Physiological swelling in human calf has been imaged under stocking compression by the sparse Bayesian learning implemented into electrical impedance tomography (SBL-EIT) to evaluate the in situ treatment effect of various compression pressures. SBL-EIT reconstructs conductivity distribution \(\Delta {\varvec{\upsigma}}\) to image excessive extracellular fluid in subcutaneous adipose tissue (SAT), indicating the susceptibilities to physiological swelling due to various compression pressures. The SBL-EIT was applied to the imaging of eight-subject calves during prolonged standing under three types of net compression pressures Pnet measured by pressure sensor – strong pressure: Pnet, Strong = 11.9 \(\pm \) 2.0 mmHg, weak pressure: Pnet, Weak = 4.47 \(\pm \) 3.1 mmHg, and control pressure: Pnet, Control = 0.00 \(\pm \) 0.0 mmHg, respectively. From the experimental results, the spatial-mean conductivity ⟨σ⟩α2 with two pre-processing steps to eliminate undesirable effects, i.e., the difference in skin condition and effect of wearing stockings itself, is the highest in the case of stocking with control pressure, followed by weak and strong pressures across all subjects. Moreover, the ⟨σ⟩α2 has a strong positive correlation with the conventional inversed impedance 1/zBIA by bioelectrical impedance analysis (BIA) (a correlation coefficient 0.528 < R < 0.990; n = 19 and p < 0.05), which is mainly increased during the prolonged standing. Moreover, various susceptibilities to physiological swelling are investigated based on the increase in \(\Delta {\varvec{\upsigma}}\) for each subject, which is associated with subject external factors such as postural changes and circumference and internal factors like SAT.
