Author

R. Christensen

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 4 Biophysics· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Organism:C. elegans· 3 Approach:deep learning· 3 Assay:imaging· 3 Approach:microscopy & imaging analysis· 3 Research Focus:clustering· 2 Research Focus:dimensionality reduction· 2 Organism:Drosophila· 2 Assay:fluorescence / live-cell microscopy· 2

Frequent Co-Authors

H. Shroff· 5 shared X. Han· 4 shared Y. Su· 4 shared Y. Wu· 4 shared D. Colon-Ramos· 3 shared J. Liu· 3 shared M. Guo· 3 shared P. La Riviere· 3 shared

Activity by Year

2017-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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5 shared papersBioengineering

H. Shroff

Mutual co-authors in cluster

Author profile →

6 papers

Recent·Most cited

The differing prevalences of propionate and butyrate producing bacteria in the human gut microbiota

R. Christensen, Y. H. D. Wang, M. Arnoldini, J. Cremer

The paper analyzes the prevalence of propionate and butyrate producing pathways in the human gut microbiota across different age groups and birth methods, finding significant variations linked to host physiology.

bioRxiv · Nov 27, 2023 · PreprintMicrobiology

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

M. Guo, Y. Wu, C. M. Hobson, Y. Su, S. Qian, E. Krueger, R. Christensen, G. Kroeschell, J. Bui, M. Chaw, L. Zhang, J. Liu, X. Hou, X. Han, Z. Lu, , H. Shroff

This paper introduces a deep learning method to correct optical aberrations in fluorescence microscopy, improving image quality and enabling better downstream analysis.

bioRxiv · Oct 15, 2023 · PreprintBioengineering

Incorporating the image formation process into deep learning improves network performance in deconvolution applications

Y. Li, Y. Su, M. Guo, X. Han, J. Liu, H. Vishwasrao, X. Li, R. Christensen, T. Sengupta, M. Moyle, J. Chen, T. B. Usdin, D. Colon-Ramos, H. Liu, Y. Wu, H. Shroff

This paper introduces RLN, a deep learning method for 3D fluorescence microscopy deconvolution that improves speed, performance, and generalizability compared to existing methods.

bioRxiv · Mar 5, 2022 · PreprintBiophysics

Multiview super-resolution microscopy

Y. Wu, X. Han, Y. Su, M. Glidewell, J. S. Daniels, J. Liu, T. Sengupta, I. Rey-Suarez, R. Fischer, A. Patel, C. Combs, J. Sun, X. Wu, R. Christensen, C. Smith, , H. Shroff

This paper introduces a multiview super-resolution microscopy approach that enhances confocal microscopy resolution and reduces phototoxicity across multiple scales.

bioRxiv · May 21, 2021 · PreprintBioengineering

A polymer gel index-matched to water enables diverse applications in fluorescence microscopy

X. Han, Y. Su, H. White, K. M. O'Neill, N. Y. Morgan, R. Christensen, D. Potarazu, H. D. Vishwasrao, S. Xu, Y. Sun, S.-y. Huang, M. W. Moyle, Q. Dai, Y. Pommier, E. Giniger, , H. Shroff

This paper introduces a polymer gel matched to water's refractive index, enabling high-resolution imaging of living cells and organisms in thick layers.

bioRxiv · Oct 4, 2020Bioengineering

Reflective imaging improves resolution, speed, andcollection efficiency in light sheet microscopy

Y. Wu, A. Kumar, C. Smith, E. Ardiel, P. Chandris, R. Christensen, I. Rey-Suarez, M. Guo, H. Vishwasrao, J. Chen, J. Tang, A. Upadhyaya, P. La Riviere, H. Shroff

This paper introduces a reflective imaging technique that enhances resolution, speed, and collection efficiency in light sheet microscopy without altering the microscope hardware.

bioRxiv · Jun 23, 2017Bioengineering