Cerebral Blood Flow Estimation Using Classification Techniques On A Sequence Of Low Resolution Tomographic Evolutive Images

Marie Chan; Joseph Aguilar-Martin; Kader Boulanouar; Pierre Celsis; Jean Pierre Marc-Vergnes

doi:10.1117/12.953197

1 May 1989 Cerebral Blood Flow Estimation Using Classification Techniques On A Sequence Of Low Resolution Tomographic Evolutive Images

Marie Chan, Joseph Aguilar-Martin, Kader Boulanouar, Pierre Celsis, Jean Pierre Marc-Vergnes

Author Affiliations +

Proceedings Volume 1090, Medical Imaging III: Image Formation; (1989) https://doi.org/10.1117/12.953197
Event: Medical Imaging III, 1989, Newport Beach, CA, United States

Abstract

In order to improve the performance of the instrumental variable method (IVM) in calculating regional cerebral blood flow (rCBF) using Single Photon Emission Computed Tomography (SPELT), and inert diffusible tracer such as 133Xe, we use Learning Algorithms for Multivariate Data Analysis (LAMDA) to classify the voxels of the images of local concentrations in the brain. The LAMDA method correctly distinguished between extra and intra-cerebral voxels. However the topography of the intra-cerebral classes did not match the Regions Of Interest (ROI) defined on an anatomical basis. Provided that all the intra-cerebral classes contaminated by bone and air passage artefact were rejected, the results given by the NM are in good agreement with those derived by the bolus distribution principle. We thus conclude that LAMDA methods can improve the reliability of images of CBF estimates.

Citation Download Citation

Marie Chan, Joseph Aguilar-Martin, Kader Boulanouar, Pierre Celsis, and Jean Pierre Marc-Vergnes "Cerebral Blood Flow Estimation Using Classification Techniques On A Sequence Of Low Resolution Tomographic Evolutive Images", Proc. SPIE 1090, Medical Imaging III: Image Formation, (1 May 1989); https://doi.org/10.1117/12.953197

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