Author

A. Ianus

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· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Organism:human· 1 Organism:rat· 1

Frequent Co-Authors

C. A. M. Gandini Wheeler-kingshott· 1 shared D. Atkinson· 1 shared D. C. Alexander· 1 shared D. Jones· 1 shared E. Panagiotaki· 1 shared F. Dell'Acqua· 1 shared F. Grussu· 1 shared H. Lin· 1 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperBioengineering

C. A. M. Gandini Wheeler-kingshott

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2 papers

Recent·Most cited

Optimisation and Pre-clinical Demonstration of Temporal Diffusion Ratio for Imaging Restricted Diffusion

W. Warner, M. Palombo, R. Cruz, N. Shemesh, D. Jones, F. Dell'Acqua, A. Ianus, I. Drobnjak

This paper optimizes and pre-clinically demonstrates Temporal Diffusion Ratio (TDR) for imaging restricted diffusion, showing improved contrast and correlation with axon diameter in rat spinal cord.

bioRxiv · Jul 25, 2022 · PreprintBioengineering

"Select and retrieve via direct upsampling" network (SARDU-Net): a data-driven, model-free, deep learning approach for quantitative MRI protocol design

F. Grussu, S. B. Blumberg, M. Battiston, L. S. Kakkar, H. Lin, A. Ianus, T. Schneider, S. Singh, R. Bourne, S. Punwani, D. Atkinson, C. A. M. Gandini Wheeler-kingshott, E. Panagiotaki, T. Mertzanidou, D. C. Alexander

SARDU-Net is a deep learning framework for designing quantitative MRI protocols by selecting informative measurements from pilot scans, achieving high information content and reproducibility.

bioRxiv · May 26, 2020 · PreprintBioengineering