105480. This work is a continuation of the work done by Dibo Ntuba on her Masters degree project. Hemoglobin Time Curves 1. Introduction DOT taken along x-ray mammogram provide valuable functional information. 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]. Blood flow, oxygen saturation(SO2), water and lipid distributions could specify the degree of malignancy of a tumor. Physiological changes in the breast due to mammographic compression are significant → should be taken into account[2]. References 4. Future Work 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 Compare reconstruction results to the constant HbT model used before Analyze how small perturbations in the reconstructed HbO and HbR affect the oxygen consumption and blood flow reconstructions 2. Forward Model and Simulation Data Invert data using Tikhonov regularization Parameter Fitting - Oxygen Consumption and Blood Flow Reconstruction 3. Reconstruction (2) Assumptions: SO2 = mean (SaO 2, SvO2) SO2 =HbO/HbT, with initial condition, SO 2,init HbT tissue = a+bt (Clinical data suggest that HbT changes over time, previous research assumed HbT constant [1,2]) Generated data for background and tumor regions using equation (2), representing 90s of mammographic compression at 6 lbs Mass balance of HbO within a volume gives [2], Where, SaO2 and SvO2 are arterial and venous oxygen saturation; OC is oxygen consumption, F is blood flow and V is volume. Factor of 4 accounts for 4 O2 molecules bound to each Hb molecule. (1) Added electronic noise, modeled as i.i.d Gaussian random variables. Simulations done in PMI toolbox developed at MGH. 0.98Arterial blood oxygen saturation, SaO 2 700 µmolTotal blood hemoglobin, HbTbl 0.000275 L/L/sBackground blood flow, 0.85 (85%)Initial tumor oxygen saturation, SO 2,init 0.7 (70%)Initial background oxygen saturation, SO 2.init a = 18 µmol/L b = a/600 Total tissue hemoglobin 10% change over 60sec. 0.0006875 L/L/sTumor blood flow, 0.672 µmol/L/sTumor OC 0.448 µmol/L/sBackground OC ValueParameter Hemodynamic model describing the relation between hemoglobin content, OC, SO2 and F in the breast during mammographic compression. OC and F could become novel breast cancer optical markers [2]. Indirect method: First reconstruct hemoglobin content and then OC and F. Forward Data OC and Flow reconstruction when no noise used in data generation OC and Flow reconstruction when 60dB SNR used in data generation |SO2original – SO2ranging| 0.5 1 0 0.00015 0.0003 OC F OC reconstruction if we use the background flow value (60dB SNR for generated data) OC reconstruction if we use the tumor flow value (60dB SNR for generated data) Use the reconstructed SO2 data do differentiate between regions and use that information when fitting for oxygen consumption Fitting of OC and Flow is very dependent on the SNR of the generated data If fitting for OC only we get a better fit if we know which region we are in 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).