Modular, low-cost optical CT platform for hands-on tomography education

Theoretical instruction in computed tomography frequently encounters a disconnect between abstract mathematical models and physical implementation due to the high costs and safety hazards associated with ionizing X-ray equipment. This work addresses this pedagogical barrier by developing a novel, non-ionizing, modular optical computed tomography (CT) educational platform for bioengineering teaching. Built from 3D-printed parts on a LEGO® chassis, with a Teensy 4.0 board and a visible red laser, the prototype integrates an incremental capacitive encoder that indexes each intensity sample to the carriage position. A Python-based framework implements filtered back-projection (FBP) through the inverse Radon transform, together with iterative methods such as the simultaneous algebraic reconstruction technique (SART) with Gaussian filtering or total-variation (TV) regularization. The system, realized for approximately USD 408 in its budget configuration (or USD 631 with a precision rotation stage), visualizes internal cross-sections of various phantoms, including biological analogs and refractive media. Validation trials demonstrate the efficacy of the hardware in resolving high-opacity inclusions and internal refractive structures, while using inherent acquisition artifacts as teaching examples of sampling theory and reconstruction limits. A pilot study with 16 bioengineering students showed high user acceptance (mean perception score 4.94/5) and a positive trend in conceptual understanding, providing preliminary evidence of pedagogical feasibility. By moving students from purely software-based simulation to physical hardware experimentation, this platform offers a safe, reconfigurable, and transparent tool for teaching tomographic principles. It exposes the engineering constraints, hardware limitations, and image-fidelity trade-offs that characterize medical imaging systems.