Interactive mining of neural pathways to preoperative neurosurgical planning

Abstract

Background and objective:

Preoperative understanding of white matter anatomy, including its spatial relationship with pathology and superficial landmarks, is vital for effective surgical planning. The ability to interactively synthesize neural pathways from diffusion data and dynamically discern neuroanatomy-referenced fiber patterns enables neurosurgeons to construct detailed mental models of the patient’s brain and assess surgical risks. We present a novel interactive software designed for real-time mining of neural pathways from diffusion-weighted magnetic resonance imaging (DW-MRI) data. This software leverages a user-guided approach, integrating curvilinear reformatting and surgeon expertise with diffusion tensor imaging (DTI) data, and employs a finite-state machine interaction model to facilitate intuitive use through a windows, icons, menus, and pointers (WIMP) interface.

Methods:

The proposed system merges user analytical skills with neuroanatomy-referenced DTI data, including scalar maps, tensor glyphs, and streamlines, within a visually interactive environment. Key features of the system include optimized GPU-based rendering for enhanced graphical representation and the proposed finite-state machine model that enables seamless interaction through intuitive controls. This approach allows for real-time manipulation of DTI data and dynamic generation of depth maps for each frame, facilitating practical exploration and analysis.

Results:

After testing seven control volumes, our system demonstrates tract reconstruction capabilities comparable to MRTrix software’s. The evaluation of GPU-based fiber tracking and rendering performance, using NVIDIA Nsight Visual Studio Edition, confirms the system’s interactive responsiveness. Preliminary results indicate that the environment effectively extracts critical fibers and evaluates their spatial relationships with surgical targets and landmarks. This functionality provides valuable insights for refining preoperative planning, optimizing surgical approaches, and minimizing potential functional damage.

Conclusion:

Our WIMP-based interactive environment empowers surgeons with enhanced capabilities for real-time manipulation of neuroanatomy-referenced DTI data. Integrating curvilinear reformatting and finite-state machine interaction enhances user experience significantly, making it a valuable tool for improving surgical safety and precision. This low-cost, accessible approach has the potential to facilitate minimally invasive procedures, accurate landmark identification, and reduced functional damage, particularly in resource-limited settings.