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Functional Imaging with Diffuse Optical Tomography

Functional Imaging with Diffuse Optical Tomography. Mark Elliott, PhD Associate Director of CMROI, Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA. Overview. Mechanisms of functional imaging with NIR light Methodology of fNIR

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Functional Imaging with Diffuse Optical Tomography

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  1. Functional Imaging with Diffuse Optical Tomography Mark Elliott, PhD Associate Director of CMROI, Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA

  2. Overview • Mechanisms of functional imaging with NIR light • Methodology of fNIR • Comparison with and without Difuse Optical Tomography (DOT)

  3. electrical activity - excitatory - inhibitory - soma action potential electrophysiology Methods for Imaging Neural Activity metabolic response FDG PET - ATP tightly regulated - glucose consumption - oxygen consumption H215O PET hemodynamic response - blood flow fNIR - blood volume - blood oxygenation fMRI EEG MEG Perfusion MRI

  4. Vascular Sensitivity offMRI and fNIR Arterial Venous II I fNIR Intravascular Perfusion MRI II IV fMRI III I Extravascular III IV Vessel Size

  5. Vascular Response fMRI vs fNIR

  6. Mechanisms of fNIR:Overview • fNIR = functional Near InfraRed • Measure changes in infrared light absorption and scattering • Primary source of signal contrast  [Hb] and [Hb0] • Biological tissue is highly scattering in NIR window • Primarily used in vivo as a spectroscopic modality • Not used to produce true images • DOT = Diffuse Optical Tomography • Methods for accurate image reconstruction

  7. Mechanisms of fNIR:Absortion of [Hb] and [Hb0] Water Absorption • Near infrared “window” ~650-900 nm • Water absorption is mimized • Hemoglobin species are dominant absorbers [Hb] & [HbO] Absorption

  8. Mechanisms of fNIR:Beer-Lambert Law Beer-Lambert law models ballistic photon propagation in absorbing media Transmittance, T = I/Io Absorbance, A = -log(I/Io) Beer-Lambert Law: A =  [X] d where: d = distance between I0 and I  = absorptivity (M-1 cm-1) [X] = concentration of absorber (M) d Io I solution[X]

  9. Source Detector Detector Fat photon path Muscle Mechanisms of fNIR:Modified Beer-Lambert Law Photons travelling through biological tissue are highly scattered (not ballistic) Scattering adds to “pathlength” travelled by photons d shallow deeper Modified Beer-Lambert Law: ( A = -log(I/Io) =  [X] d DPF +G where: DPF = differential pathlength factor G = Scattering loss factor (generally unknown) Source-detector spacing influences depth penetration

  10. Mechanisms of fNIR:Measures Changes in [Absorber] • Scattering factor, G, is unknown • Absolute concentrations are not derivable • Can measure changes in [Hb] & [HbO] • Need baseline assumption or independent measure of [Hb] Measure [Absorber] A2–A1 = -log(I2/I1) =  [X] d DPF where: A2,A1 = absorption measured at two time points

  11. fNIR Methodology: Tissue Penetration • NIR light penetration into biological tissue allows for surface imaging • Penetration increases with source light intensity • Limits on safe levels of source light intensity (~1mW/mm2) • SNR  sqrt(Io) • Highly sensitive detectors (PMTs) allow 2-6 cm deep probing

  12. fNIR Methodology:Quantitation of Multiple Chromophores Multiple absorbers ([Hb], [Hb0]) multiple wavelengths Extension of MBLL to multiple absorbers: (MBLL): A1 = (Hb 1[Hb] + HbO1[HbO]) d DPF A2 = (Hb 2[Hb] + HbO2[HbO]) d DPF 1 2 3 Source illumination is time or frequency multiplexed at several wavelengths.

  13. fNIR Methodology:Temporal Resolution • Extremely high temporal resolution possible • Practical systems ~ 10 – 100 Hz • fMRI ~ 1-2 Hz • Hemodynamic changes are slow ~ 2-5 sec • Fiber-optic systems for simultaneous fMRI • Fast signal – cell conformation and swelling • Scattering changes > 10 Hz • Extremely low signal • Ellusive to date from Strangeman Biol Psych 2002

  14. fNIR Methodology:Spatial Localization • Discrete arrays of sources and detectors • # voxels = # sources  # detectors • Typical systems  10 – 100 voxels • Poorly localized “blobograms” • Resolution  1-8 cm3 • Surface FOV • Compare to low-res fMRI: 64x64x30  217 voxels! • Whole brain coverage from Franceshini, NeuroImage, 2004

  15. fNIR Methodology:Spatial Localization with DOT • True tomographic methods ~ 10,000 S-D pairs • Flying spot illumation • CCD detection • Low temporal resolution ~10 – 100 sec / image • ill suited for functional assessment • “Hitting Density”,  – poor basis set • undetermined inversion problem (r) A = (r) (r) dr  = Hb[Hb] + HbO[HbO]) from Strangeman Biol Psych 2002

  16. fNIR Methodology:MBLL vs DOT • Many fNIR implementations report [Hb] changes from individual S-D pairs w/o attempt at DOT • DPF in MBLL calculated from uniform background absorption and scattering. Focal changes not properly modelled. • “MBLL and DOT results did not agree in terms of absolute magnitudes, relative magnitudes, or even the relative sign for changes in [HbO] and [Hb].” (Boas, NeuroImage, 2001)

  17. Spatial Maps of HRF Metrics:TTP Maps

  18. fMRI: Mental Chronometry ADC compartmentalization resolves events separated by 125ms. TTP Map 1 second right fovea & auditory delay

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