SignificanceThe 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).AimWe evaluate the impact of the AIF on perfusion assessment through DCE-FI.ApproachA 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.ResultsIndividual AIF can reduce the variability in kinetic parameters, and population-based AIF can potentially replace individual AIF for estimating wash-out rate (kep), maximum intensity (Imax), ingress slope with lower differences compared with those in estimating blood flow, volume transfer constant (Ktrans), and time to peak.ConclusionsIndividual AIF can provide the most accurate perfusion assessment compared with assessment without AIF or based on population-based AIF correction.
SignificanceIndocyanine green-based dynamic contrast-enhanced fluorescence imaging (DCE-FI) can objectively assess bone perfusion intraoperatively. However, it is susceptible to motion artifact due to patients’ involuntary respiration and mechanical disturbance. Reducing motion artifacts would significantly improve DCE-FI for orthopedic surgical guidance.AimOur primary objective is to develop an automated correction method to reduce motion artifacts in DCE-FI and improve the accuracy of bone perfusion assessment.ApproachWe developed an automated motion correction approach based on frame-by-frame mutual information (MI) and validated the effectiveness of this approach in various phantom studies and patient images from 45 imaging sessions of fifteen amputees.ResultsThe MI-based correction reduced motion artifacts by 93% for mechanical disturbances and 76% for simulated respiration in phantom studies. Patient images show improved alignment, improved kinetic curves, and restored bone perfusion-related parameters with an average correction of 4.3 and 9.6 mm in x- and y-axes per session.ConclusionsThe automated MI-based motion correction was able to eliminate motion artifacts effectively and significantly improved the quantitative assessment of bone perfusion by DCE-FI.
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) frame-by-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.
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