gms | German Medical Science

63rd Annual Meeting of the German Society of Neurosurgery (DGNC)
Joint Meeting with the Japanese Neurosurgical Society (JNS)

German Society of Neurosurgery (DGNC)

13 - 16 June 2012, Leipzig

A framework for cortical risk map calculation in trajectory planning – initial experience

Meeting Abstract

Search Medline for

  • M.H.A. Bauer - Klinik für Neurochirurgie, Universitätsklinikum Marburg, Marburg
  • C. Kappus - Klinik für Neurochirurgie, Universitätsklinikum Marburg, Marburg
  • B. Carl - Klinik für Neurochirurgie, Universitätsklinikum Marburg, Marburg
  • C. Nimsky - Klinik für Neurochirurgie, Universitätsklinikum Marburg, Marburg

Deutsche Gesellschaft für Neurochirurgie. Japanische Gesellschaft für Neurochirurgie. 63. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), Joint Meeting mit der Japanischen Gesellschaft für Neurochirurgie (JNS). Leipzig, 13.-16.06.2012. Düsseldorf: German Medical Science GMS Publishing House; 2012. DocP 099

doi: 10.3205/12dgnc486, urn:nbn:de:0183-12dgnc4861

Published: June 4, 2012

© 2012 Bauer et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.



Objective: Precise planning of trajectories in neurosurgery can be a challenging and time consuming task in navigated or stereotactic procedures, as navigated needle-biopsy, stereotactic-biopsy, stereoencephalography or others. While manual trajectory planning includes analyzing each considered trajectory, checking for risk structures or other areas that should not be crossed or affected, pre-processing of the image data and automated analysis and evaluation of trajectories can speed up the planning time. To improve planning time we present our first experience on our framework for automatic calculation of cortical risk maps (CRM), projecting trajectories touching or crossing risk structures like small vessels or the ventricles on a color-coded cortical map.

Methods: After registration of all data sets, the target point is determined besides a cortical area of interest (AOI) for surgical access. Time-of-flight-angiography data is used for vessel detection; the ventricles are pre-segmented using a region growing approach in T1-weighted MR images. All possible access points are selected from the manually defined cortical AOI, used as endpoints for the trajectories. Each trajectory is then analyzed (enlarged with a defined offset) for crossing risk structures or being close to them and a color-coded risk index is projected to the skull, resulting in low risk zones and high risk zones for trajectory testing. We collected high resolution structural MRI data sets as needed on a 3T Siemens Trio MRI using a 32-channel head coil.

Results: Based on the pre-segmented maps for ventricles and information of vessel structures as main risk structures, cortical risk maps were calculated for defined target points and access areas within 2–3 minutes for trajectories enlarged with different offsets. For quality control, 30 trajectories for risk and no-risk access points were randomly picked and manually analyzed in all cases. On average, 27.4 ±1.14 (91.33% ±3.80%) out of 30 no-risk trajectories and 28.20 ±0.84 (94.00% ±2.79%) out of 30 risk trajectories were classified correctly with the CRM tool.

Conclusions: We presented a new framework for calculation of cortical risk maps in trajectory planning, open for including further data sets like diffusion tensor imaging with reconstructed white matter tracts as risk structures. Even though different trajectory analyzing procedures for risk calculation can be included allowing controlled evaluation of different analyzing procedures, data quality and pre-processing steps.