Spatiotemporal imaging of vascular reactivity

Representative results from simulated, laboratory and physiological studies are presented, demonstrating the ability to extract important features of dynamic behavior from dense scattering media. These results were obtained by analyzing a time series of image data. Investigations on the human forearm clearly reveal the ability to identify and correctly locate principal features of the vasculature. Characterization of these features using linear and nonlinear time-series analysis methods can produce a wealth of information regarding the spatio-temporal features of the dynamics of vascular reactivity.

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

[2]  N Stergiopulos,et al.  Arterial vasomotion: effect of flow and evidence of nonlinear dynamics. , 1998, American journal of physiology. Heart and circulatory physiology.

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

[4]  T. Griffith,et al.  Chaos and Fractals in Vascular Biology , 1994 .

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

[6]  James G. Fujimoto,et al.  Advances in Optical Imaging and Photon Migration , 1996 .

[7]  Mingzhou Ding,et al.  Estimating correlation dimension from a chaotic time series: when does plateau onset occur? , 1993 .

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

[9]  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.

[10]  J. Folkman Angiogenesis and breast cancer. , 1994, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[11]  James Theiler,et al.  On the evidence for how-dimensional chaos in an epileptic electroencephalogram , 1995 .

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

[13]  H. Kantz,et al.  Nonlinear time series analysis , 1997 .

[14]  J. Brobeck,et al.  Best and Taylorʼs Physiological Basis of Medical Practice , 1977 .

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

[16]  J. Clairambault,et al.  Linear and non-linear analyses of heart rate variability: a minireview. , 1996, Cardiovascular research.

[17]  秦 浩起,et al.  Characterization of Strange Attractor (カオスとその周辺(基研長期研究会報告)) , 1987 .

[18]  J. Rhee,et al.  Implication of Blood Flow in Hyperthermic Treatment of Tumors , 1984, IEEE Transactions on Biomedical Engineering.