Multimodal Image-Based Virtual Reality Presurgical Simulation and Evaluation for Trigeminal Neuralgia and Hemifacial Spasm

Yao, Shujing; Zhang, Jiashu; Zhao, Yining; Hou, Yuanzheng; Xu, Xinghua; Zhang, Zhizhong; Kikinis, Ron; Chen, Xiaolei

doi:10.1016/j.wneu.2018.02.069

Cited by 31 publications

(15 citation statements)

References 27 publications

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“…Therefore, this study may indicate that reading multimodal fusion images can overcome the differences in clinical experience and correctly identify deep Sylvian veins. Previous reports have indicated that three-dimensionally printed models 25 ) and multimodal image-based virtual reality 26 ) are useful tools for not only preoperative planning and simulation but also for education. Multimodal fusion imaging can be a preoperative education tool that bridges the experience gap between younger trainees and senior surgeons.…”

Section: Discussionmentioning

confidence: 99%

Usefulness of the Multimodal Fusion Image for Visualization of Deep Sylvian Veins

Nakase

Takeshima

Konishi

et al. 2022

Neurol. Med. Chir.(Tokyo)

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The preoperative assessment of cerebral veins is important to avoid unexpected cerebral venous infarction in the neurosurgical setting. However, information is particularly limited regarding deep Sylvian veins, which occasionally disturb surgical procedures for cerebral anterior circulation aneurysms. The predictability of detecting deep Sylvian veins and their tributaries using a modern multimodal fusion image was aimed to be evaluated. Moreover, 51 patients who underwent microsurgery for unruptured cerebral aneurysms with Sylvian fissure dissection were retrospectively reviewed. The visualization of the four components of the deep Sylvian veins in conventional computed tomography (CT) venography and multimodal fusion images was evaluated. To compare the detection accuracy among these radiological images, the sensitivity and specificity for the detection of each of the four venous structures were calculated in comparison with those of intraoperative inspections. The kappa coefficients were also measured and the inter-rater agreement for each venous structure in each radiological image was examined. In all veins, the multimodal fusion image exhibited a high detection rate without statistical difference from intraoperative inspections (P = 1.0). However, CT venography exhibited a low detection rate with a significant difference from intraoperative inspections in the common vertical trunk (P = 0.006) and attached vein (P = 0.008). The kappa coefficients of the fusion image ranged from 0.73 to 0.91 and were superior to those of CT venography for all venous structures. This is the first report to indicate the usefulness of a multimodal fusion image in evaluating deep Sylvian veins, especially for the detection of nontypical, relatively small veins with large individual variability.

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Section: Discussionmentioning

confidence: 99%

Usefulness of the Multimodal Fusion Image for Visualization of Deep Sylvian Veins

Nakase

Takeshima

Konishi

et al. 2022

Neurol. Med. Chir.(Tokyo)

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“…Yao et al used data primarily at 1.5 Tesla to visualize them in 3D with surface rendering. The information of the different data was only fused in the 3D visualization by selecting the modality that offers the highest contrast of the respective target tissue [ 26 ]. In this strategy, major information of structures within the CSF cisterns was not taken into account during image processing and was therefore not provided in the resulting 3D representations.…”

Section: Discussionmentioning

confidence: 99%

Data fusion and 3D visualization for optimized representation of neurovascular relationships in the posterior fossa

et al. 2022

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Background Reliable 3D visualization of neurovascular relationships in the posterior fossa at the surface of the brainstem is still critical due to artifacts of imaging. To assess neurovascular compression syndromes more reliably, a new approach of 3D visualization based on registration and fusion of high-resolution MR data is presented. Methods A total of 80 patients received MRI data with 3D-CISS and 3D-TOF at 3.0 Tesla. After registration and subsequent segmentation, the vascular information of the TOF data was fused into the CISS data. Two 3D visualizations were created for each patient, one before and one after fusion, which were verified with the intraoperative situation during microvascular decompression (MVD). The reproduction quality of vessels was evaluated with a rating system. Results In all cases, the presented approach compensated for typical limitations in the 3D visualization of neurovascular compression such as the partial or complete suppression of larger vessels, suppression of smaller vessels at the CSF margin, and artifacts from heart pulsation. In more than 95% of the cases of hemifacial spasm and glossopharyngeal neuralgia, accurate assessment of the compression was only possible after registration and fusion. In more than 50% of the cases with trigeminal neuralgia, the presented approach was crucial to finding the actually offending vessel. Conclusions 3D visualization of fused image data allows for a more complete representation of the vessel-nerve situation. The results from this approach are reproducible and the assessment of neurovascular compression is safer. It is a powerful tool for planning MVD.

