Regional Tissue Perfusion Index (RTPI): A New Optical-Based Metric for Quantifying Regional Tissue Perfusion

Purpose Accurate, continuous assessment of regional tissue perfusion remains a significant clinical challenge, as most existing modalities are invasive, indirect, or impractical for routine monitoring. Near-infrared spectroscopy (NIRS) has been widely adopted to assess tissue oxygenation; however, conventional NIRS-derived indices are insufficient surrogates for true perfusion and often fail to capture rapid hemodynamic changes. This study aimed to introduce and validate the Regional Tissue Perfusion Index (RTPI), a novel NIRS-derived metric that integrates multiple features of the NIRS signal to provide continuous, non-invasive, and physiologically relevant assessment of tissue perfusion. Methods RTPI was developed using principal component analysis (PCA) of multiple NIRS-derived parameters, including pulse amplitude ratio, signal derivatives, and area under the curve. Its performance was evaluated in healthy volunteers during controlled ischemia–reperfusion protocols and compared with established reference standards, including laser Doppler flowmetry (LDF) and photoplethysmography (PPG). Partial least squares (PLS) regression was also applied to test the robustness of the approach. Results RTPI showed strong correlations with LDF and PPG during dynamic perfusion changes. Unlike conventional NIRS-derived oxygenation and hemodynamic indices, which often exhibited delayed or paradoxical responses, RTPI demonstrated immediate and significant sensitivity to both complete and partial ischemia–reperfusion episodes across all cases. Intraclass correlation and error analyses confirmed high test–retest reliability and low measurement error. Comparable performance between PCA- and PLS-derived indices further supported robustness and generalizability. Conclusion RTPI represents a multiparametric, physiologically meaningful, and computationally efficient metric for real-time tissue perfusion monitoring. Its ability to detect perfusion compromise independently of oxygenation indices highlights its translational potential for bedside implementation in critical care, trauma, perioperative, and vascular medicine, where improved diagnostic accuracy could significantly impact patient outcomes.