Research- and Service Group
IT & Electronics Development Head: Dr. Stefan Vollmar
(IT & Elektronik-Entwicklung)
Visualization and Analysis of PET data
A software package for image analysis and visualization of volume data generated by medical tomography systems has been developed at our institute: VINCI – “Volume Imaging in Neurological Research, Co-Registration and ROIs included”. It has been developed for the analysis of neurological PET studies including fully automatic co-registration of image volumes and image fusion (multi-modality imaging) in addition to VOI and ROI evaluation.
VINCI is an ongoing project and current development is associated with group Cortical Networks. A „Lite-Version“ is available from the VINCI-Homepage .
(a) a gray scale visualization of the full data range (γ=1.0) (b) gamma-corrected gray scale (γ=0.5) with data range adjusted to emphasize streak artefacts (c) linear gray scale visualization with data range suitable for diagnostic purposes, (d) visualization with „rainbow“ color scale (e) „hot metal“ color table. Further color tables are depicted in (f) to (j). Color scales with distinct levels like „GraySteps“ and „Bronson“ are suitable to visualize statistical properties of activation studies.
Quality Assurance (VHIST)
Methods and tools for analysis of image data are becoming increasingly complex. Thus, a detailed and complete documentation of all processing steps (e.g. reconstruction, quantification and statistical analysis) is desirable, even mandatory for good scientific practice: which software tools in which versions have been used with which arguments.
Reconstruction of PET data has been an active research topic for a number of years (Vollmar et al, HeinzelCluster 2002). A PET study with our HRRT system generates approx. 30 GB (gigabytes) of data (a single patient). Using a compute cluster, an image volume which still has size of about 50 MB (megabytes) is reconstructed from this.
One challenge posed by the HRRT system is to use iterative reconstruction methods - despite the large data sets - to better take into account the scanner’s imaging characteristics. These methods are very time consuming: even with an efficient parallelization of the reconstruction scheme, several hours of computing time on a 16 CPU compute cluster are required (equipment). In addition, a number of processing steps is required to turn the raw data into a quantified image volume: many parameters affect the image quality in subtle ways. The processing „history“ needs to be documented and reproducible (good scientific practice) (Quality Assurance).
The members of the electronics laboratory advice and support the neurological research groups in imaging and electro-physiological techniques. They develop and assemble measuring components and laboratory systems. Their skills cover
The company Medres Medres arose from this group. It develops hardware for use in medical research.
Working topics in detail:
Technologies for medical research