Lightweight encoding for medical additive manufacturing files.

Abstract

Background: Additive manufacturing technology has revolutionized the medical field by enabling the production of customized implants with complex internal structures that enhance mechanical properties and biocompatibility. These intricate designs often result in exceedingly large 3D model files due to the high level of detail required. The substantial data volume poses significant file storage, transmission, and processing challenges. Traditional compression methods cannot encode complex models efficiently without compromising accuracy and compatibility. This study aims to develop a lightweight encoding strategy for 3D geometric files in medical additive manufacturing that significantly reduces file size while preserving data accuracy and compatibility with existing industry-standard formats.

Methods: We proposed a geometric relationship-based clustering method for the topological reconstruction of mesh models. The method involves non-uniform and multi-scale mesh simplification to retain critical features and reduce redundant data. By encoding these repetitive features only once, the encoding strategy enhances compression efficiency. We implemented compatible encoding schemes for the AMF (Additive Manufacturing File) and 3MF (3D Manufacturing Format) data formats, referred to as Lite AMF and Lite 3MF. Experiments on three medical implant models were conducted to evaluate the effectiveness of the proposed method.

Results: The proposed encoding strategy achieved significant file size reductions, with Lite AMF and Lite 3MF formats reducing file sizes by 81.99% and 91.34%, respectively, compared to the original formats. The compression algorithm effectively preserved the geometric characteristics of the models. The Hausdorff distance between the original and compressed models was less than 0.001 for all three models, indicating high fidelity and maintaining accuracy within the acceptable manufacturing tolerances of current medical additive manufacturing technologies.

Conclusion: The lightweight encoding strategy effectively reduces the file size of complex medical 3D models by over 80% while preserving data accuracy and compatibility with existing formats. By efficiently encoding repetitive structures and optimizing mesh data, the method enhances storage and transmission efficiency, addressing the challenges of large data volumes in medical additive manufacturing. The compatibility with standard AMF and 3MF formats ensures that the encoded models can be directly utilized in existing 3D printing software without modification.