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“…Manava et al (2020) visualized various types of nerves including the trigeminal nerve through a series of direct volume rendering methods (Hastreiter et al, 2002;Naraghi et al, 2004Naraghi et al, , 2007 to analyze and compare different results of exact reproducible anatomical 3D-representations of the ventrolateral medulla (VLM) in patients with arterial hypertension. To address the feasibility and predictive value of multimodal image-based virtual reality in detecting and assessing features of neurovascular confliction, Yao et al (2018) utilized the open source 3D Slicer to perform multimodal 3D images reconstruction to generate multimodal virtual reality (VR) images for detection of possible NVC in the cerebellopontine angle. To validate the accuracy of image-bsed pre-operative segmentation using the gold standard endoscopic and microscopic findings for localization and pre-opetative diagnosis of the offending vessel, Dolati et al (2015) used BrainLab iPlanNet (BrainLab AG, Munich, Germany) to segment and reconstruct 20 3.0T MRI images with TN and hemifacial spasm.…”

Section: Semi-automatic 3d Visualizationmentioning

confidence: 99%

“…The presence of neurovascular compression (NVC), the morphological and spatial relationships of vascular and nerve, the severity of the compression have significant impacts on the outcome of MVD (Li et al, 2004;Sindou et al, 2007;Yao et al, 2018). Accurate preoperative assessment of the characteristics of the NVC is not only critical to the surgical planning and the outcome of trigeminal neuralgia treatment, but also has the potential to shorten the operative time (Miller et al, 2008;Leal et al, 2010Leal et al, , 2011Zeng et al, 2013;Xie et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning

et al. 2021

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Trigeminal neuralgia caused by paroxysmal and severe pain in the distribution of the trigeminal nerve is a rare chronic pain disorder. It is generally accepted that compression of the trigeminal root entry zone by vascular structures is the major cause of primary trigeminal neuralgia, and vascular decompression is the prior choice in neurosurgical treatment. Therefore, accurate preoperative modeling/segmentation/visualization of trigeminal nerve and its surrounding cerebrovascular is important to surgical planning. In this paper, we propose an automated method to segment trigeminal nerve and its surrounding cerebrovascular in the root entry zone, and to further reconstruct and visual these anatomical structures in three-dimensional (3D) Magnetic Resonance Angiography (MRA). The proposed method contains a two-stage neural network. Firstly, a preliminary confidence map of different anatomical structures is produced by a coarse segmentation stage. Secondly, a refinement segmentation stage is proposed to refine and optimize the coarse segmentation map. To model the spatial and morphological relationship between trigeminal nerve and cerebrovascular structures, the proposed network detects the trigeminal nerve, cerebrovasculature, and brainstem simultaneously. The method has been evaluated on a dataset including 50 MRA volumes, and the experimental results show the state-of-the-art performance of the proposed method with an average Dice similarity coefficient, Hausdorff distance, and average surface distance error of 0.8645, 0.2414, and 0.4296 on multi-tissue segmentation, respectively.

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Multimodal Image-Based Virtual Reality Presurgical Simulation and Evaluation for Trigeminal Neuralgia and Hemifacial Spasm

Cited by 31 publications

References 27 publications

Usefulness of the Multimodal Fusion Image for Visualization of Deep Sylvian Veins

Usefulness of the Multimodal Fusion Image for Visualization of Deep Sylvian Veins

Data fusion and 3D visualization for optimized representation of neurovascular relationships in the posterior fossa

Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning

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