Pauline Chauvet1,2, Toby Collins2, Clement Debize2, Lorraine Novais-Gameiro3, Bruno Pereira4, Adrien Bartoli2, Michel Canis1,2, Nicolas Bourdel5,6. 1. Department of Gynecologic Surgery, Clermont-Ferrand University Hospital Estaing, place Lucie et Raymond Aubrac, Clermont-Ferrand, France. 2. Faculté de Médecine, ALCoV UMR6284, CNRS/Université d'Auvergne, ISIT, Clermont-Ferrand, France. 3. Faculté de Médecine, CICS Centre Imagerie Cellulaire Santé, 28 Place Henri Dunant, Clermont-Ferrand, France. 4. Biostatistics Unit (DRCI), University Hospital Clermont-Ferrand, 58, Rue Montalembert, 63003, Clermont-Ferrand Cedex, France. 5. Department of Gynecologic Surgery, Clermont-Ferrand University Hospital Estaing, place Lucie et Raymond Aubrac, Clermont-Ferrand, France. nicolas.bourdel@gmail.com. 6. Faculté de Médecine, ALCoV UMR6284, CNRS/Université d'Auvergne, ISIT, Clermont-Ferrand, France. nicolas.bourdel@gmail.com.
Abstract
BACKGROUND: Augmented Reality (AR) guidance is a technology that allows a surgeon to see sub-surface structures, by overlaying pre-operative imaging data on a live laparoscopic video. Our objectives were to evaluate a state-of-the-art AR guidance system in a tumor surgical resection model, comparing the accuracy of the resection with and without the system. Our system has three phases. Phase 1: using the MRI images, the kidney's and pseudotumor's surfaces are segmented to construct a 3D model. Phase 2: the intra-operative 3D model of the kidney is computed. Phase 3: the pre-operative and intra-operative models are registered, and the laparoscopic view is augmented with the pre-operative data. METHODS: We performed a prospective experimental study on ex vivo porcine kidneys. Alginate was injected into the parenchyma to create pseudotumors measuring 4-10 mm. The kidneys were then analyzed by MRI. Next, the kidneys were placed into pelvictrainers, and the pseudotumors were laparoscopically resected. The AR guidance system allows the surgeon to see tumors and margins using classical laparoscopic instruments, and a classical screen. The resection margins were measured microscopically to evaluate the accuracy of resection. RESULTS: Ninety tumors were segmented: 28 were used to optimize the AR software, and 62 were used to randomly compare surgical resection: 29 tumors were resected using AR and 33 without AR. The analysis of our pathological results showed 4 failures (tumor with positive margins) (13.8%) in the AR group, and 10 (30.3%) in the Non-AR group. There was no complete miss in the AR group, while there were 4 complete misses in the non-AR group. In total, 14 (42.4%) tumors were completely missed or had a positive margin in the non-AR group. CONCLUSIONS: Our AR system enhances the accuracy of surgical resection, particularly for small tumors. Crucial information such as resection margins and vascularization could also be displayed.
BACKGROUND: Augmented Reality (AR) guidance is a technology that allows a surgeon to see sub-surface structures, by overlaying pre-operative imaging data on a live laparoscopic video. Our objectives were to evaluate a state-of-the-art AR guidance system in a tumor surgical resection model, comparing the accuracy of the resection with and without the system. Our system has three phases. Phase 1: using the MRI images, the kidney's and pseudotumor's surfaces are segmented to construct a 3D model. Phase 2: the intra-operative 3D model of the kidney is computed. Phase 3: the pre-operative and intra-operative models are registered, and the laparoscopic view is augmented with the pre-operative data. METHODS: We performed a prospective experimental study on ex vivo porcine kidneys. Alginate was injected into the parenchyma to create pseudotumors measuring 4-10 mm. The kidneys were then analyzed by MRI. Next, the kidneys were placed into pelvictrainers, and the pseudotumors were laparoscopically resected. The AR guidance system allows the surgeon to see tumors and margins using classical laparoscopic instruments, and a classical screen. The resection margins were measured microscopically to evaluate the accuracy of resection. RESULTS: Ninety tumors were segmented: 28 were used to optimize the AR software, and 62 were used to randomly compare surgical resection: 29 tumors were resected using AR and 33 without AR. The analysis of our pathological results showed 4 failures (tumor with positive margins) (13.8%) in the AR group, and 10 (30.3%) in the Non-AR group. There was no complete miss in the AR group, while there were 4 complete misses in the non-AR group. In total, 14 (42.4%) tumors were completely missed or had a positive margin in the non-AR group. CONCLUSIONS: Our AR system enhances the accuracy of surgical resection, particularly for small tumors. Crucial information such as resection margins and vascularization could also be displayed.
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