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A direct slicing technique for the 3D printing of implicitly represented medical models
- Source :
- Computers in biology and medicine. 135
- Publication Year :
- 2021
-
Abstract
- In conventional medical image printing methods, volumetric medical data needs to be conversed into STereo Lithography (STL) format, the most commonly used format for representing geometric models for 3D printing. However, this STL conversion process is not only time consuming, but more importantly, it often leads to the loss of accuracy. It has become a critical factor hindering the printing efficiency and precision of organ models. By examining the key characteristics of discrete medical volume data, this paper proposes a direct slicing technique for printing implicitly represented 3D medical models. The proposed method mainly consists of three algorithms: (1) A layer-based contour extraction algorithm for discrete volume data; (2) An inner shell construction algorithm based on discrete point differential indentation; (3) An infill generation algorithm based on the constructed virtual contour and scan lines. The proposed method has been applied to the slicing of several organ models for experiments, and the ratios of time cost and memory cost between the conventional method and the proposed method are about 4–100 and 1.1 to 1.4 respectively, which demonstrate that the proposed method has a great improvement in both time and space performance when compared with the conventional STL-based method. Our technique extends the direct input format of geometric models for additive manufacturing. That is, discrete volume data can be used as a direct input for additive manufacturing without conversion to STL format.
- Subjects :
- 0301 basic medicine
Computer science
business.industry
Process (computing)
3D printing
Health Informatics
Slicing
Scan line
Computer Science Applications
03 medical and health sciences
030104 developmental biology
0302 clinical medicine
Printing, Three-Dimensional
Key (cryptography)
Point (geometry)
business
Lithography
Algorithm
030217 neurology & neurosurgery
Algorithms
Volume (compression)
Subjects
Details
- ISSN :
- 18790534
- Volume :
- 135
- Database :
- OpenAIRE
- Journal :
- Computers in biology and medicine
- Accession number :
- edsair.doi.dedup.....cea03447e6e8b478d16d4419e8f041ee