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Markus Barth

As seen in: MSN Österreich, Color Research & Application, Nature, Wiley Online Library, Frankfurter Allgemeine Zeitung, BioRxiv, JNeurosci, Water Online, ESG Investor
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Articles

Multimodal ultra-high-field MRI, clinical, cognitive, and genetic profiles across the ALS–FTD spectrum

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By Thomas Shaw, Markus Barth, Steffen Bollmann, Pierrick Bourgeat, Jeryn Chang, Harriet Dempsey-Jones, Amir Fazlollahi, Jurgen Fripp, Christine Guo, Robert Henderson, Jinglei Lv, Pamela A McCombe, Aswin Narayanan, Shyuan T. Ngo, Vinh Nguyen, Kieran S O’Brien, Gail Robinson, Simon Robinson, Olivier Salvado, Ashley Stewart, Frederik J. Steyn, Saskia Bollmann, Aiman Al Najjar, Natalie Garden, Elaine Kuan
| Nature Verified
Abstract This dataset was acquired and curated to explore the spectrum of Motor Neuron Disease (MND) and Fronto-Temporal Dementia (FTD) with Ultra-High Field Magnetic Resonance Imaging (7 Tesla) and compare these to non-neurodegenerative disease controls (known colloquially as “The 7 T hEalthy Ageing study [7TEA]”).
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SynPoC: a high-quality generative diffusion model for transforming ultra-low-field point-of-care MRI using high-field MRI representations - Scientific Reports

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By Markus Barth, Katie McMahon
| Nature Verified
Abstract Ultra-low-field (ULF) point-of-care (PoC) Magnetic Resonance Imaging (MRI) offers a promising pathway to improve accessibility in medical imaging due to its portability and lower cost. However, the diagnostic utility of ULF MRI is currently limited by lower image quality, particularly in signal-to-noise ratio, resolution, and contrast. To address this, we introduce SynPoC, a generative diffusion model designed to enhance ULF MRI by synthesizing high-field MRI-like images.
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An Annotated Multi-Site and Multi-Contrast Magnetic Resonance Imaging Dataset for the study of the Human Tongue Musculature - Scientific Data

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By Fernanda L. Ribeiro, Xincheng Ye, Shyuan T. Ngo, Frederik J. Steyn, Markus Barth, Steffen Bollmann
| Nature Verified
Abstract This dataset provides the first annotated, openly available MRI-based imaging dataset for investigations of tongue musculature, including multi-contrast and multi-site MRI data from non-disease participants. The present dataset includes 47 participants collated from three studies: BeLong (four participants; T2-weighted images), EATT4MND (19 participants; T2-weighted images), and BMC (24 participants; T1-weighted images).
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