[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126737-en":3,"doc-seo-126737-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126737,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Fast multicolour optical sectioning over extended fields of view with patterned illumination and machine learning","Structured illumination rejects out-of-focus signal, enabling high-speed imaging over large areas with widefield optics, but reconstruction artefacts and poor low signal-to-noise performance limit current optical-sectioning methods. This work combines multicolour interferometric pattern generation with machine learning for high-contrast, real-time reconstruction robust to background noise and sample motion. Validation in silico and imaging of fixed, live biological samples and synthetic biosystems achieve live reconstruction at 11 Hz across a 44×44 μm² field of view, with acquisition speeds exceeding 154 Hz.","1 Fast, multicolour optical sectioning over  \n2 extended fields of view with patterned  \n3 illumination and machine learning  \n4 EDWARD N. WARD, 1,† REBECCA M. MCCLELLAND, 1,† JACOB R.  \n5 LAMB , 1 ROGER RUBIO-SÁNCHEZ , 1,2 CHARLES N. CHRISTENSEN , 1  \n6 BISMOY MAZUMDER , 1 SOFIA KAPSIANI , 1 LUCA MASCHERONI , 1  \n7 LORENZO DI MICHELE , 1,2 GABRIELE S. KAMINSKI SCHIERLE , 1 AND  \n8 CLEMENS F. KAMINSKI1,*  \n9 1 Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, CB3 0AS, 10 UK  \n11 2 fabriCELL, Molecular Sciences Research Hub, Imperial College London, London, W12 0BZ, UK  \n12 † The authors contributed equally to this work.  \n*  \n13 [cfk23@cam.ac.uk](cfk23@cam.ac.uk)  \n14 Abstract:  \n15 Structured illumination can reject out-of-focus signal from a sample, enabling high-speed  \n16 and high-contrast imaging over large areas with widefield detection optics. However, this  \n17 optical-sectioning technique is currently limited by image reconstruction artefacts and poor  \n18 performance at low signal-to-noise ratios. We combine multicolour interferometric pattern  \n19 generation with machine learning to achieve high-contrast, real-time reconstruction of image  \n20 data that is robust to background noise and sample motion. We validate the method in silico and  \n21 demonstrate imaging of diverse specimens, from fixed and live biological samples to synthetic  \n22 biosystems, reconstructing data live at 11 Hz across a 44 × 44􀁠􀀼 2 field of view, and demonstrate  \n23 image acquisition speeds exceeding 154 Hz.  \n24 1. Introduction  \n25 Widefield fluorescence microscopy permits the imaging of biological structures with a high  \n26 specificity, however, out-of-focus light limits image contrast. Unless planar excitation profiles are  \n27 used for illumination – such as in lightsheet [1] or HiLo [2] microscopy – fluorescent probes  \n28 located above and below the focal plane contribute to the signal collected in the final image.  \n29 Scanning confocal microscopy circumvents this problem through the use of a pinhole to  \n30 physically reject out-of-focus signal. While this is effective at increasing contrast, only a  \n31 single point in the sample can be imaged at a time and images must be built up sequentially, 32 pixel-by-pixel, greatly increasing acquisition time. Furthermore, high excitation powers are  \n33 required to compensate for the signal lost through the pinhole.  \n34 A different approach to achieve optical sectioning (OS) is through structured illumination  \n35 microscopy (SIM) [3] . Here, a fluorescent sample is illuminated by patterned excitation light  \n36 and the emitted fluorescence is imaged with widefield detection. In super-resolution (SR) SIM, 37 interference patterns are produced by the interaction of the patterned excitation with the structures  \n38 of the sample and these interference patterns are used to extract high-resolution information  \n39 about the sample [4, 5] . OS-SIM makes use of the fact that the modulation depth of the excitation  \n40 pattern is highest in the in-focus plane and decreases rapidly with distance from it. Hence, the  \n41 in-and out-of-focus structures can be distinguished by differences in stripe contrast. To achieve  \n42 this, the sample is typically illuminated with a sinusoidal stripe pattern, and three sequential  \n43 images are acquired as the pattern is shifted in phase over the sample. Under these conditions, 44 only the in-focus structures will show a change in intensity between the phase shifted images  \n45 and therefore these can be extracted and the three raw images reconstructed into one optically  \n46 sectioned image. This method was pioneered by Neil et al. [3] who used the squared difference  \n47 (SD) between the three phases to reconstruct the image:  \n􀀞2􀀧 = (􀀞1 − 􀀞2 )2 + (􀀞2 − 􀀞3 )2 + (􀀞1 − 􀀞3 )2 , (1)  \n48 where 􀀞􀀧 is the reconstructed image and 􀀞􀀽 represents the n-th phase image of the sample  \n49 under striped illumination with the phase s","cbCaipARyjaP9ulL","https://ap.wps.com/l/cbCaipARyjaP9ulL","pdf",10836644,1,15,"English","en",105,"# Abstract\n# Introduction\n## Limits of widefield fluorescence and confocal microscopy\n## Structured illumination microscopy and OS-SIM principles\n## Pattern generation and hardware constraints\n## Challenges for multi-colour imaging","[{\"question\":\"What problem does structured illumination optical sectioning address in microscopy?\",\"answer\":\"It rejects out-of-focus signal so contrast improves over wide areas using widefield detection optics.\"},{\"question\":\"Why do existing OS-SIM reconstructions struggle in practice?\",\"answer\":\"They can produce reconstruction artefacts and perform poorly at low signal-to-noise ratios, with noise and phase errors amplifying problems.\"},{\"question\":\"How does the proposed method improve reconstruction speed and robustness?\",\"answer\":\"It uses multicolour interferometric patterned illumination combined with machine learning to deliver high-contrast real-time reconstructions robust to background noise and sample motion, reaching 11 Hz live reconstruction and over 154 Hz acquisition.\"}]","Fast multicolour optical sectioning over extended fields of view with patterned illumination and machine learning | 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problem does structured illumination optical sectioning address in microscopy?","Question",{"text":75,"@type":76},"It rejects out-of-focus signal so contrast improves over wide areas using widefield detection optics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why do existing OS-SIM reconstructions struggle in practice?",{"text":80,"@type":76},"They can produce reconstruction artefacts and perform poorly at low signal-to-noise ratios, with noise and phase errors amplifying problems.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed method improve reconstruction speed and robustness?",{"text":84,"@type":76},"It uses multicolour interferometric patterned illumination combined with machine learning to deliver high-contrast real-time reconstructions robust to background noise and sample motion, reaching 11 Hz live reconstruction and over 154 Hz 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