Optical tomography measurements and reconstructions of a multiple-scattering 3d-printed microphantom
<p>This dataset contains 2 sets of measurements of a 3d-printed microphantom, carried out with optical diffraction tomography system at Warsaw University of Technology. The measurements are conducted for 2 different wavelengths: 633nm and 835nm. Also, tomographic reconstructions of these datasets are shown. The reconstructions were computed with 3 algorithms: GPSC [1], MSBP-I [2] and MSBP-E [3]. Additionally, model of the 3D-printed microphantom is given.</p> <p>All files are *.mat files.</p> <p>In the reconstruction files there are 4 variables:</p> <ul> <li>REC - reconstruction matrix with information about 3D refractive index values in the microphantom</li> <li>dx - sample size in the reconstruction in x-y direction</li> <li>dz - sample size in the reconstruction in z direction (if not given, dz=dx)</li> <li>niter - number of iterations that were computed to generate the reconstruction</li> </ul> <p>The variables in the sinogram files are:</p> <ul> <li>dx - sample size in tomographic projections</li> <li>lambda - wavelength</li> <li>M - magnification in the optical system</li> <li>n_immersion - refractive index of the immersion medium</li> <li>NA - numerical aperture of the optical system</li> <li>rayXY - x-y coordinates of vectors representing illumination directions from which tomographic projections were acquired</li> <li>SINOamp - amplitude distribution of tomographic projections</li> <li>SINOph - phase distributions of tomographic projections</li> </ul> <p>The variables in the phantom model files are:</p> <ul> <li>dx - sample size</li> <li>n_immersion - refractive index of simulated immersion</li> <li>n_phantom - refractive index of the phantom model</li> </ul> <p>[1] W. Krauze, “Optical diffraction tomography with finite object support for the minimization of missing cone artifacts,”277<br> Biomed. optics express 11, 1919–1926 (2020)<br> [2] S. Chowdhury, M. Chen, R. Eckert, D. Ren, F. Wu, N. Repina, and L. Waller, “High-resolution 3D refractive index292<br> microscopy of multiple-scattering samples from intensity images,” Optica 6, 1211 (2019).<br> [3] U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Learning approach288<br> to optical tomography,” Optica 2, 517 (2015).</p>
ShareScore
44/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 8
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 4