Name
Title | ||
|---|---|---|
A Direct Slicing Technique For The 3d Printing Of Implicitly Represented Medical Models |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.compbiomed.2021.104534 | COMPUTERS IN BIOLOGY AND MEDICINE |
Keywords | DocType | Volume |
Slicing, 3D printing, Discrete medical volume data, G-code generator, Implicit modeling | Journal | 135 |
ISSN | Citations | PageRank |
0010-4825 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingqi Hong | 1 | 0 | 0.34 |
| Lingli Lin | 2 | 0 | 0.34 |
| Qingde Li | 3 | 0 | 0.34 |
| Ziyou Jiang | 4 | 0 | 0.34 |
| Jun Fang | 5 | 0 | 0.34 |
| Beizhan Wang | 6 | 0 | 0.34 |
| Kunhong Liu | 7 | 0 | 0.34 |
| Qingqiang Wu | 8 | 0 | 0.34 |
| Chenxi Huang | 9 | 1 | 1.36 |