Currently, surgeons study numerous discrete 2-dimensional (2D) imaging modalities displaying distinct data to plan for operations on 3D patients. Furthermore, viewing this data on a 2D screen is not fully capable of communicating the full extent of the collected data. As the number of imaging modalities grows, the ability to synthesize more data from distinct modalities, which exhibit multiple scales of organization, necessitates new forms of digital and physical representation to condense and curate information for more effective and efficient surgical planning.
3D-printed, patient-specific models have emerged as a new diagnostic tool for surgical planning that has been shown to reduce operating time and surgical complications1. However, the process is time-consuming due to the standard stereolithography (STL) method of 3D printing, which shows a visible loss of data and renders printed objects as solid, homogeneous, and isotropic materials. As a result, 3D printing for surgical planning has been limited to bony structures and simple morphological descriptions of complex organs2. This limitation is a result of an outdated manufacturing paradigm guided by the products and needs of the industrial revolution, where manufactured objects are fully described by their exterior boundaries3. However, the needs of the biomedical industry to replicate human tissue, which displays multiple scales of organization and varying material distributions, necessitate new forms of representation that represent the variations across the entire volume, which change point by point.
To address this issue, a 3D visualization and modeling technique (Figure 1) was developed and coupled with a novel, additive manufacturing process that enables greater control over the mixing and deposition of resins in ultrahigh resolution. This method, called bitmap printing, replicates human anatomy by 3D printing directly from medical images at a level of spatial fidelity and spatial/contrast resolution of advanced imaging technology approaching 15 µm. This enables the precise and graduated control required to replicate variations in morphologically complex soft tissue with no loss or alteration of data from diagnostic source images.