Imaging of vascular chaos

Dynamic processes in biology are often controlled by multiple parameters that interact in a complex nonlinear fashion. Increasingly, evidence has accumulated that such behavior exhibits the property of sensitivity to initial conditions, a feature exhibited by chaotic systems. One such system is the vasculature. In this report, we present what we believe is the first experimental demonstration ever of imaging chaotic behavior of the vasculature in a large tissue structure (i.e., the human forearm). Supporting these findings are results from numerical simulation demonstrating our ability to image and correctly characterize complex dynamic behavior in dense scattering media that experience spatiotemporally coincident variations in hemodynamic states.

[1]  Harry L. Graber,et al.  Imaging of spatio-temporal coincident states by dynamic optical tomography , 2001, SPIE BiOS.

[2]  Theiler,et al.  Generating surrogate data for time series with several simultaneously measured variables. , 1994, Physical review letters.

[3]  K. Briggs An improved method for estimating Liapunov exponents of chaotic time series , 1990 .

[4]  Ulrich Parlitz,et al.  Identification of True and Spurious Lyapunov Exponents from Time Series , 1992 .

[5]  Harry L. Graber,et al.  Spatiotemporal imaging of vascular reactivity , 2000, Medical Imaging.

[6]  Ott,et al.  Experimental observation of a strange nonchaotic attractor. , 1990, Physical review letters.

[7]  S Zhong,et al.  Instrumentation and calibration protocol for imaging dynamic features in dense-scattering media by optical tomography. , 2000, Applied optics.

[8]  Theiler,et al.  Spurious dimension from correlation algorithms applied to limited time-series data. , 1986, Physical review. A, General physics.

[9]  O. W. van Assendelft,et al.  Spectrophotometry of haemoglobin derivatives , 1970 .

[10]  Harry L. Graber,et al.  Spatio – Temporal Imaging of Vascular Reactivity by Optical Tomography , 2022 .

[11]  C H Schmitz,et al.  Optical tomographic imaging of dynamic features of dense-scattering media. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[12]  Yu. A. Kuznetsov,et al.  Applied nonlinear dynamics: Analytical, computational, and experimental methods , 1996 .

[13]  T. Griffith,et al.  Temporal chaos in the microcirculation. , 1996, Cardiovascular research.

[14]  Harry L. Graber,et al.  Clinical applications of dynamic optical tomography in vascular disease , 2001, SPIE BiOS.

[15]  M. Rosenstein,et al.  A practical method for calculating largest Lyapunov exponents from small data sets , 1993 .

[16]  S Nioka,et al.  Time-resolved spectroscopy of hemoglobin and myoglobin in resting and ischemic muscle. , 1988, Analytical biochemistry.

[17]  Harry L. Graber,et al.  Normalized-constraint method for minimizing interparameter cross-talk in reconstructed images of spatially heterogeneous scattering and absorption coefficients , 2001, SPIE BiOS.

[18]  P. Grassberger,et al.  Measuring the Strangeness of Strange Attractors , 1983 .

[19]  Joseph M. Lasker,et al.  Performance characteristics of silicon photodiode (SiPD)-based instrument for fast functional optical tomography , 2001, SPIE BiOS.

[20]  B. Tromberg,et al.  Non-invasive measurements of breast tissue optical properties using frequency-domain photon migration. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[21]  C. Honig,et al.  Minimum intracellular PO2 for maximum cytochrome turnover in red muscle in situ. , 1987, The American journal of physiology.

[22]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[23]  D Righi,et al.  Is there any relationship between cold-induced vasodilatation and vasomotion? , 1999, Microvascular research.

[24]  L. Glass,et al.  From Clocks to Chaos: The Rhythms of Life , 1988 .