Author

Ula Briski

King's College London

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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Neuroscience· 2

Signature Assays, Organisms & Methods

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Assay:imaging· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Organism:human· 1 Approach:statistical model· 1

Frequent Co-Authors

František VášaiD· 2 shared Levente BaljeriD· 2 shared Niall BourkeiD· 2 shared Rosalyn MoraniD· 2 shared Sean DeoniiD· 2 shared Steven WilliamsiD· 2 shared Ashwin V. VenkataramaniD· 1 shared Bokang Methola· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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2 shared papersNeuroscience

František VášaORCID

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2 papers

Recent·Most cited

Deep learning super-resolution of paediatric ultra-low-field MRI without paired high-field scans

Ula Briski, Niall Bourke, Hajer Karoui, Kirsten Ann Donald, Layla E. Bradford, Simone R. Williams, Michal R. Zieff, Sadia Parkar, Sidra Kaleem, Salman Osmani, Sean Deoni, Steven Williams, Khula South Africa Data Collection Team, Donna Herr, Marlie Miles, , František Váša

This study develops a deep learning model to enhance ultra-low-field MRI scans to near high-field resolution without paired high-field data, improving image quality and accessibility.

Scientific Reports · Aug 26, 2026Peer-reviewedNeuroscience

Ultra-low-field brain MRI morphometry: test-retest reliability and correspondence to high-field MRI

František Váša, Carly Bennallick, Niall Bourke, Francesco Padormo, Levente Baljer, Ula Briski, Paul Cawley, Tomoki Arichi, Tobias Wood, David J. Lythgoe, Flavio Dell’Acqua, Thomas Calvert Booth, Ashwin V. Venkataraman, Emil Ljungberg, Sean Deoni, , Steven Williams

This study evaluates the reliability and correspondence of ultra-low-field MRI morphometry to high-field MRI, showing high reliability and correspondence across scan resolutions and contrasts.

Imaging Neuroscience · Jan 1, 2025Peer-reviewedNeuroscience