CvhSlicer: an interactive cross-sectional anatomy navigation system based on high-resolution Chinese visible human data.

We introduce the design and implementation of an interactive system for the navigation of cross-sectional anatomy based on Chinese Visible Human (CVH) data, named CvhSlicer. This system is featured in real-time computation and rendering of high-resolution anatomical images on standard personal computers (PCs) equipped with commodity Graphics Processing Units (GPUs). In order to load the whole-body dataset into the memory of a common PC, several processing steps are first applied to compress the huge CVH data. Thereafter, an adaptive CPU-GPU balancing scheme is performed to dynamically distribute rendering tasks among CPU and GPU based on parameters of computing resources. Experimental results demonstrate that our system can achieve real-time performance and has great potential to be used in anatomy education.

Volume-based morphometry of brain MR images in adolescent idiopathic scoliosis and healthy control subjects.

BACKGROUND AND PURPOSE: Adolescent idiopathic scoliosis (AIS) is a spinal deformity with unknown cause. Previous studies have suggested that subclinical neurologic abnormalities are associated with AIS. The objective of this prospective study was to characterize systematically neuroanatomic changes in patients with left thoracic AIS vs right thoracic AIS and healthy control subjects by using volume-based morphometry. MATERIALS AND METHODS: Our current study involved 9 girls with left thoracic AIS and 20 girls with right thoracic AIS vs 11 and 17 matched female control subjects, respectively. Voxel-based morphometry (VBM), deformation-based morphometry (DBM), and tensor-based morphometry (TBM) were used to analyze the MR images aligned with a specific brain template of local adolescent girls. The statistical t test was used in VBM and TBM, and the Hotelling T(2) test was applied in DBM. RESULTS: Using VBM, we found statistically significant differences (P < .05) in the white matter attenuation of the genu of the corpus callosum and left internal capsule (left thoracic AIS < control subjects). In contrast, no significant differences were observed between patients with right thoracic AIS and control subjects. CONCLUSIONS: White matter attenuation in the corpus callosum and left internal capsule, responsible for interhemispheric communication and conduit of the corticothalamic projectional fibers, respectively, were found to be significantly lower in left thoracic AIS compared with control subjects; however, this was not the case in right thoracic AIS. Confirmation of the findings is required in future research, which needs to evaluate the relationship of white matter abnormality to curve laterality, pathogenesis, and prognosis in patients with AIS, with biologic significance and possible therapeutic correction.

An adaptive transmission protocol for managing dynamic shared states in collaborative surgical simulation.

A force prediction algorithm is proposed to facilitate virtual-reality (VR) based collaborative surgical simulation by reducing the effect of network latencies. State regeneration is used to correct the estimated prediction. This algorithm is incorporated into an adaptive transmission protocol in which auxiliary features such as view synchronization and coupling control are equipped to ensure the system consistency. We implemented this protocol using multi-threaded technique on a cluster-based network architecture.

An adaptive framework using cluster-based hybrid architecture for enhancing collaboration in surgical simulation.

Research on collaborative virtual surgery opens the opportunity for simulating the cooperative work during surgical operations. It is however a challenging task to design and implement a high performance collaborative surgical simulation system because of the difficulty in maintaining a high level of state consistency under limited network transmission capacity. In this paper, we present an adaptive framework using cluster-based hybrid architecture to support real-time collaboration in surgical simulation. In addition to the TCP communication protocol, the framework is also equipped with UDP for multicasting, allowing for a flexible strategy to reduce network latency. A set of techniques was proposed to assure reliable transmission on top of standard yet unreliable multicast protocols. Experimental results demonstrate that this framework can support collaborative surgical simulation with lower network latencies than traditional client-server architecture.

Photorealistic virtual anatomy based on Chinese Visible Human data.

Virtual reality based learning of human anatomy is feasible when a database of 3D organ models is available for the learner to explore, visualize, and dissect in virtual space interactively. In this article, we present our latest work on photorealistic virtual anatomy applications based on the Chinese Visible Human (CVH) data. We have focused on the development of state-of-the-art virtual environments that feature interactive photo-realistic visualization and dissection of virtual anatomical models constructed from ultra-high resolution CVH datasets. We also outline our latest progress in applying these highly accurate virtual and functional organ models to generate realistic look and feel to advanced surgical simulators.

Thin sectional anatomy, three-dimensional reconstruction and visualization of the heart from the Chinese Visible Human.

The research aimed to provide sectional anatomic and three-dimensional (3D) virtual anatomic bases for imaging diagnosis and surgical operation by the use of data from the heart of the first Chinese digitized Visible Human. Data from the series of thin sections of the heart were analyzed and input into an SGI workstation, and 3D reconstruction and virtualization of the heart were performed. Each image of sectional anatomy was clear and the 3D structures of the heart were reconstructed in their entirety. All reconstructed structures can be displayed by multiple structural and color modes, individually or jointly, and can be rotated continuously in any plane. The model of the virtual heart clearly showed fine structures of the heart in random orientation. The dataset of the sectional anatomy provides a fine and integrated morphologic base for imaging diagnosis. The 3D reconstructed images clearly show the internal and entire structures of the heart.

Level set based auto segmentation of the tagged left ventricle MR images.

To facilitate automatic segmentation, we adopt SVM (Support Vector Machine) to localize the left ventricle, and the segmentation is then carried out with narrow band level set. The method of generating the narrow band is improved such that the time used is reduced. Based on the imaging characteristics of the tagged left ventricle MR images, BPV (block-pixel variation) and intensity comparability are introduced to improve the speed term of level set and to increase the precision of segmentation. Our method can perform the segmentation of the tagged left ventricle MR images accurately and automatically.

Interactive navigation and bronchial tube tracking in virtual bronchoscopy.

An interactive virtual environment for simulation of bronchoscopy is developed. Medical doctor can safely plan their surgical bronchoscopy using the virtual environment without any invasive diagnosis which may risk the patient's health. The 3D pen input device of the system allows the doctor to navigate and visualize the bronchial tree of the patient naturally and interactively. To navigate the patient's bronchial tree, a vessel tracking process is required. While manual tracking is tedious and labor-intensive, fully automatic tracking may not be reliable. We propose a semi-automatic tracking technique called Intelligent Path Tracker which provides automation and enough user control during the vessel tracking. To support an interactive frame rate, we also introduce a new volume rendering acceleration technique, named as IsoRegion Leaping. The volume rendering is further accelerated by distributed rendering on a TCP/IP-based network of low-cost PCs. With these approaches, a 256 x 256 x 256 volume data of human lung, can be navigated and visualized at a frame rate of over 10 Hz in our virtual bronchoscopy system.

Estimate of exponential convergence rate and exponential stability for neural networks.

Estimate of exponential convergence rate and exponential stability are studied for a class of neural networks which includes the Hopfield neural networks and the cellular neural networks. Both local and global exponential convergence is discussed. Theorems for estimate of exponential convergence rate are established and the bounds on the rate of convergence are given. The domains of attraction in the case of local exponential convergence are obtained. Simple conditions are presented for checking exponential stability of the neural networks.