Microwave imaging is a promising non-ionizing technique for bedside follow-up of intracranial hemorrhage, but dynamic monitoring remains challenging under limited multistatic sampling because weak inter-frame changes can be obscured by measurement variability, model mismatch, and the high cost of frame-by-frame nonlinear inversion. To address this problem, this paper proposes a state-referenced truncated singular-value decomposition (SR-TSVD) framework for dynamic microwave monitoring of hemorrhagic evolution. The method maintains an internal gate state and reconstructs only the state-referenced increment at each monitoring instant. A row-whitened TSVD inversion is introduced to reduce channel dominance effects and improve robustness to route-dependent imbalance, while a residual-driven gate-refresh mechanism updates the internal state only when the current linearization background becomes insufficiently accurate. The proposed method was validated through two-dimensional numerical experiments and hardware phantom measurements. The numerical study examined different lesion evolution scenarios and analyzed the effects of antenna count, frequency diversity, and measurement noise. The hardware study showed that the method preserves the main dynamic evolution in a real measurement system and remains more stable than baseline linear methods under sparse array conditions. These results indicate that SR-TSVD provides an effective and computationally practical framework for repeated bedside microwave monitoring of intracranial hemorrhage.
Zhang et al. (Thu,) studied this question.