BackgroundFLuorescence-based Enhanced Reality (FLER) is a software providing quantitative fluorescence angiography (FA), by computing the fluorescence intensity time-to-peak (TTP), after intravenous injection of indocyanine green (ICG). HSI is a contrast-free optical imaging modality, which measures tissue oxygenation (StO 2 ). MethodsIn 8 pigs, an ischemic bowel segment was created by dividing the arcade branches and imaged using HSI and FLER. StO 2 values were acquired through a hyperspectral imaging (HSI) system. Subsequently, FA was performed using a nearinfrared laparoscopic camera, after intravenous injection of 0.2mg/kg of ICG. The TTP fluorescence signal was analyzed through a proprietary software to realize a perfusion map. This was overlaid onto real-time images to obtain FLER. Simultaneously, nine adjacent regions of interest (ROIs) were selected and superimposed onto the real-time video obtaining HYperspectral-based Enhanced Reality (HYPER). FLER and HYPER were superimposed allowing a comparison of both imaging modalities. Local capillary lactates (LCL) were sampled at the ROIs. Two LCL prediction models were extrapolated based on both imaging. ResultsFor all ROIs mean LCL measured 4.67 ± 4.34 mmol/L, mean StO 2 45.92 ± 18.59%, and mean TTP 10.33 ± 9.36 sec. Pearson's test between FLER-TTP and HSI-StO2 at the corresponding ROIs gave an R=-0.66 (p<0.0001). The HSI lactate prediction model proved significantly more accurate than the FLER-based one (p<0.0001). Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation ConclusionBowel perfusion was clearly quantified using HSI and FA. HSI yielded more accurate results than FA. HYPER might become a useful intraoperative tool to quantify bowel ischemia in a contrast-free fashion.

To reduce the risk of pancreatic fistula after pancreatectomy, a satisfactory blood flow at the pancreatic stump is considered crucial. Our group has developed and validated a real-time computational imaging analysis of tissue perfusion, using fluorescence imaging, the fluorescence-based enhanced reality (FLER). Hyperspectral imaging (HSI) is another emerging technology, which provides tissue-specific spectral signatures, allowing for perfusion quantification. Both imaging modalities were employed to estimate perfusion in a porcine model of partial pancreatic ischemia. Perfusion quantification was assessed using the metrics of both imaging modalities (slope of the time to reach maximum fluorescence intensity and tissue oxygen saturation (StO2), for FLER and HSI, respectively). We found that the HSI-StO2 and the FLER slope were statistically correlated using the Spearman analysis (R = 0.697; p = 0.013). Local capillary lactate values were statistically correlated to the HSI-StO2 and to the FLER slope (R = −0.88; p < 0.001 and R = −0.608; p = 0.0074). HSI-based and FLER-based lactate prediction models had statistically similar predictive abilities (p = 0.112). Both modalities are promising to assess real-time pancreatic perfusion. Clinical translation in human pancreatic surgery is currently underway.

Introduction/objective Gastric conduit (GC) is used for reconstruction after esophagectomy. Anastomotic leakage (AL) incidence remains high, given the extensive disruption of the gastric circulation. Currently, there is no reliable method to intraoperatively quantify gastric perfusion. Hyperspectral imaging (HSI) has shown its potential to quantify serosal StO2. Confocal laser endomicroscopy (CLE) allows for automatic mucosal microcirculation quantification as functional capillary density area (FCD-A). The aim of this study was to quantify serosal and mucosal GC’s microperfusion using HSI and CLE. Local capillary lactate (LCL) served as biomarker. Methods GC was formed in 5 pigs and serosal StO2% was quantified at 3 regions of interest (ROI) using HSI: fundus (ROI-F), greater curvature (ROI-C), and pylorus (ROI-P). After intravenous injection of sodium-fluorescein (0.5 g), CLE-based mucosal microperfusion was assessed at the corresponding ROIs, and LCLs were quantified via a lactate analyzer. Results StO2 and FCD-A at ROI-F (41 ± 10.6%, 3.3 ± 3.8, respectively) were significantly lower than ROI-C (68.2 ± 6.7%, p value: 0.005; 18.4 ± 7, p value: 0.01, respectively) and ROI-P (72 ± 10.4%, p value: 0.005; 15.7 ± 3.2 p value: 0.001). LCL value at ROI-F (9.6 ± 4.7 mmol/L) was significantly higher than at ROI-C (2.6 ± 1.2 mmol/L, p value: 0.04) and ROI-P (2.6 ± 1.3 mmol/L, p value: 0.04). No statistically significant difference was found in all metrics between ROI-C and ROI-P. StO2 correlated with FCD-A (Pearson’s r = 0.67). The LCL correlated negatively with both FCD-A (Spearman’s r =  − 0.74) and StO2 (Spearman’s r =  −  0.54). Conclusions GC formation causes a drop in serosal and mucosal fundic perfusion. HSI and CLE correlate well and might become useful intraoperative tools.

Learning and imitating a complex motor action requires to visually follow complex movements, but conscious perception seems too slow for such tasks. Recent findings suggest that visual perception has a higher temporal resolution at an unconscious than at a conscious level. Here we investigate whether high-temporal resolution in visual perception relies on prediction mechanisms and attention shifts based on recently experienced sequences of visual information. To that aim we explore sequential effects during four different simultaneity/asynchrony discrimination tasks. Two stimuli are displayed on each trial with varying stimulus onset asynchronies (SOA). Subjects decide whether the stimuli are simultaneous or asynchronous and give manual responses. The main finding is an advantage for different-over same-order trials, when subjects decided that stimuli had been simultaneous on trial t-1, and when trial t is with an SOA slightly larger than trial t-1, or equivalent. The advantage for different-order trials disappears when the stimuli change eccentricity but not direction between trials (Experiment 2), and persists with stimuli displayed in the center and unlikely to elicit a sense of direction (Experiment 4). It is still observed when asynchronies on trial t-1 are small and undetected (Experiment 2). The findings can be explained by an attention shift that is precisely planned in time and space and that incidentally allows subjects to detect an isolated stimulus on the screen, thus helping them to detect an asynchrony.