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Simplifying the hardware requirements for fast neural EIT of peripheral nerves.

Enrico RavagliSvetlana MastitskayaDavid S HolderKirill Y Aristovich
Published in: Physiological measurement (2022)
Objective . The main objective of this study was to assess the feasibility of lowering the hardware requirements for fast neural electrical impedance tomography (EIT) in order to support the distribution of this technique. Specifically, the feasibility of replacing the commercial modules present in the existing high-end setup with compact and cheap customized circuitry was assessed. Approach . Nerve EIT imaging was performed on rat sciatic nerves with both our standard ScouseTom setup and a customized version in which commercial benchtop current sources were replaced by custom circuitry. Electrophysiological data and images collected in the same experimental conditions with the two setups were compared. Data from the customized setup was subject to a down-sampling analysis to simulate the use of a recording module with lower specifications. Main results . Compound action potentials (573 ± 287 μ V and 487 ± 279 μ V, p =0.28) and impedance changes (36 ± 14 μ V and 31 ± 16 μ V, p =0.49) did not differ significantly when measured using commercial high-end current sources or our custom circuitry, respectively. Images reconstructed from both setups showed neglibile (<1voxel, i.e. 40 μ m) difference in peak location and a high degree of correlation ( R 2  = 0.97). When down-sampling from 24 to 16 bits ADC resolution and from 100 to 50 KHz sampling frequency, signal-to-noise ratio showed acceptable decrease (<-20%), and no meaningful image quality loss was detected (peak location difference <1voxel, pixel-by-pixel correlation R 2  = 0.99). Significance : The technology developed for this study greatly reduces the cost and size of a fast neural EIT setup without impacting quality and thus promotes the adoption of this technique by the neuroscience research community.
Keyphrases
  • image quality
  • electronic health record
  • deep learning
  • healthcare
  • computed tomography
  • drinking water
  • mental health
  • dual energy
  • magnetic resonance
  • big data
  • neuropathic pain
  • diffusion weighted imaging