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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2021.3076873
|2 doi
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|a pubmed24n1085.xml
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|a DE-627
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Nehme, Elias
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Learning Optimal Wavefront Shaping for Multi-Channel Imaging
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|c 2021
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 10.12.2021
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|a Date Revised 14.12.2021
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Fast acquisition of depth information is crucial for accurate 3D tracking of moving objects. Snapshot depth sensing can be achieved by wavefront coding, in which the point-spread function (PSF) is engineered to vary distinctively with scene depth by altering the detection optics. In low-light applications, such as 3D localization microscopy, the prevailing approach is to condense signal photons into a single imaging channel with phase-only wavefront modulation to achieve a high pixel-wise signal to noise ratio. Here we show that this paradigm is generally suboptimal and can be significantly improved upon by employing multi-channel wavefront coding, even in low-light applications. We demonstrate our multi-channel optimization scheme on 3D localization microscopy in densely labelled live cells where detectability is limited by overlap of modulated PSFs. At extreme densities, we show that a split-signal system, with end-to-end learned phase masks, doubles the detection rate and reaches improved precision compared to the current state-of-the-art, single-channel design. We implement our method using a bifurcated optical system, experimentally validating our approach by snapshot volumetric imaging and 3D tracking of fluorescently labelled subcellular elements in dense environments
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| 650 |
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4 |
|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Ferdman, Boris
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Weiss, Lucien E
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Naor, Tal
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Freedman, Daniel
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Michaeli, Tomer
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shechtman, Yoav
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 43(2021), 7 vom: 02. Juli, Seite 2179-2192
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:43
|g year:2021
|g number:7
|g day:02
|g month:07
|g pages:2179-2192
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| 856 |
4 |
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|u http://dx.doi.org/10.1109/TPAMI.2021.3076873
|3 Volltext
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