Standardizing image assessment in optical diffraction tomography
| Autor: | Yanping He, Nansen Zhou, Michał Ziemczonok, Yijin Wang, Lei Lei, Liting Duan, Renjie Zhou |
|---|---|
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Optics Letters. 48:395 |
| ISSN: | 1539-4794 0146-9592 |
| Popis: | Optical diffraction tomography (ODT) has gradually become a popular label-free imaging technique that offers diffraction-limited resolution by mapping an object's three-dimensional (3D) refractive index (RI) distribution. However, there is a lack of comprehensive quantitative image assessment metrics in ODT for studying how various experimental conditions influence image quality, and subsequently optimizing the experimental conditions. In this Letter, we propose to standardize the image assessment in ODT by proposing a set of metrics, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and structural distinguishability (SD). To test the feasibility of the metrics, we performed experiments on angle-scanning ODT by varying the number of illumination angles, RI contrast of samples, sample feature sizes, and sample types (e.g., standard polystyrene beads and 3D printed structures) and evaluated the RI tomograms with SNR, CNR, and SD. We further quantitatively studied how image quality can be improved, and tested the image assessment metrics on subcellular structures of living cells. We envision the proposed image assessment metrics may greatly benefit end-users for assessing the RI tomograms, as well as experimentalists for optimizing ODT instruments. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|