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