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Augmented Reality in Image-Guided Robotic Surgery
Published in Terry M. Peters, Cristian A. Linte, Ziv Yaniv, Jacqueline Williams, Mixed and Augmented Reality in Medicine, 2018
Wen Pei Liu, Russell H. Taylor
Other teams also have reported the successful use of different types of mixed reality and endoscopic video. Previously rendered images were superimposed with transparency on the real scene using a virtual helmet. Su et al. (2008) used AR during robot-assisted partial nephrectomy, in which they overlaid reconstructed 3D computer tomography images onto real-time stereo video footage. Although retrospective, these results showed the possibility of incorporating these images directly in the surgical field during the operation. Pietrabissa et al. (2009) also demonstrated the advantages of AR during the treatment of a splenic artery aneurysm.
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Published in Valerio Voliani, Nanomaterials and Neoplasms, 2021
Kim M. Tsoi, Sonya A. Macparland, Xue-Zhong Ma, Vinzent N. Spetzler, Juan Echeverri, Ben Ouyang, Saleh M. Fadel, Edward A. Sykes, Nicolas Goldaracena, Johann M. Kaths, John B. Conneely, Benjamin A. Alman, Markus Selzner, Mario A. Ostrowski, Oyedele A. Adeyi, Anton Zilman, Ian D. Mcgilvray, Warren C. W. Chan
Finally, we asked whether flow dynamics and microarchitecture could be used to predict nanomaterial uptake in the spleen. We found that nanomaterial accumulation reflects blood velocity, as almost all nanomaterials were found within the red pulp region (see Fig. 17.4a). Washout studies have demonstrated that blood preferentially slows down in the red pulp, where it has a half-life of ∼10 min (Refs. 39,40). Like the hepatic sinusoid, the red pulp is rich in macrophages. As macrophages in the hepatic sinusoid and the splenic red pulp are exposed to nanomaterial-containing blood flowing at a very slow rate, we hypothesized that quantum dot uptake would be comparable between the two macrophage types. However, when we analysed quantum dot uptake in splenic mononuclear cells isolated from quantum-dot-treated rats, we found that splenic macrophages took up significantly less nanomaterial than Kupffer cells. 12 h post-injection, only 25.4 ± 10.1% of splenic macrophages were quantum-dot-positive, compared to 84.8 ± 6.4% of Kupffer cells (see Fig. 17.4b,c). Splenic macrophages also took up ten times less nanomaterial on a per cell basis (see Fig. 17.4b,c). A similar trend was seen 4 h post-injection (see Fig. 17.4c). This suggests that cellular phenotype within the MPS also contributes to uptake. Despite similar opportunity, splenic macrophages have less endocytic/phagocytic affinity for nanomaterials than their counterparts in the liver. We confirmed the role of cellular phenotype by comparing quantum dot uptake by primary splenic and hepatic macrophages in vitro. As anticipated, Kupffer cells took up more quantum dots than did splenic macrophages (see Fig. 17.4d,e). At the 80 nM dose, 59.9 ± 9.0% of Kupffer cells were quantum-dot-positive, compared to 35.1 ± 10.4% of splenic macrophages and the MFI for Kupffer cells was approximately double that for splenic macrophages. The same trend was found for other nanomaterial designs. Interestingly, the liver–spleen difference is more pronounced in vivo, and this may relate to other anatomical and physiological differences between the organs. First, despite their location in the “slow flow” red pulp region of the spleen, splenic macrophages may not have the same access to transiting nanomaterials as do Kupffer cells in the liver. Second, the rat liver receives approximately 21% of the cardiac output via both the hepatic artery and the portal vein, whereas the spleen receives only 1% via the splenic artery18,41.
Station number assignment to abdominal lymph node for assisting gastric cancer surgery
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2021
Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori
The labelled artery volume is inputted in the proposed method. We extract eight arteries from the input volume. The eight arteries are left gastric artery (LGA), right gastric artery (RGA), left gastroepiploic artery (LGEA), right gastroepiploic artery (RGEA), splenic artery (SA), proper hepatic artery (PHA), common hepatic artery (CHA), and coeliac artery (CA). These arteries are in close relation to the station group numbers as described in Section 2. The binary volumes for each artery, , , , , , , , and are obtained from the labelled artery volume. The proposed method computes the vessel dominant maps for each artery from these binary volumes as described in Section 3.2. After computation, we obtain the vessel dominant maps for each artery, , , , , , , , and . Using these vessel dominant maps, the proposed method assigns station group numbers to the lymph nodes. Let denote the extracted lymph node volume and denote th lymph node in this volume. For the lymph node , we calculate the maximum voxel value in each vessel dominant map,