Eric Felli, MSc1, Mahdi Al-Taher, MD2, Takeshi Urade, MD2, Manuel Barberio, MD, PhD2, Emanuele Felli, MD3, Laurent Goffin4, Giuseppe Maria Ettorre, MD5, Jacques Marescaux, MD FACS Hon FRCS Hon FJSES Hon6, Michele Diana, MD, PhD6. 1Physiology Institute, EA 3072, Mitochondria and Oxidative Stress, University of Strasbourg, France, 2IHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France, 3Hepato-Biliary and Pancreatic Surgical Unit, General, Digestive, and Endocrine Surgery, University Hospital of Strasbourg, France, 4AVR Team-Project, ICube/CNRS, Strasbourg University, 67081 Strasbourg, France Laurent, 5San Camillo Forlanini Hospital, Department of Transplantation and General Surgery, Rome, Italy, 6IRCAD, Research Institute against Cancer of the Digestive System, Strasbourg, France
Objective of the technology or device: During liver resections, the clinical assessment of the demarcation line separating ischemic from non-ischemic parenchyma may be difficult in general and even more challenging in specific conditions such as cirrhosis. Optical imaging technologies can enhance the appreciation of the resection line. Near-infrared fluorescence (NIRF) with indocyanine green (ICG) has been successfully used as a means to guide both major hepatectomies and segmental resections. However, ICG fluorescence-based segmentation techniques are limited by the recirculation of the fluorophore to ischemic areas, within a few minutes, which reduces the sharpness of the demarcation line. Hyperspectral imaging (HSI) is a real-time non-invasive optical imaging modality which combines a camera and a spectroscope and allows for a quantitative imaging of tissue oxygenation. However, current HSI systems display the quantitative information in a side-by-side fashion. Our group has developed a software tool which allows to overlay HSI images directly onto the operative field with an enhanced reality method. The aim of the present experimental study is to evaluate the accuracy of HSI-based enhanced reality to identify the resection line after a left vascular inflow occlusion during an anatomical left hepatectomy in a preclinical model.
Technology description and method for its use or application: In the porcine model (n=3), the left branches of the hepatic pedicle were ligated using Vicryl 2.0. Before and after vascular occlusion, HSI (TIVITA®, Diaspective Vision GmbH, Germany) images based on tissue oxygenation were acquired and superimposed onto RGB camera images. The demarcation line was marked on the liver surface with electrocautery according to the HSI-based augmented reality. Local lactates were measured on blood samples from the liver surface in both the ischemic and perfused segment using a strip-based portable device (EDGE®, ApexBio, Taipei, Taiwan, ROC). At the same areas, confocal endomicroscopy (Cellvizio, Mauna Kea, Paris) was used to obtain microscopic images of both ischemic and perfused areas. The hepatectomy was performed using a clamp crushing method with total intermittent Pringle’s maneuver (cycles of 15 minutes of ischemia and 5 minutes of reperfusion).
Preliminary results if available: Ligation of left branches resulted in a reduced perfusion of the left medial lobe (LML) as compared to the right medial lobe (RML). Hyperspectral imaging of the LML demonstrated a significantly lower oxygenation (0.27%±0.20) when compared to the RML and the control represented by the liver before partial ligation (58.60%±12.08; p=0.0015; 66.03%±14.67; p=0.0008). A significantly higher level of capillary lactates (3.07mmol/L±0.84) was measured at the LML vs. the RML and the control (1.33±0.71mmol/L; p=0.0356; 0.70mmol/L; p=0.0091). Similarly, confocal videos demonstrated the absence of blood flow in the LML whereas the control and the RML areas showed normal perfusion in the sinusoids.
Conclusions/future directions: HSI-based enhanced reality could correctly identify the demarcation line and quantify liver oxygenation. HSI could well be a suitable intraoperative imaging tool to guide perfusion-based liver resections.
This abstract was accepted for Podium presentation at the 2020 SAGES Virtual Meeting in the topic. Its program number was: ET003 and its Abstract ID was: 106363