Potentials of computer simulation of lung tumors in comparison with ^99mТс-MIBI SPECT/CT data

Abstract

The aim of the study was to develop and validate a software package (SP) for computer simulation of the procedure for examining patients with lung cancer by SPECT/CT and assessing the accuracy of reconstruction of tumor lesions.Material and Methods. Lung scintigraphy for a patient with peripheral squamous cell carcinoma of the upper lobe of the right lung was performed using a two-detector gamma camera GE Discovery NM/CT 670 DR (USA) with high-resolution collimators for an energy of 140 KeV and a radiopharmaceutical (RP) 99mTc-Technetril (MIBI, Diamed, Moscow). The data obtained were subjected to computer processing using a specialized Xeleris 4.0 system from GE (USA). The SP included a program for generating a voxel phantom (“virtual patient”), a program for modeling the “raw” data acquisition (“virtual tomograph”) and an image reconstruction based on the OSEM algorithm (Ordered Subset Expectation Maximization). In order to validate the created SP, computer simulation of the above clinical case was performed. The semi-quantitative comparative image analysis was based on a tumor/background score.Results. There was a good correlation between clinical “raw” data recorded from a real patient and projection data calculated by the Monte Carlo method from a “virtual patient”. The results of the comparative analysis showed that the tumor/background assessment was underestimated in the reconstructed images.Conclusion. The problem of the accuracy of the tumor lesions reconstruction by using standard OSEM reconstruction algorithms has not been studied. This issue is important in the management of patients with tumor lesions of the lungs and requires study and systematization. The SP will be used in further studies to analyze errors and artifacts in images of tumor lesions, as well as to develop approaches to overcome them.

Keywords

• computer simulation
• mathematical phantom
• voxel human phantom
• iteration reconstruction algorithm
• spect/ct
• lung cancer