IEEE Access (Jan 2019)

Toward Quantitative Near Infrared Brain Functional Imaging: Lock-In Photon Counting Instrumentation Combined With Tomographic Reconstruction

  • Dongyuan Liu,
  • Bingyuan Wang,
  • Tiantian Pan,
  • Jiao Li,
  • Zhuanping Qin,
  • Limin Zhang,
  • Zhongxing Zhou,
  • Feng Gao

DOI
https://doi.org/10.1109/ACCESS.2019.2924710
Journal volume & issue
Vol. 7
pp. 86829 – 86842

Abstract

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It is well acknowledged that the functional near-infrared spectroscopic (fNIRS) imaging of brain functions can be quantitatively enhanced by diffuse optical tomography (DOT). Unlike the widely-used conventional optical topography (optical mapping), an fNIRS-DOT instrument needs to possess both high sensitivity and large dynamic-range so that measurements at multiple source-detector separations are reliably and synchronously collected for improved depth-resolution, quantitation, and superficial signal reduction, the performances particularly crucial to the occipital cortex probing. For the goal, we implemented a novel 3-wavelength, 240-channel (20 sources and 12 detectors) continuous-wave fNIRS-DOT instrument. The system features the square-wave modulation lock-in photon-counting scheme that combines the multi-channel parallelization of the lock-in detection, the ultra-high sensitivity, and unprecedented dynamic-range of the photon-counting technique, as well as the reduced complexity of the hardware architecture. We have systematically investigated the key specifications of the instrument such as the detection linearity, stability, channel crosstalks and so on, under the practically-viable conditions of the incident power and signal-to-noise ratio, and accordingly proposed wavelength-switched multiple-field illumination paradigms for both the non-overlapping and overlapping DOT measurements, respectively. Finally, DOT experiments using the semi-three-dimensional image reconstruction has been conducted on a phantom with a practically low-contrasted and deep-sit absorption target, to evaluate the overall imaging performance of the system and to demonstrate the superiority of the model-based image reconstruction over the topographical mapping.

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