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During surgery for foci-related epilepsy, neurosurgeons face significant difficulties in identifying and resecting MRI-negative or deep-seated epileptic foci. Here, we present a neuro-robotic navigation system that is specifically designed for resection of MRI negative epileptic foci. We recruited 52 epileptic patients, and randomly assigned them to treatment group with either neuro-robotic navigation or conventional neuronavigation system. For each patient, in the neuro-robotic navigation group, we integrated multimodality imaging including MRI and PET-CT into the robotic workstation and marked the boundary of foci from the fused image. During surgery, this boundary was delineated by the robotic laser device with high accuracy, guiding resection for the surgeon. For deeply seated foci, we exploited the neuro-robotic navigation system to localize the deepest point with biopsy needle insertion and methylene dye application to locate the boundary of the foci. Our results show that, compared with the conventional neuronavigation, the neuro-robotic navigation system performs equally well in MRI positive epilepsy patients (ENGEL I ratio: 71.4% vs 100%, p = 0.255) systems and show better performance in patients with MRI-negative focal cortical dysplasia (ENGEL I ratio: 88.2% vs 50%, p = 0.0439). At present, there are no documented neurosurgery robots with similar function and application in the field of epilepsy. Our research highlights the added value of using neuro-robotic navigation systems in resection surgery for epilepsy, particularly in cases that involve MRI-negative or deep-seated epileptic foci. [ABSTRACT FROM AUTHOR]

Background: Positron emission tomography (PET)-derived LV MBF quantification is usually measured in standard anatomical vascular territories potentially averaging flow from normally perfused tissue with those from areas with abnormal flow supply. Previously we reported on an image-based tool to noninvasively measure absolute myocardial blood flow at locations just below individual epicardial vessel to help guide revascularization. The aim of this work is to determine the robustness of vessel-specific flow measurements (MBFvs) extracted from the fusion of dynamic PET (dPET) with coronary computed tomography angiography (CCTA) myocardial segmentations, using flow measured from the fusion with CCTA manual segmentation as the reference standard. Methods: Forty-three patients' 13NH3 dPET, CCTA image datasets were used to measure the agreement of the MBFvs profiles after the fusion of dPET data with three CCTA anatomical models: (1) a manual model, (2) a fully automated segmented model and (3) a corrected model, where major inaccuracies in the automated segmentation were briefly edited. Pairwise accuracy of the normality/abnormality agreement of flow values along differently extracted vessels was determined by comparing, on a point-by-point basis, each vessel's flow to corresponding vessels' normal limits using Dice coefficients (DC) as the metric. Results: Of the 43 patients CCTA fully automated mask models, 27 patients' borders required manual correction before dPET/CCTA image fusion, but this editing process was brief (2–3 min) allowing a 100% success rate of extracting MBFvs in clinically acceptable times. In total, 124 vessels were analyzed after dPET fusion with the manual and corrected CCTA mask models yielding 2225 stress and 2122 rest flow values. Forty-seven vessels were analyzed after fusion with the fully automatic masks producing 840 stress and 825 rest flow samples. All DC coefficients computed globally or by territory were ≥ 0.93. No statistical differences were found in the normal/abnormal flow classifications between manual and corrected or manual and fully automated CCTA masks. Conclusion: Fully automated and manually corrected myocardial CCTA segmentation provides anatomical masks in clinically acceptable times for vessel-specific myocardial blood flow measurements using dynamic PET/CCTA image fusion which are not significantly different in flow accuracy and within clinically acceptable processing times compared to fully manually segmented CCTA myocardial masks. [ABSTRACT FROM AUTHOR]

Background: Conventional laparoscopes provide a flat representation of the three-dimensional (3D) operating field and are incapable of visualizing internal structures located beneath visible organ surfaces. Computed tomography (CT) and magnetic resonance (MR) images are difficult to fuse in real time with laparoscopic views due to the deformable nature of soft-tissue organs. Utilizing emerging camera technology, we have developed a real-time stereoscopic augmented-reality (AR) system for laparoscopic surgery by merging live laparoscopic ultrasound (LUS) with stereoscopic video. The system creates two new visual cues: (1) perception of true depth with improved understanding of 3D spatial relationships among anatomical structures, and (2) visualization of critical internal structures along with a more comprehensive visualization of the operating field. Methods: The stereoscopic AR system has been designed for near-term clinical translation with seamless integration into the existing surgical workflow. It is composed of a stereoscopic vision system, a LUS system, and an optical tracker. Specialized software processes streams of imaging data from the tracked devices and registers those in real time. The resulting two ultrasound-augmented video streams (one for the left and one for the right eye) give a live stereoscopic AR view of the operating field. The team conducted a series of stereoscopic AR interrogations of the liver, gallbladder, biliary tree, and kidneys in two swine. Results: The preclinical studies demonstrated the feasibility of the stereoscopic AR system during in vivo procedures. Major internal structures could be easily identified. The system exhibited unobservable latency with acceptable image-to-video registration accuracy. Conclusions: We presented the first in vivo use of a complete system with stereoscopic AR visualization capability. This new capability introduces new visual cues and enhances visualization of the surgical anatomy. The system shows promise to improve the precision and expand the capacity of minimally invasive laparoscopic surgeries. [ABSTRACT FROM AUTHOR]

We present a novel multimodality image registration system for spinal surgery. The system comprises a surface-based algorithm that performs computed tomography/magnetic resonance (CT/MR) rigid registration and MR image segmentation in an iterative manner. The segmentation/registration process progressively refines the result of MR image segmentation and CT/MR registration. For MR image segmentation, we propose a method based on the double-front level set that avoids boundary leakages, prevents interference from other objects in the image, and reduces computational time by constraining the search space. In order to reduce the registration error from the misclassification of the soft tissue surrounding the bone in MR images, we propose a weighted surface-based CT/MR registration scheme. The resultant weighted surface is registered to the segmented surface of the CT image. Contours are generated from the reconstructed CT surfaces for subsequent MR image segmentation. This process iterates till convergence. The registration method achieves accuracy comparable to conventional techniques while being significantly faster. Experimental results demonstrate the advantages of the proposed approach and its application to different anatomies. [ABSTRACT FROM AUTHOR]