Real-time quantification of intestinal perfusion and arterial versus venous occlusion using laser speckle contrast imaging in porcine model.

PURPOSE: Real-time intraoperative perfusion assessment may reduce anastomotic leaks. Laser speckle contrast imaging (LSCI) provides dye-free visualization of perfusion by capturing coherent laser light scatter from red blood cells and displays perfusion as a colormap. Herein, we report a novel method to precisely quantify intestinal perfusion using LSCI. METHODS: ActivSight™ is an FDA-cleared multi-modal visualization system that can detect and display perfusion via both indocyanine green imaging (ICG) and LSCI in minimally invasive surgery. An experimental prototype LSCI perfusion quantification algorithm was evaluated in porcine models. Porcine small bowel was selectively devascularized to create regions of perfused/watershed/ischemic bowel, and progressive aortic inflow/portal vein outflow clamping was performed to study arterial vs. venous ischemia. Continuous arterial pressure was monitored via femoral line. RESULTS: LSCI perfusion colormaps and quantification distinguished between perfused, watershed, and ischemic bowel in all vascular control settings: no vascular occlusion (p < 0.001), aortic occlusion (p < 0.001), and portal venous occlusion (p < 0.001). LSCI quantification demonstrated similar levels of ischemia induced both by states of arterial inflow and venous outflow occlusion. LSCI-quantified perfusion values correlated positively with higher mean arterial pressure and with increasing distance from ischemic bowel. CONCLUSION: LSCI relative perfusion quantification may provide more objective real-time assessment of intestinal perfusion compared to conventional naked eye assessment by quantifying currently subjective gradients of bowel ischemia and identifying both arterial/venous etiologies of ischemia.

Real-time quantification of bowel perfusion using Laparoscopic Laser Speckle Contrast Imaging (LSCI) in a porcine model.

BACKGROUND/PURPOSE: Real-time quantification of tissue perfusion can improve intraoperative surgical decision making. Here we demonstrate the utility of Laser Speckle Contrast Imaging as an intra-operative tool that quantifies real-time regional differences in intestinal perfusion and distinguishes ischemic changes resulting from arterial/venous obstruction. METHODS: Porcine models (n = 3) consisted of selectively devascularized small bowel loops that were used to measure the perfusion responses under conditions of control/no vascular occlusion, arterial inflow occlusion, and venous outflow occlusion using laser speckle imaging and indocyanine green fluoroscopy. Laser Speckle was also used to assess perfusion differences between small bowel antimesenteric-antimesenteric and mesenteric-mesenteric anastomoses. Perfusion quantification was measured in relative perfusion units calculated from the laser speckle perfusion heatmap. RESULTS: Laser Speckle distinguished between visually identified perfused, watershed, and ischemic intestinal segments with both color heatmap and quantification (p < .00001). It detected a continuous gradient of relative intestinal perfusion as a function of distance from the stapled ischemic bowel edge. Strong positive linear correlation between relative perfusion units and changes in mean arterial pressure resulting from both arterial (R2 = .96/.79) and venous pressure changes (R2 = .86/.96) was observed. Furthermore, Laser Speckle showed that the antimesenteric anastomosis had a higher perfusion than mesenteric anastomosis (p < 0.01). CONCLUSIONS: Laser Speckle Contrast Imaging provides objective, quantifiable tissue perfusion information in both color heatmap and relative numerical units. Laser Speckle can detect spatial/temporal differences in perfusion between antimesenteric and mesenteric borders of a bowel segment and precisely detect perfusion changes induced by progressive arterial/venous occlusions in real-time.

High-Accuracy Neuro-Navigation with Computer Vision for Frameless Registration and Real-Time Tracking.

