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Proof of concept: Portable ultra-low-field MRI for the assessment of brain tumors.

Neuro-oncology practice

Authors: Thomas Zeyen, Hemmen Sabir, Tobias Bauer, Oliver Henke, Nils Lehnen, Mousa Zidan, Simon Olbrich, Annalena Lange, Justus Bisten, Anne Groteklaes, Jennifer Faber, Lea Röver, Niklas Schäfer, Johannes Weller, Walter Bruchhausen, Alexander Radbruch, Ulrich Herrlinger, Theodor Rüber

BACKGROUND: High-field (HF) MRI is a standard diagnostic tool for brain cancer, but its high cost and technical demands limit accessibility in low- and middle-income countries. Recent advancements in ultra-low field (ULF) MRI technology, including the development of portable scanners, offer a promising solution to these challenges. This study evaluates the diagnostic capabilities of ULF-MRI in detecting brain cancer and compares radiological evaluation using ULF- with HF-MRI.

METHODS: Consecutive patients with suspected or confirmed brain tumors undergoing routine 3T HF-MRI at the University Hospital Bonn were recruited for this study and underwent ULF-MRI. Eligible patients were at least 18 years old and had MRI-abnormalities in the HF-MRI. The 0.064 Tesla Swoop portable MR Imaging System was utilized. HF-MRI and ULF-MRI scans were independently evaluated by two experienced neuroradiologists and results were compared.

RESULTS: Thirteen patients were recruited, of whom 11 (85%) were diagnosed with brain tumors. In 11/11 (100%) patients with brain tumors, ULF-MRI identified tumor lesions corresponding to the findings of HF-MRI. In 7/11 (63.6%) identification of all tumor lesions could be achieved. Three of four further relevant imaging findings in HF-MRI (e.g. acute hydrocephalus or concomitant ischemia) were also found in in ULF-MRI.

CONCLUSION: This single-center study demonstrates that ULF-MRI is a practical tool in neuro-oncology, which may particularly be helpful in resource-limited settings. Further research is required to define the role of ULF-MRI alongside existing imaging modalities for brain cancer diagnosis and management.

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PMID: 42111036

Participating cluster members