Source: Annual Congress 2010 - State of the art imaging

Abstract

Pulmonary function tests currently form an important part of the diagnosis of lung disease, however these techniques are only able to give information about the whole lung. Single Photon Emission Computed Tomography (SPECT) ventilation imaging offers the possibility of determining the presence and extent of regional ventilation dysfunction, which may prove to be more sensitive than lung function measurements as a means of early detection of lung disease.
Central to this is a means of measuring the regional ventilation of the lung. SPECT imaging can provide data representing the ventilation of the lung over the whole volume, but this gives little information about how the ventilation varies with the physical anatomy. For the current study, a method has been developed to obtain the sublobar segments of the lung from High Resolution CT (HRCT) scans and to register these with the SPECT data by means of a Low Resolution CT (LRCT) scan performed on the SPECT machine.
The method is based on analysis of the lung airway tree extracted by a commercially available software package. The airway tree data obtained from the software contains the positions of the branches of the airway tree. Software was developed in Matlab to allocate the voxels in the HRCT data to the sublobes based on proximity to the segmental branches using a Voronoi space division algorithm.
Initial results show that the algorithm was able to divide the lung into the sublobar segments. Further work will be carried out to verify the accuracy of the divisions obtained and to register the HRCT and LRCT data, which will allow the SPECT data to be aligned with the sublobar segments.

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Citations should be made in the following way:
M. Bennett, T. Havelock, J. Fleming (United Kingdom). Automated division of the lung into sub lobar segments based on airway tree anatomy for regional analysis of functional images. Eur Respir J 2010; 36: Suppl. 54, 5276

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