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Method Article
This method demonstrates a voxel-based 3D printing workflow, which prints directly from medical images with exact spatial fidelity and spatial/contrast resolution. This enables the precise, graduated control of material distributions through morphologically complex, graduated materials correlated to radiodensity without loss or alteration of data.
Most applications of 3-dimensional (3D) printing for presurgical planning have been limited to bony structures and simple morphological descriptions of complex organs due to the fundamental limitations in accuracy, quality, and efficiency of the current modeling paradigm. This has largely ignored the soft tissue critical to most surgical specialties where the interior of an object matters and anatomical boundaries transition gradually. Therefore, 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.
Presented here is a novel technique to create 3D models directly from medical images, which are superior in spatial and contrast resolution to current 3D modeling methods and contain previously unachievable spatial fidelity and soft tissue differentiation. Also presented are empirical measurements of novel, additively manufactured composites that span the gamut of material stiffnesses seen in soft biological tissues from MRI and CT. These unique volumetric design and printing methods allow for deterministic and continuous adjustment of material stiffness and color. This capability enables an entirely new application of additive manufacturing to presurgical planning: mechanical realism. As a natural complement to existing models that provide appearance matching, these new models also allow medical professionals to "feel" the spatially varying material properties of a tissue simulant-a critical addition to a field in which tactile sensation plays a key role.
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.
NOTE: 3D Slicer Medical Image Computing Software4 (see the Table of Materials) was used for the work completed in sections 1 through 3.
1. Data input
2. Manipulations
NOTE: A masking step is required if the anatomy is sufficiently complex, to the point where surrounding tissues and extraneous data are present after modifications to the Volume Properties.
3. Slicing
NOTE: This process bypasses the traditional 3D printing method by sending the slice files directly to the 3D printing instead of an STL mesh file. In the following steps, slices will be created from the volume rendering. The Bitmap Generator module is a custom-built extension. This can be downloaded from Extensions Manager.
4. Dithering
NOTE: Adobe Photoshop (see the Table of Materials) was used for the work completed in section 4.
5. Voxel printing
NOTE: Stratasys GrabCAD5 was used for the work completed in section 5.
A positive result, as shown in Figure 2 and Figure 3, will be a direct translation of the volume rendering as defined in steps 1.2.5 or 2.1.1.4. The final model should visually match the volume rendering in size, shape, and color. Along this process, there are numerous steps where an error can occur, which will affect one or more of the properties listed above.
Issues related to the uniform scaling, as shown in
The current representational framework that the majority, if not all, of digital modeling tools employ today results in the STL file format8. Nevertheless, the specific nature of this paradigm has proven inadequate when trying to express the granular or hierarchical structure of more complex, natural materials. With the arrival of recent additive manufacturing techniques such as multimaterial 3D printing, highly tuned and highly optimized objects can be produced, which display gradual material tra...
N.J. is an author on a patent application filed by the University of Colorado Regents that describes methods like those described in this work (application no. US16/375,132; publication no. US20200316868A1; filed 04 April 2019; published 08 October 2020). All other authors declare that they have no competing interests.
We thank AB Nexus and the State of Colorado for their generous support of our scientific research into voxel printing for presurgical planning. We thank L. Browne, N. Stence, and S. Sheridan for providing data sets used in this study. This study was funded by the AB Nexus Grant and the State of Colorado Advanced Industries Grant.
Name | Company | Catalog Number | Comments |
3D Slicer Image Computing Platform | Slicer.org | Version 4.10.2–4.11.2 | |
GrabCAD | Stratasys | 1.35 | |
J750 Polyjet 3D Printer | Stratasys | ||
Photoshop | Adobe | 2021 |
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