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Volume visualization has been widely used for decades for analyzing datasets ranging from 3D medical images to seismic data to paleontological data. Many have proposed using immersive virtual reality (VR) systems to view volume visualizations, and there is anecdotal evidence of the benefits of VR for this purpose. However, there has been very little empirical research exploring the effects of higher levels of immersion for volume visualization, and it is not known how various components of immersion influence the effectiveness of visualization in VR. We conducted a controlled experiment in which we studied the independent and combined effects of three components of immersion (head tracking, field of regard, and stereoscopic rendering) on the effectiveness of visualization tasks with two x-ray microscopic computed tomography datasets. We report significant benefits of analyzing volume data in an environment involving those components of immersion. We find that the benefits do not necessarily require all three components simultaneously, and that the components have variable influence on different task categories. The results of our study improve our understanding of the effects of immersion on perceived and actual task performance, and provide guidance on the choice of display systems to designers seeking to maximize the effectiveness of volume visualization applications.
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http://dx.doi.org/10.1109/TVCG.2012.42 | DOI Listing |
Hum Brain Mapp
September 2025
Department of Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany.
Postoperative aphasia (POA) is a common complication in patients undergoing surgery for language-eloquent lesions. This study aimed to enhance the prediction of POA by leveraging preoperative navigated transcranial magnetic stimulation (nTMS) language mapping and diffusion tensor imaging (DTI)-based tractography, incorporating deep learning (DL) algorithms. One hundred patients with left-hemispheric lesions were retrospectively enrolled (43 developed postoperative aphasia, as the POA group; 57 did not, as the non-aphasia (NA) group).
View Article and Find Full Text PDFNat Methods
September 2025
Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK.
Volume correlative light and electron microscopy (vCLEM) is a powerful imaging technique that enables the visualization of fluorescently labeled proteins within their ultrastructural context. Currently, vCLEM alignment relies on time-consuming and subjective manual methods. This paper presents CLEM-Reg, an algorithm that automates the three-dimensional alignment of vCLEM datasets by leveraging probabilistic point cloud registration techniques.
View Article and Find Full Text PDFComput Methods Programs Biomed
September 2025
Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China; Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, 210096, China. Electronic address: xuji@s
Background: Photon counting computed tomography (PCCT) has emerged as a potential technology that is revolutionizing clinical CT imaging. Using photon counting detectors (PCDs), the PCCT counts each X-ray event and measures the corresponding energy above the noise floor with significantly higher spatial resolution. However, the multiple-energy-bin setting and much smaller pixels increase the raw data size of PCCT by 20-100 times compared to traditional CT.
View Article and Find Full Text PDFBraz Oral Res
September 2025
Universidade de Passo Fundo - UPF, School of Dentistry, Post-Graduation Program in Dentistry, Passo Fundo, RS, Brazil.
This study evaluated the influence of a customized healing abutment (CHA) placed on immediate implants. It also assessed bone ridge volume, keratinized mucosal collar, and postoperative pain. Thirty-one patients needing tooth extraction and immediate implant were selected.
View Article and Find Full Text PDFAnn Acad Med Singap
August 2025
Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore.
Introduction: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc.
View Article and Find Full Text PDF