Computer-Assisted Visualization of Central Lung Tumours Based on 3-Dimensional Reconstruction

Abstract

Each year, lung cancer is diagnosed in approximately 1.4 million people world-wide. There are approx. 205,000 new cases in the USA and approx. 345,000 in Europe [Cancer Atlas of the Federal Republic of Germany]. US researchers at the Centers for Disease Control and Prevention [CDC] expect the number of deaths to continue to rise as an immediate consequence of smoking. During the 20th Century, tobacco consumption caused about 100 million deaths, and this number is estimated at about one billion deaths world—wide for the 21st Century [CDC]. Axial 2-D computed tomography (CT) of the thorax is the accepted and established standard method used in pre-operative morphological imaging diagnosis in patients with central benign or malignant lung tumours. Tumour size, infiltration of central structures or segmental relatedness are the decisive parameters that the surgeon can derive in variable quality from 2-dimensional images, in order to assess the technical operability and the extent of the resection. However, the availability and quality of CTs vary greatly from one hospital to the next. The surgeon is thus often given print-outs on paper of a CT with 5 mm slices. Comprehensive coverage across the board with multi-slice detector CT (MSDCT) with 1mm slices and the possibility of interactive observation by the operator is, however, not yet available. Improved imaging and image-processing is crucial to the further optimization of pre-operative risk assessment, especially with reference to population development in industrialized nations. Multimorbid patients, patients with severe obstructive or restrictive diseases of the respiratory tract, as well as patients of advanced age, often limit the – actually required – tactical oncological extent of resection due to a post-operative lung function that is too low. Demands must therefore be made for a best-possible pre-operative localization and functional diagnostics, also with reference to the constant rise in patient age for the corresponding co-morbidities.