281 resultados para Realtà Aumentata Augmented Reality App Vuforia Image Targeting Unity XCode iOS


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OBJECTIVE: Our aim was to evaluate a fluorescence-based enhanced-reality system to assess intestinal viability in a laparoscopic mesenteric ischemia model. MATERIALS AND METHODS: A small bowel loop was exposed, and 3 to 4 mesenteric vessels were clipped in 6 pigs. Indocyanine green (ICG) was administered intravenously 15 minutes later. The bowel was illuminated with an incoherent light source laparoscope (D-light-P, KarlStorz). The ICG fluorescence signal was analyzed with Ad Hoc imaging software (VR-RENDER), which provides a digital perfusion cartography that was superimposed to the intraoperative laparoscopic image [augmented reality (AR) synthesis]. Five regions of interest (ROIs) were marked under AR guidance (1, 2a-2b, 3a-3b corresponding to the ischemic, marginal, and vascularized zones, respectively). One hour later, capillary blood samples were obtained by puncturing the bowel serosa at the identified ROIs and lactates were measured using the EDGE analyzer. A surgical biopsy of each intestinal ROI was sent for mitochondrial respiratory rate assessment and for metabolites quantification. RESULTS: Mean capillary lactate levels were 3.98 (SD = 1.91) versus 1.05 (SD = 0.46) versus 0.74 (SD = 0.34) mmol/L at ROI 1 versus 2a-2b (P = 0.0001) versus 3a-3b (P = 0.0001), respectively. Mean maximal mitochondrial respiratory rate was 104.4 (±21.58) pmolO2/second/mg at the ROI 1 versus 191.1 ± 14.48 (2b, P = 0.03) versus 180.4 ± 16.71 (3a, P = 0.02) versus 199.2 ± 25.21 (3b, P = 0.02). Alanine, choline, ethanolamine, glucose, lactate, myoinositol, phosphocholine, sylloinositol, and valine showed statistically significant different concentrations between ischemic and nonischemic segments. CONCLUSIONS: Fluorescence-based AR may effectively detect the boundary between the ischemic and the vascularized zones in this experimental model.

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In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.

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Aim: Gamma Knife surgery (GKS) is a non-invasive neurosurgical stereotactic procedure, increasingly used as an alternative to open functional procedures. This includes the targeting of the ventro-intermediate (Vim) nucleus of the thalamus for tremor. We currently perform an indirect targeting, using the "quadrilatere of Guyot," as the Vim nucleus is not visible on current 3 Tesla (T) MRI acquisitions. The primary objective of the current study was to enhance anatomic imaging for Vim GKS using high-field (7 T) MRI, with the aim of refining the visualization and precision of anatomical targeting. Method: Five young healthy subjects (mean age 23 years) were scanned both on 3 and 7 T MRI in Lausanne University Hospital (CHUV) and Center for Biomedical Imaging (CIBM). Classical T1-weighted MPRAGE, T2 CISS sequences (replacing former ventriculography) and diffusion tensor imaging were acquired at 3T. We obtained high-resolution susceptibility weighted images (SWI) at 7T for the visualization of thalamic subparts. SWI was further integrated for the first time into Leksell Gamma Plan® (LGP) software and co-registered with the 3T images. A simulation of targeting of the Vim was done using the "quadrilatere of Guyot" methodology on the 3T images. Furthermore, a correlation with the position of the found target on SWI was performed. The atlas of Morel et al. was used to confirm the findings on a detailed computer analysis outside LGP. Also, 3T and 7T MRI of one patient undergoing GKS Vim thalamotomy, were obtained before and 2 years after the procedure, and studied similarly. Results: The use of SWI provided a superior resolution and improved image contrast within the central gray matter. This allowed visualization and direct delineation of groups of thalamic nuclei in vivo, including the Vim. The position of the target, as assessed with the "quadrilatere of Guyot" method on 3 T, perfectly matched with the supposed one of the Vim on the SWI. Furthermore, a 3-dimensional model of the Vim target area was created on the basis of 3T and 7T images. Conclusion: This is the first report of the integration of SWI high-field MRI into the LGP in healthy subjects and in one patient treated GKS Vim thalamotomy. This approach aims at the improvement of targeting validation and further direct targeting of the Vim in tremor. The anatomical correlation between the direct visualization on 7T and the current targeting methods on 3T seems to show a very good anatomical matching.

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This paper presents a new non parametric atlas registration framework, derived from the optical flow model and the active contour theory, applied to automatic subthalamic nucleus (STN) targeting in deep brain stimulation (DBS) surgery. In a previous work, we demonstrated that the STN position can be predicted based on the position of surrounding visible structures, namely the lateral and third ventricles. A STN targeting process can thus be obtained by registering these structures of interest between a brain atlas and the patient image. Here we aim to improve the results of the state of the art targeting methods and at the same time to reduce the computational time. Our simultaneous segmentation and registration model shows mean STN localization errors statistically similar to the most performing registration algorithms tested so far and to the targeting expert's variability. Moreover, the computational time of our registration method is much lower, which is a worthwhile improvement from a clinical point of view.

