Show simple item record

dc.contributor.advisorAgarwal, Krishna
dc.contributor.authorAcuna Maldonado, Sebastian Andres
dc.date.accessioned2023-11-28T13:49:28Z
dc.date.available2023-11-28T13:49:28Z
dc.date.issued2023-12-11
dc.description.abstractFluorescence Microscopy is still the most preferred tool for studying the cell's inner structures. One of the reasons for this preference is its great selectivity, which allows it to label specific types of structures and visualize them with high contrast. However, its resolution is conventionally limited due to the diffraction of light, which makes the study of cells at scales below 200 nm challenging. To go beyond this limitation, different techniques are required, such as Super-resolution microscopy. The objective of this thesis is to explore and further develop one of these techniques: the Multiple Signal Classification Algorithm (MUSICAL), a computational method that enables the reconstruction of super-resolution images from a temporal stack of diffraction-limited fluorescent images. MUSICAL exploits the fluctuations in light intensity produced by the fluorescent emitters in the sample to improve resolution and contrast. It is based on the separation of a signal and noise component, followed by their subsequent recombination. This thesis includes five publications that contribute to different aspects of MUSICAL. Paper I enhances the usability of the method, bridging the gap between the algorithm and the end-user through an easy-to-use ImageJ plugin. This plugin allows the customization of various parameters for quick testing and facilitates the processing of multi-channel data and long stacks of images. Papers II and III contribute to expanding the technical knowledge of the algorithm by extending its definition and providing further insights into its working mechanism. In these papers, soft MUSICAL and contrast enhancement (CE) are presented for the first time. These methods impose fewer constraints on the separation between signal and noise and rely less on the user's input. Finally, the thesis concludes with Papers IV and V, which provide case studies of practical illumination mechanisms for microscopy that can potentially complement MUSICAL to achieve a resolution gain up to three times the conventional limit. These two systems correspond to chip-based microscopy and structured illumination, whose data was studied and then processed with MUSICAL. Overall, this thesis contributes to the improvement of MUSICAL in three key areas and can be read as a story of the algorithm. Therefore, it also serves as a window briefly showcasing the tremendous potential behind MUSICAL, which will hopefully inspire further developments in super-resolution microscopy.en_US
dc.description.doctoraltypeph.d.en_US
dc.description.popularabstractFluorescence Microscopy is still the most preferred tool for studying the cell's inner structures. One of the reasons is that it is possible to mark and image specific structures inside of it with great contrast. However, in conventional microscopes, the smallest objects that can be studied are close to 200 nm. To surpass this limit, it is required a different set of techniques. The ones under the category of Super-resolution microscopy. This thesis focuses on a super-resolution computational microscopy technique called Multiple Signal Classification Algorithm or just MUSICAL). The work presented in this thesis includes 5 articles that contribute to 3 aspects of the algorithm: usability, technical improvement, and application on different microscopy systems. The first introduces a plugin that allows easy-to-use application of MUSICAL. The second and third present 2 variations of the algorithm that improve the results and simplify the workflow for regular users. The final 2 articles showcase MUSICAL on two different systems where the optics work together with a computational algorithm to achieve super-resolution. This work then represents a big contribution to the super-resolution microscopy community and to MUSICAL in particular.en_US
dc.description.sponsorshipUiT Strategic funding for PhD positions FRIPRO Young funding from RCN (id 288082) ERC Starting Grant funding from Horizon 2020 (804233) FET Open RIA funding from Horizon 2020 (964800)en_US
dc.identifier.isbn978-82-8236-558-1 (printed version)
dc.identifier.isbn978-82-8236-559-8 (electronic/pdf version)
dc.identifier.urihttps://hdl.handle.net/10037/31879
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.relation.haspart<p>Paper I: Acuña, S., Ströhl, F., Opstad, I.S., Ahluwalia, B.S. & Agarwal, K. (2020). MusiJ: an ImageJ plugin for video nanoscopy. <i>Biomedical Optics Express, 11</i>(5), 2548-2559. Also available in Munin at <a href=https://hdl.handle.net/10037/18593>https://hdl.handle.net/10037/18593</a>. <p>Paper II: Acuña, S., Opstad, I.S., Godtliebsen, F., Ahluwalia, B.S. & Agarwal, K. (2020). Soft thresholding schemes for multiple signal classification algorithm. <i>Optics Express, 28</i>, 34434-34449. Also available in Munin at <a href=https://hdl.handle.net/10037/20408>https://hdl.handle.net/10037/20408</a>. <p>Paper III: Acuña, S., Roy, M., Villegas-Hernandez, L.E., Dubey, V.K., Ahluwalia, B.S. & Agarwal, K. (2021). Deriving high contrast fluorescence microscopy images through low contrast noisy image stacks. <i>Biomedical Optics Express, 12</i>, 5529-5543. Also available in Munin at <a href=https://hdl.handle.net/10037/23845>https://hdl.handle.net/10037/23845</a>. <p>Paper IV: Opstad, I.S., Hansen, D.H., Acuña Maldonado, S.A., Ströhl, F., Priyadarshi, A., Tinguely, J., … Agarwal, K. (2021). Fluorescence fluctuation-based super-resolution microscopy using multimodal waveguided illumination. <i>Optics Express, 29</i>, 23368-23380. Also available in Munin at <a href=https://hdl.handle.net/10037/21953>https://hdl.handle.net/10037/21953</a>. <p>Paper V: Butola, A., Acuna, S., Hansen, D.H. & Agarwal, K. (2022). Scalable-resolution structured illumination microscopy. <i>Optics Express, 30</i>, 43752-43767. Also available in Munin at <a href=https://hdl.handle.net/10037/27943>https://hdl.handle.net/10037/27943</a>.en_US
dc.relation.isbasedonOpstad, I.S. (2021). Replication data for: Fluorescence fluctuation-based super-resolution microscopy using multimodal waveguided illumination. DataverseNO, V1, <a href=https://doi.org/10.18710/JEN4SB>https://doi.org/10.18710/JEN4SB</a>.en_US
dc.relation.isbasedonOpstad, I.S. (2021). 3DSIM data of mitochondria in the cardiomyoblast cell-line H9c2 adapted to either glucose or galactose. DataverseNO, V2, <a href=https://doi.org/10.18710/PDCLAS>https://doi.org/10.18710/PDCLAS</a>.en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)en_US
dc.subjectMicroscopyen_US
dc.subjectSuper-resolutionen_US
dc.subjectMUSICALen_US
dc.subjectFluorescenceen_US
dc.subjectStructure-illuminationen_US
dc.titleMultiple Signal Classification Algorithm: A computational microscopy tool for fluorescence microscopyen_US
dc.typeDoctoral thesisen_US
dc.typeDoktorgradsavhandlingen_US


File(s) in this item

Thumbnail
No Thumbnail [100%x240]

This item appears in the following collection(s)

Show simple item record

Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)