The new platform, described in the open-access journal HardwareX by Jerry Febrico and Basari of Universitas Indonesia, is built around two inexpensive, widely available integrated circuits: the STM32F407VGT6 microcontroller and the AD5933 impedance converter. EIT works by injecting a small, safe alternating current into a subject or phantom through an array of surface electrodes and measuring the faint voltages that appear at the boundary. Because different tissues conduct electricity differently, the pattern of these boundary voltages encodes information about the internal distribution of conductivity, which a computer algorithm can then reconstruct into a cross-sectional image. For the lungs, this is particularly powerful: as air flows in and out during breathing, the local impedance of lung tissue changes dramatically, allowing EIT to track regional ventilation at the bedside without a single X-ray. Diseases such as chronic obstructive pulmonary disease and pulmonary edema, which demand repeated monitoring rather than one-off snapshots, are precisely the conditions where this continuous, non-ionizing approach holds the greatest clinical promise.

What distinguishes the new design from earlier open-source efforts is its deliberately modular analog front-end. The heart of the system is the AD5933, a chip that combines a direct digital synthesizer signal generator and a 12-bit impedance converter on a single die. In this implementation, the AD5933 is configured to output a sinusoidal excitation signal at 50 kilohertz, which is fed into a voltage-controlled current source that injects a constant current of roughly 1.44 milliamperes peak-to-peak into the imaging domain. On the receive side, weak differential voltages picked up from the electrodes are amplified by an instrumentation amplifier and routed back into the AD5933, whose on-chip digital signal processor applies a discrete Fourier transform to extract the real and imaginary components of the measured signal. The STM32 microcontroller orchestrates the entire sequence over an I2C bus, computes impedance magnitudes, and streams the results over USB to a personal computer running Python-based reconstruction software.

The modularity is not an abstract design philosophy; it is physically built into the circuit board. The single custom PCB, measuring 177.8 by 155.83 millimeters, carries two alternative voltage-controlled current source topologies, a Load-in-the-Loop design and a Mirrored Howland current pump, alongside two instrumentation amplifier options, a discrete INA118P chip and an AD620 module. Manual DIP switches on the board let researchers route the signal through any combination of these circuits, enabling direct, empirical comparison of four distinct analog front-end configurations under identical operating conditions. The board is assembled with IC sockets, female headers, and through-hole resistors specifically so that users can swap components, replace damaged parts, and experiment with their own circuit modifications without fabricating a new board. Every component is listed in a bill of materials with purchase links from global suppliers such as DigiKey and AliExpress, and the complete design files, from EasyEDA schematics to PCB Gerber files, are released under CERN-OHL, MIT, and CC BY 4.0 open-source licenses.

Data acquisition follows the classic adjacent four-terminal sensing pattern used across the EIT field. Four 16-channel CD74HC4067 analog multiplexers, steered by the microcontroller, dynamically connect the current source and the measurement amplifier to a ring of 16 stainless steel electrodes mounted on a 3D-printed polylactic acid container. Current is injected through one adjacent electrode pair while differential voltages are measured sequentially across all remaining neighboring pairs; the injection pair then rotates around the ring, and the process repeats. One complete sweep yields 208 impedance measurements, which the firmware collects in just two seconds. The authors tested alternative injection patterns, including opposite and diagonal schemes that push current deeper into the domain, but found in their comparative experiments that the adjacent pattern best preserved the shape and location of target inclusions, while cross and opposite patterns introduced excessive noise that obscured objects entirely.

Validation of the hardware was thorough and refreshingly candid. Signal-to-noise ratio measurements, taken across 50 consecutive frames in a saline-filled phantom with a conductivity of 966 millisiemens per meter, ranged from 21.5 to 43.7 decibels depending on the front-end configuration. The Mirrored Howland paired with the AD620 achieved the highest peak SNR of 43.7 decibels, a figure the authors note is comparable to the clinically validated Sheffield Mk 3.5 system, which operated at roughly 40 decibels. The Load-in-the-Loop configurations delivered more uniform channel-to-channel performance, around 37 to 38 decibels, because this topology does not depend on precisely matched resistor networks, whereas the Howland pump’s high output impedance degrades rapidly when built with standard 1 percent tolerance components. Precision testing with standard resistors from 100 to 1000 ohms revealed exceptionally tight repeatability, with standard deviations between 0.54 and 6.33 ohms across all configurations and coefficients of determination reaching as high as 0.9997.

