Head model collection for mixed reality navigation in neurosurgical intervention for intracranial lesions

<b>Background</b>Mixed reality navigation (MRN) technology is emerging as an increasingly significant and interesting topic in neurosurgery. MRN enables neurosurgeons to "see through" the head with an interactive, hybrid visualization environment that merges virtual- and physical-world elements. Offering immersive, intuitive, and reliable guidance for preoperative and intraoperative intervention of intracranial lesions, MRN showcases its potential as an economically efficient and user-friendly alternative to standard neuronavigation systems. However, the clinical research and development of MRN systems present challenges: recruiting a sufficient number of patients within a limited timeframe is difficult, and acquiring commercially available, medically significant head or skull models at a low cost is equally challenging. To accelerate the development of novel MRN systems and surmount these obstacles, this study introduces a customized dataset tailored for MRN system development and testing in the neurosurgical domain.<b>What's included</b>Our Dataset encompasses computed tomography<b> (CT)</b> and magnetic resonance imaging<b> (MRI)</b> data from <b>19 patients</b> with intracranial lesions and the corresponding three-dimensional (3D) models of anatomical structures and validation references derived from these data. Models are provided in <b>Wavefront object (OBJ)</b> and <b>Stereolithography (STL) file formats</b>, facilitating the development and evaluation of neurosurgical MRN applications. To facilitate customizing various image processing methodologies by researchers, 19 editable, well-organized <b>M</b><b>edical reality bundle (MRB)</b> files are also provided, which can be opened and re-edited within<b> 3D Slicer software</b>.<b>Image data</b>Multimodal MRI data were acquired using a 1.5 T MRI scanner (Espree, Siemens, Erlangen, Germany), while CT data were collected with a 128 multislice CT scanner (SOMATOM, Siemens, Forchheim, Germany). Multimodal MRI usually includes T1-weighted imaging (<b>T1W</b>I), T1-weighted contrast-enhanced (<b>T1-CE</b>), T2-weighted sequence (<b>T2WI</b>), and Diffusion tensor imaging (<b>DTI</b>). Anonymized data was provided in Nearly Raw Raster Data (<b>NRRD</b>) and DICOM <b>ZIP</b> archive format.<b>Holograms generation</b>Segmentation techniques were applied to develop detailed holographic models for MRN-assisted surgical planning, focusing on critical anatomical and pathological features such as lesion locations, vascular structures, key white matter tracts, as well as planned surgical path. This process, crucial for neuro-oncology and intracerebral hemorrhage interventions, utilized tools like the "Segment Editor" and "UKF Tractography" within 3D Slicer to delineate surgical pathways and critical areas precisely. Through manual and automatic segmentation methods, essential structures were accurately mapped and transformed into comprehensive 3D models via the "Segmentation" and "Model Maker" extensions, enhancing surgical visualization and planning.<b>3D-printed phantom generation</b>3D printing STL files were created from segmented skin surfaces of reference CT/MRI scans using the "Segment Editor" tools in 3D Slicer, such as thresholding and smoothing, to craft a skin surface of 1 mm thickness. Through optimization, these processes yielded cost-effective and high-quality 3D models suitable for researchers. We developed two head phantom variants to support diverse MRN research needs. One with markers on the 3D skin surface and another adding positioning lines, using the "Merge Models" extension for seamless integration. Crucially, we ensured no transformations during model creation to maintain alignment with the reference objects for accurate validation.<b>Subjects metadata</b>Demographic information, including age, gender, and pathological diagnosis, were obtained. 19 cases were chosen (F/M: 7/12, mean age: 54.4 ± 18.5 years). Lesion size and depth were automatically calculated using the "Segment Statistics" and "Model to Model distance" modules within 3D Slicer.<b>Usage note</b>Any individual or institution may freely download, share, copy, or republish the data in any medium or format for reasonable research purposes. The dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)<b>Cite this dataset</b>Qi, Ziyu; Jin, Haitao; Xu, Xinghua; Wang, Qun; Gan, Zhichao; Xiong, Ruochu; et al. (2023). Head model collection for mixed reality navigation in neurosurgical intervention for intracranial lesions. figshare. Dataset. https://doi.org/10.6084/m9.figshare.24550732.v6<b><i>Developers, may your MRN application run at 60 Hz, 0 error(s), 0 warning(s), and be WOW-ed by others!!!</i></b><br>