CT-Image analyzing using MC simulations to diagnose lung cancer at early stages

Introduction: Monte Carlo(MC) simulation is an accurate method to analyze CT-images. The main purpose of this research was to apply MC modelling to develop an integrated multimodality image display system to aid lung cancer detection. Materials and methods: BEAMnrc was used for MC simulation to create a database of numbers ranged from 1 to 3 for normal tissues include bone, muscle and air and 4 for abnormal tissues. To digitalize the CT-Images, the density and HU number (Hounsfield Units) of targets were considered. The volume of interest for the simulated lung was set to 0.8×0.8×0.5 μm3. The CT-images conversion was repeated 10 times for ten different patients. The results of MC simulation were then directly compared with the results of radiologist's observations. Results: There were 150 suspected locations of abnormal tissues found by MC simulation for all CT-images that 124 of them were confirmed by radiologists as cancerous tissues. The agreement between MC simulation and practical observation was more than 80%, which demonstrates the accuracy of MC model of CT-images. Conclusion: The accuracy of MC simulated imaging system of lung cancer was significant. This method can be exploited to aid screening and improve the diagnosis of lung cancer in clinic.

کلیدواژه ها:

MC simulation ، CT image analyzing ، Hounsfield Units