RESUMEN

Significance: The arterial input function (AIF) plays a crucial role in correcting the time-dependent concentration of the contrast agent within the arterial system, accounting for variations in agent injection parameters (speed, timing, etc.) across patients. Understanding the significance of the AIF can enhance the accuracy of tissue vascular perfusion assessment through indocyanine green-based dynamic contrast-enhanced fluorescence imaging (DCE-FI). Aim: We evaluate the impact of the AIF on perfusion assessment through DCE-FI. Approach: A total of 144 AIFs were acquired from 110 patients using a pulse dye densitometer. Simulation and patient intraoperative imaging were conducted to validate the significance of AIF for perfusion assessment based on kinetic parameters extracted from fluorescence images before and after AIF correction. The kinetic model accuracy was evaluated by assessing the variability of kinetic parameters using individual AIF versus population-based AIF. Results: Individual AIF can reduce the variability in kinetic parameters, and population-based AIF can potentially replace individual AIF for estimating wash-out rate ( k ep ), maximum intensity ( I max ), ingress slope with lower differences compared with those in estimating blood flow, volume transfer constant ( K trans ), and time to peak. Conclusions: Individual AIF can provide the most accurate perfusion assessment compared with assessment without AIF or based on population-based AIF correction.

Asunto(s)

Verde de Indocianina , Imagen Óptica , Humanos , Imagen Óptica/métodos , Verde de Indocianina/química , Verde de Indocianina/farmacocinética , Femenino , Persona de Mediana Edad , Anciano , Masculino , Medios de Contraste/química , Adulto , Arterias/diagnóstico por imagen , Imagen de Perfusión/métodos , Simulación por Computador

RESUMEN

Indocyanine green (ICG)-based dynamic contrast-enhanced fluorescence imaging (DCE-FI) can objectively assess bone perfusion intraoperatively. However, it is susceptible to motion artifacts due to patient's involuntary respiration during the 4.5-minute DCE-FI data acquisition. An automated motion correction approach based on mutual information (MI) frameby-frame was developed to overcome this problem. In this approach, MIs were calculated between the reference and the adjacent frame translated and the maximal MI corresponded to the optimal translation. The images obtained from eighteen amputation cases were utilized to validate the approach and the results show that this correction can significantly reduce the motion artifacts and can improve the accuracy of bone perfusion assessment.