In this paper, a new approach for haptic rendering of high resolution voxel-based anatomic models is presented. For visualization the surface location is determined by a ray-casting algorithm at sub-voxel resolution. Since the same algorithm is used for the haptics as well, a very high level of detail is achieved for haptic feedback. Both graphical and haptic representation is congruent. The in...

background: cone-beam computed tomography (cbct) is a new imaging technology that has been widely used in implantology, oral and maxillofacial surgery and orthodontics. this method provides 3-d images that are composed of voxel, which is the smallest image unit, and determines image resolution. smaller voxel is associated with the higher resolution and also greater radiation exposure. this stud...

objective: the diagnosis of vertical root fracture (vrf) in endodontically treated teeth is a clinical challenge due to lack of specific clinical and radiographic signs. although radiographic evaluation such as cbct is helpful, intracanal posts can produce artifacts and may impair the quality of cbct scans. this study aimed to compare the diagnostic accuracy of different resolutions of cbct in ...

Thickness measurements of the cerebral cortex can aid diagnosis and provide valuable information about the temporal evolution of several diseases such as Alzheimer's, Huntington's, Schizophrenia, as well as normal ageing. The presence of deep sulci and 'collapsed gyri' (caused by the loss of tissue in patients with neurodegenerative diseases) complicates the tissue segmentation due to partial v...

BACKGROUND Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional stu...

PURPOSE Localization of target anatomy and critical structures defined in preoperative MR images can be achieved by means of multi-modality deformable registration to intraoperative CT. We propose a symmetric diffeomorphic deformable registration algorithm incorporating a modality independent neighborhood descriptor (MIND) and a robust Huber metric for MR-to-CT registration. METHOD The method...

Diffusion MRI allows us to make inferences on the structural organisation of the brain by mapping water diffusion to white matter microstructure. However, such a mapping is generally ill-defined; for instance, diffusion measurements are antipodally symmetric (diffusion along x and -x are equal), whereas the distribution of fibre orientations within a voxel is generally not symmetric. Therefore,...

Accurate 3D object detection from point clouds has become a crucial component in autonomous driving. However, the volumetric representations and projection methods previous works fail to establish relationships between local sets. In this paper, we propose Sparse Voxel-Graph Attention Network (SVGA-Net), novel end-to-end trainable network which mainly contains voxel-graph module sparse-to-dense...

The existing 3D deep learning methods adopt either individual point-based features or local-neighboring voxel-based features, and demonstrate great potential for processing data. However, the models are inefficient due to unordered nature of point clouds suffer from large information loss. Motivated by success recent point-voxel representation, such as PVCNN DRINet, we propose a new convolution...

Magnetic resonance imaging (MRI) is a widely used biomedical imaging modality that derives much of its contrast from microscale magnetic field gradients in biological tissues. However, the connection between these sub-voxel field patterns and MRI contrast has not been studied experimentally. Here, we describe a new method to map subcellular magnetic fields in mammalian cells and tissues using n...