Realistic breast phantoms with segmented real tumour formations from tomographic images

A common approach in the development and improvement of diagnostic imaging techniques is the use of anthropomorphic phantoms. These phantoms can be physical or computational. In this study the creation of computational breast phantoms with included pathological formations is presented. The creation of the realistic phantoms is achieved by utilizing real patient data in the form of tomographic images. The 3D tumour models are generated by segmenting the regions containing tumour formations in the patient images. The segmentation is performed with a developed software tool based on a semi-automatic algorithm, which makes use of a series of image processing and region growing techniques. The software tool also provides the user an opportunity for corrections after the automated segmentation. Then the acquired flat images are stacked in a 3D voxel matrix. Creation of the computational healthy breast model as well as the compression procedure is achieved with a software tool called BreastSimulator. The healthy breast model and the segmented tumour formation are then interactively combined with a software tool called XRAYImagingSimulator. While the user can select a location for the tumour formation, also an automatic software processing is applied for integration between the two computational models. The simulation procedure for acquiring tomographic images from the created realistic breast phantom with included tumour formation is performed with the XRAYImagingSimulator software tool. Finally, the acquired simulation images are reconstructed with a software tool called FDKR. The combination of mathematical models of the breast and tumour models segmented from real patient data leads to the creation of realistic breast phantoms, which can be used in X-ray imaging simulation studies. The presented approach gives an opportunity for generation of multiple cases of breast cancer; thus allowing for further progress in already existing software models and techniques in diagnostic imaging. Acknowledgements This research is supported by the Bulgarian National Science Fund under grant agreement DN17/2. This project also has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 692097.