Perhaps the most instructive finding concerns absolute accuracy. The system exhibited a systematic positive offset, producing relative errors that ranged from 0.60 percent to as much as 37.82 percent for the lowest resistor values. In most measurement contexts, such errors would be disqualifying. But EIT, as practiced here, relies on time-difference imaging: a baseline reference dataset is captured in a homogeneous medium, and images are reconstructed from the changes that occur relative to that baseline. Static hardware offsets, parasitic capacitances, and multiplexer channel resistances are mathematically subtracted during this differential process, which means repeatability, not absolute accuracy, is the metric that matters. By this standard, the platform performs admirably, and the authors are transparent that the fixed additive error, traced through linear regression to positive y-intercepts between 11.2 and 99.4 ohms, is effectively cancelled by the reconstruction algorithm.

Imaging experiments confirmed the system’s practical capability. Using cylindrical phantom targets, one highly conductive stainless steel tube and one non-conductive plastic tube, placed at various positions inside the saline tank, the team reconstructed 2D images using three algorithms available in the open-source pyEIT library: Back-Projection, the Jacobian-based JAC method, and GREIT. Back-Projection was favored for its speed and low computational cost. Quantitative comparison against simulated ground-truth images, scored with the Structural Similarity Index Measure and Root Mean Square Error, showed strong reconstruction fidelity, with the HOW-INA118 configuration achieving a peak SSIM of 0.9242 and the HOW-AD620 configuration delivering the lowest RMSE of 28.29. The reconstructed inclusions showed the characteristic boundary smearing and spatial deformation inherent to adjacent-drive EIT with linear reconstruction, particularly near the center of the domain, but the system reliably detected and localized both conductive and non-conductive anomalies across all four hardware configurations and all tested positions.

The team is explicit that this prototype is intended strictly for education and non-human phantom experiments; it lacks the medical-grade patient isolation required by the IEC 60601-1 safety standard, and no commercial analog currently exists at this price point. Yet the significance of the work extends well beyond the laboratory bench. Existing open-source EIT systems that support standard 16-electrode arrays typically cost between 250 and 330 dollars, while cheaper designs sacrifice electrode count or capability. By delivering a full 16-electrode, 50-kilohertz system with an end-to-end open-source software pipeline, including a Tkinter-based graphical interface, STM32 firmware, 3D-printable container files, and even a step-by-step video calibration tutorial, for under 100 dollars, the researchers have lowered the barrier to entry for students and early-career researchers to a fraction of its previous level. Future plans include upgrading to next-generation impedance converters with multi-frequency capability, compressing acquisition below one second for true real-time respiration monitoring, boosting SNR with active-shielded cabling, and exploring generative deep-learning models such as conditional variational autoencoders and diffusion models to push image quality beyond the physical limits of conventional reconstruction. For a field whose clinical value has long been constrained by hardware cost and complexity, a 95-dollar, fully open imaging platform is a genuinely democratizing development.

Subject of Research: A portable, low-cost, open-source electrical impedance tomography system with a modular analog front-end based on an STM32 microcontroller and AD5933 impedance converter.

Article Title: A portable, low-cost, and open-source electrical impedance tomography system with a modular analog front-end based on STM32 and AD5933

Article References: Febrico, J., & Basari (2026). A portable, low-cost, and open-source electrical impedance tomography system with a modular analog front-end based on STM32 and AD5933. HardwareX, Article e00836. https://doi.org/10.1016/j.ohx.2026.e00836

Image Credits: AI Generated

DOI: 10.1016/j.ohx.2026.e00836

Keywords: electrical impedance tomography, open-source hardware, STM32, AD5933, lung imaging, biomedical engineering, analog front-end, pyEIT, medical imaging, respiratory monitoring, low-cost instrumentation, phantom experiments