SPITFIR(e): a supermaneuverable algorithm for fast denoising and deconvolution of 3D fluorescence microscopy images and videos
Abstract Modern fluorescent microscopy imaging is still limited by the optical aberrations and the photon budget available in the specimen. A direct consequence is the necessity to develop flexible and “off-road” algorithms in order to recover structural details and improve spatial resolution, which...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-26178-y |
| _version_ | 1811175895500587008 |
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| author | Sylvain Prigent Hoai-Nam Nguyen Ludovic Leconte Cesar Augusto Valades-Cruz Bassam Hajj Jean Salamero Charles Kervrann |
| author_facet | Sylvain Prigent Hoai-Nam Nguyen Ludovic Leconte Cesar Augusto Valades-Cruz Bassam Hajj Jean Salamero Charles Kervrann |
| author_sort | Sylvain Prigent |
| collection | DOAJ |
| description | Abstract Modern fluorescent microscopy imaging is still limited by the optical aberrations and the photon budget available in the specimen. A direct consequence is the necessity to develop flexible and “off-road” algorithms in order to recover structural details and improve spatial resolution, which is critical when restraining the illumination to low levels in order to limit photo-damages. Here, we report SPITFIR(e) a flexible method designed to accurately and quickly restore 2D–3D fluorescence microscopy images and videos (4D images). We designed a generic sparse-promoting regularizer to subtract undesirable out-of-focus background and we developed a primal-dual algorithm for fast optimization. SPITFIR(e) is a ”swiss-knife” method for practitioners as it adapts to any microscopy techniques, to various sources of signal degradation (noise, blur), to variable image contents, as well as to low signal-to-noise ratios. Our method outperforms existing state-of-the-art algorithms, and is more flexible than supervised deep-learning methods requiring ground truth datasets. The performance, the flexibility, and the ability to push the spatiotemporal resolution limit of sub-diffracted fluorescence microscopy techniques are demonstrated on experimental datasets acquired with various microscopy techniques from 3D spinning-disk confocal up to lattice light sheet microscopy. |
| first_indexed | 2024-04-10T19:44:27Z |
| format | Article |
| id | doaj.art-b52434e26756432784c634c0ecdc1942 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T19:44:27Z |
| publishDate | 2023-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b52434e26756432784c634c0ecdc19422023-01-29T12:10:18ZengNature PortfolioScientific Reports2045-23222023-01-0113112110.1038/s41598-022-26178-ySPITFIR(e): a supermaneuverable algorithm for fast denoising and deconvolution of 3D fluorescence microscopy images and videosSylvain Prigent0Hoai-Nam Nguyen1Ludovic Leconte2Cesar Augusto Valades-Cruz3Bassam Hajj4Jean Salamero5Charles Kervrann6SERPICO Project-Team, Inria Centre Rennes-Bretagne AtlantiqueSERPICO Project-Team, Inria Centre Rennes-Bretagne AtlantiqueSERPICO Project-Team, Inria Centre Rennes-Bretagne AtlantiqueSERPICO Project-Team, Inria Centre Rennes-Bretagne AtlantiqueLaboratoire Physico-Chimie, Institut Curie, PSL Research University, Sorbonne Universités, CNRS UMR168SERPICO Project-Team, Inria Centre Rennes-Bretagne AtlantiqueSERPICO Project-Team, Inria Centre Rennes-Bretagne AtlantiqueAbstract Modern fluorescent microscopy imaging is still limited by the optical aberrations and the photon budget available in the specimen. A direct consequence is the necessity to develop flexible and “off-road” algorithms in order to recover structural details and improve spatial resolution, which is critical when restraining the illumination to low levels in order to limit photo-damages. Here, we report SPITFIR(e) a flexible method designed to accurately and quickly restore 2D–3D fluorescence microscopy images and videos (4D images). We designed a generic sparse-promoting regularizer to subtract undesirable out-of-focus background and we developed a primal-dual algorithm for fast optimization. SPITFIR(e) is a ”swiss-knife” method for practitioners as it adapts to any microscopy techniques, to various sources of signal degradation (noise, blur), to variable image contents, as well as to low signal-to-noise ratios. Our method outperforms existing state-of-the-art algorithms, and is more flexible than supervised deep-learning methods requiring ground truth datasets. The performance, the flexibility, and the ability to push the spatiotemporal resolution limit of sub-diffracted fluorescence microscopy techniques are demonstrated on experimental datasets acquired with various microscopy techniques from 3D spinning-disk confocal up to lattice light sheet microscopy.https://doi.org/10.1038/s41598-022-26178-y |
| spellingShingle | Sylvain Prigent Hoai-Nam Nguyen Ludovic Leconte Cesar Augusto Valades-Cruz Bassam Hajj Jean Salamero Charles Kervrann SPITFIR(e): a supermaneuverable algorithm for fast denoising and deconvolution of 3D fluorescence microscopy images and videos Scientific Reports |
| title | SPITFIR(e): a supermaneuverable algorithm for fast denoising and deconvolution of 3D fluorescence microscopy images and videos |
| title_full | SPITFIR(e): a supermaneuverable algorithm for fast denoising and deconvolution of 3D fluorescence microscopy images and videos |
| title_fullStr | SPITFIR(e): a supermaneuverable algorithm for fast denoising and deconvolution of 3D fluorescence microscopy images and videos |
| title_full_unstemmed | SPITFIR(e): a supermaneuverable algorithm for fast denoising and deconvolution of 3D fluorescence microscopy images and videos |
| title_short | SPITFIR(e): a supermaneuverable algorithm for fast denoising and deconvolution of 3D fluorescence microscopy images and videos |
| title_sort | spitfir e a supermaneuverable algorithm for fast denoising and deconvolution of 3d fluorescence microscopy images and videos |
| url | https://doi.org/10.1038/s41598-022-26178-y |
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