In-silico CT phantom of lung tumor from finite element mesh

This study proposes a computational framework for generating in-silico computed tomography (CT) images from accurate geometric reconstructions of the lung with an embedded tumor. Our methodology leverages a finite element (FE) mesh of the human lungs, derived from clinical dynamic CT (4DCT) scans, and integrates an irregularly shaped tumor geometry to generate synthetic CT images. This framework simulates a CT scan by projecting the FE mesh nodes onto a virtual detector to generate a 3D sinogram. The final CT images are then reconstructed from this sinogram using the filtered back-projection method. We evaluated different reconstruction filters, including Ramp, Hamming, and Hann filters, and found a clear trade-off between image noise and spatial resolution. The successfully generated in-silico CT images with a visible tumor demonstrate the potential of this pipeline as a rigorous basis for generating synthetic CT imaging datasets of lung tumors, thereby advancing the development of image analysis tools and data-driven tumor detection models.