For the past three decades, neurosurgeons have utilized cranial neuro-navigation systems, bringing millimetric accuracy to operating rooms worldwide. These systems require an operating room team, anesthesia, and, most critically, cranial fixation. As a result, treatments for acute neurosurgical conditions, performed urgently in emergency rooms or intensive care units on awake and non-immobilized patients, have not benefited from traditional neuro-navigation. These emergent procedures are performed freehand, guided only by anatomical landmarks with no navigation, resulting in inaccurate catheter placement and neurological deficits. A rapidly deployable image-guidance technology that offers highly accurate, real-time registration and is capable of tracking awake, moving patients is needed to improve patient safety. The Zeta Cranial Navigation System is currently the only non-fiducial-based, FDA-approved neuro-navigation device that performs real-time registration and continuous patient tracking. To assess this system's performance, we performed registration and tracking of phantoms and human cadaver heads during controlled motions and various adverse surgical test conditions. As a result, we obtained millimetric or sub-millimetric target and surface registration accuracy. This rapid and accurate frameless neuro-navigation system for mobile subjects can enhance bedside procedure safety and expand the range of interventions performed with high levels of accuracy outside of an operating room.

Accurate Heuristic Terrain Prediction in Powered Lower-Limb Prostheses Using Onboard Sensors.

OBJECTIVE: This study describes the development and offline validation of a heuristic algorithm for accurate prediction of ground terrain in a lower limb prosthesis. This method is based on inference of the ground terrain geometry using estimation of prosthetic limb kinematics during gait with a single integrated inertial measurement unit. METHODS: We asked five subjects with below-knee amputations to traverse level ground, stairs, and ramps using a high-range-of-motion powered prosthesis while internal sensor data were remotely logged. We used these data to develop three terrain prediction algorithms. The first two employed state-of-the-art machine learning approaches, while the third was a directly tuned heuristic using thresholds on estimated prosthetic ankle joint translations and ground slope. We compared the performance of these algorithms using resubstitution error for the machine learning algorithms and overall error for the heuristic algorithm. RESULTS: Our optimal machine learning algorithm attained a resubstitution error of 3.4% using 45 features, while our heuristic method attained an overall prediction error of 2.8% using only 5 features derived from estimation of ground slope and horizontal and vertical ankle joint displacement. Compared with pattern recognition, the heuristic performed better on each individual subject, and across both level and non-level strides. CONCLUSION AND SIGNIFICANCE: These results demonstrate a method for heuristic prediction of ground terrain in a powered prosthesis. The method is more accurate, more interpretable, and less computationally expensive than machine learning methods considered state-of-the-art for intent recognition, and relies only on integrated prosthesis sensors. Finally, the method provides intuitively tunable thresholds to improve performance for specific walking conditions.

Translational Motion Tracking of Leg Joints for Enhanced Prediction of Walking Tasks.

OBJECTIVE: Walking task prediction in powered leg prostheses is an important problem in the development of biomimetic prosthesis controllers. This paper proposes a novel method to predict upcoming walking tasks by estimating the translational motion of leg joints using an integrated inertial measurement unit. METHODS: We asked six subjects with unilateral transtibial amputations to traverse flat ground, ramps, and stairs using a powered prosthesis while inertial signals were collected. We then performed an offline analysis in which we simulated a real-time motion tracking algorithm on the inertial signals to estimate knee and ankle joint translations, and then used pattern recognition separately on the inertial and translational signal sets to predict the target walking tasks of individual strides. RESULTS: Our analysis showed that using inertial signals to derive translational signals enabled a prediction error reduction of 6.8% compared to that attained using the original inertial signals. This result was similar to that seen by addition of surface electromyography sensors to integrated sensors in previous work, but was effected without adding any extra sensors. Finally, we reduced the size of the translational set to that of the inertial set and showed that the former still enabled a composite error reduction of 5.8%. CONCLUSION AND SIGNIFICANCE: These results indicate that translational motion tracking can be used to substantially enhance walking task prediction in leg prostheses without adding external sensing modalities. Our proposed algorithm can thus be used as a part of a task-adaptive and fully integrated prosthesis controller.