An Open-Source Tool for Converting 3D Mesh volumes into Synthetic DICOM CT Images for Medical Physics Research

Background Access to medical imaging data is crucial for research, training, and treatment planning in medical imaging and radiation therapy. However, ethical constraints and time-consuming approval processes often limit the availability of such data. This study introduces DICOMator, an open-source Blender add-on designed to address this challenge by enabling the creation of synthetic CT datasets from 3D mesh objects. Purpose DICOMator aims to provide researchers and medical professionals with a flexible tool for generating customisable and qualitatively realistic synthetic CT data, including 4D CT capabilities. The add-on leverages Blender's powerful 3D modelling environment, utilising its mesh manipulation, and rendering capabilities to create synthetic data ranging from simple phantoms to accurate anatomical models. DICOMator incorporates various features to simulate common CT imaging artefacts, bridging the gap between 3D modelling and medical imaging. Methods The methodology involves voxelising 3D mesh objects, assigning appropriate Hounsfield Unit values, and applying artefact simulations. These simulations include detector noise, metal artefacts and partial volume effects. By incorporating these artefacts, DICOMator produces synthetic CT data that more closely resembles real CT scans. The resulting data is then exported in DICOM format, ensuring compatibility with existing medical imaging workflows and treatment planning systems. Results To demonstrate DICOMator's capabilities, three synthetic CT datasets were created: a simple lung phantom to illustrate basic functionality, a realistic cranial CT scan to demonstrate complex anatomical modelling, and a thoracic 4DCT scan featuring multiple breathing phases to demonstrate dynamic imaging capabilities. These examples were chosen to highlight DICOMator's versatility in generating diverse and complex synthetic CT data suitable for various research and educational purposes, from basic quality assurance to advanced motion management studies. Conclusions DICOMator offers a promising solution to the limitations of patient CT data availability in medical physics research. By providing a user-friendly interface for creating customisable synthetic datasets, it has the potential to accelerate research, improve treatment planning algorithms, and enhance educational resources in the field of radiation oncology. Future developments may include integration with other imaging modalities, such as MRI or PET, further expanding its utility in multi-modal imaging research.