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Introduction: Gamma Knife surgery (GKS) is a noninvasive neurosurgical stereotactic procedure, increasingly used as an alternative to open functional procedures. This includes the targeting of the ventrointermediate nucleus of the thalamus (e.g., Vim) for tremor. Objective: To enhance anatomic imaging for Vim GKS using high-field (7 T) MRI and Diffusion Weighted Imaging (DWI). Methods: Five young healthy subjects and two patients were scanned both on 3 and 7 T MRI. The protocol was the same in all cases, and included: T1-weighted (T1w) and DWI at 3T; susceptibility weighted images (SWI) at 7T for the visualization of thalamic subparts. SWI was further integrated into the Gamma Plan Software® (LGP, Elekta Instruments, AB, Sweden) and co-registered with 3T images. A simulation of targeting of the Vim was done using the quadrilatere of Guyot. Furthermore, a correlation with the position of the found target on SWI and also on DWI (after clustering of the different thalamic nuclei) was performed. Results: For the 5 healthy subjects, there was a good correlation between the position of the Vim on SWI, DWI and the GKS targeting. For the patients, on the pretherapeutic acquisitions, SWI helped in positioning the target. For posttherapeutic sequences, SWI supposed position of the Vim matched the corresponding contrast enhancement seen at follow-up MRI. Additionally, on the patient's follow-up T1w images, we could observe a small area of contrast-enhancement corresponding to the target used in GKS (e.g., Vim), which belongs to the Ventral-Lateral-Ventral (VLV) nuclei group. Our clustering method resulted in seven thalamic groups. Conclusion: The use of SWI provided us with a superior resolution and an improved image contrast within the central gray matter, enabling us to directly visualize the Vim. We additionally propose a novel robust method for segmenting the thalamus in seven anatomical groups based on DWI. The localization of the GKS target on the follow-up T1w images, as well as the position of the Vim on 7 T, have been used as a gold standard for the validation of VLV cluster's emplacement. The contrast enhancement corresponding to the targeted area was always localized inside the expected cluster, providing strong evidence of the VLV segmentation accuracy. The anatomical correlation between the direct visualization on 7T and the current targeting methods on 3T (e.g., quadrilatere of Guyot, histological atlases, DWI) seems to show a very good anatomical matching.

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The mouse has emerged as an animal model for many diseases. At IRO, we have used this animal to understand the development of many eye diseases and treatment of some of them. Precise evaluation of vision is a prerequisite for both these approaches. In this unit we describe three ways to measure vision: testing the optokinetic response, and evaluating the fundus by direct observation and by fluorescent angiography.

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This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

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In addition to being instrumental to the protection of mucosal epithelia, secretory IgA (SIgA) adheres to and is transported by intestinal Peyer's patch (PP) M cells. The possible functional reason for this transport is unknown. We have thus examined in mice the outcome of SIgA delivered from the intestinal lumen to the cells present in the underlying organized mucosa-associated lymphoreticular tissue. We show selective association of SIgA with dendritic cells and CD4(+) T and B lymphocytes recovered from PP in vitro. In vivo, exogenously delivered SIgA is able to enter into multiple PP lining the intestine. In PP, SIgA associates with and is internalized by dendritic cells in the subepithelial dome region, whereas the interaction with CD4(+) T cells is limited to surface binding. Interaction between cells and SIgA is mediated by the IgA moiety and occurs for polymeric and monomeric molecular forms. Thus, although immune exclusion represents the main function of SIgA, transport of the Ab by M cells might promote Ag sampling under neutralizing conditions essential to the homeostasis of mucosal surfaces.

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The mammalian target of rapamycin (mTOR), which exists in two functionally distinct complexes, mTORC1 and mTORC2 plays an important role in tumor growth. Whereas the role of mTORC1 has been well characterized in this process, little is known about the functions of mTORC2 in cancer progression. In this study, we explored the specific role of mTORC2 in colon cancer using a short hairpin RNA expression system to silence the mTORC2-associated protein rictor. We found that downregulation of rictor in HT29 and LS174T colon cancer cells significantly reduced cell proliferation. Knockdown of rictor also resulted in a G1 arrest as observed by cell cycle analysis. We further observed that LS174T cells deficient for rictor failed to form tumors in a nude mice xenograft model. Taken together, these results show that the inhibition of mTORC2 reduces colon cancer cell proliferation in vitro and tumor xenograft formation in vivo. They also suggest that specifically targeting mTORC2 may provide a novel treatment strategy for colorectal cancer.