| Title |
Micro-computed tomography characterization of tissue engineering scaffolds: effects of pixel size and rotation step
|
|---|---|
| Published in |
Journal of Materials Science: Materials in Medicine, July 2017
|
| DOI | 10.1007/s10856-017-5942-3 |
| Pubmed ID | |
| Authors |
Ibrahim Fatih Cengiz, Joaquim Miguel Oliveira, Rui L. Reis |
| Abstract |
Quantitative assessment of micro-structure of materials is of key importance in many fields including tissue engineering, biology, and dentistry. Micro-computed tomography (µ-CT) is an intensively used non-destructive technique. However, the acquisition parameters such as pixel size and rotation step may have significant effects on the obtained results. In this study, a set of tissue engineering scaffolds including examples of natural and synthetic polymers, and ceramics were analyzed. We comprehensively compared the quantitative results of µ-CT characterization using 15 acquisition scenarios that differ in the combination of the pixel size and rotation step. The results showed that the acquisition parameters could statistically significantly affect the quantified mean porosity, mean pore size, and mean wall thickness of the scaffolds. The effects are also practically important since the differences can be as high as 24% regarding the mean porosity in average, and 19.5 h and 166 GB regarding the characterization time and data storage per sample with a relatively small volume. This study showed in a quantitative manner the effects of such a wide range of acquisition scenarios on the final data, as well as the characterization time and data storage per sample. Herein, a clear picture of the effects of the pixel size and rotation step on the results is provided which can notably be useful to refine the practice of µ-CT characterization of scaffolds and economize the related resources. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 48 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 15% |
| Researcher | 7 | 15% |
| Student > Bachelor | 4 | 8% |
| Student > Master | 4 | 8% |
| Student > Doctoral Student | 3 | 6% |
| Other | 7 | 15% |
| Unknown | 16 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 12 | 25% |
| Medicine and Dentistry | 6 | 13% |
| Materials Science | 4 | 8% |
| Biochemistry, Genetics and Molecular Biology | 3 | 6% |
| Agricultural and Biological Sciences | 2 | 4% |
| Other | 3 | 6% |
| Unknown | 18 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 July 2017.
All research outputs
#17,906,525
of 22,990,068 outputs
Outputs from Journal of Materials Science: Materials in Medicine
#1,170
of 1,406 outputs
Outputs of similar age
#225,812
of 314,950 outputs
Outputs of similar age from Journal of Materials Science: Materials in Medicine
#6
of 8 outputs
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