Multimodality Neurological Data Visualization With Multi-VOI-Based DTI Fiber Dynamic Integration.

Abstract

Brain lesions are usually located adjacent to critical spinal structures, so it is a challenging task for neurosurgeons to precisely plan a surgical procedure without damaging healthy tissues and nerves. The advancement of medical imaging technologies produces a large amount of neurological data, which are capable of showing a wide variety of brain properties. Advanced algorithms of medical data computing and visualization are critically helpful in efficiently utilizing the acquired data for disease diagnosis and brain function and structure exploration, which is helpful for treatment planning. In this paper, we describe new algorithms and a software framework for multiple volume of interest specified diffusion tensor imaging (DTI) fiber dynamic visualization. The displayed results have been integrated with a volume rendering pipeline for multimodality neurological data exploration. A depth texture indexing algorithm is used to detect DTI fiber tracts in graphics process units (GPUs), which makes fibers to be displayed and interactively manipulated with brain data acquired from functional magnetic resonance imaging, T1- and T2-weighted anatomic imaging, and angiographic imaging. The developed software platform is built on an object-oriented structure, which is transparent and extensible. It provides a comprehensive human-computer interface for data exploration and information extraction. The GPU-accelerated high-performance computing kernels have been implemented to enable our software to dynamically visualize neurological data. The developed techniques will be useful in computer-aided neurological disease diagnosis, brain structure exploration, and general cognitive neuroscience.