Advancing the Volumetric Analysis of Ultra‐Low‐Field Brain MRI Using Image‐to‐Image Translation
Ultra‐low‐field (ULF) MRI offers a promising path to accessible neuroimaging, with potential to address global healthcare disparities and advance population‐level brain health research. However, the inherently low signal‐to‐noise ratio (SNR), reduced spatial resolution, and altered tissue contrasts relative to conventional high‐field (HF) scans are significant barriers to ULF analysis and interpretation. While deep learning (DL) approaches have been proposed to enhance ULF image quality, many rely on synthetic training data due to the lack of available subject‐matched ULF and HF scans, introducing potential “domain shift” errors when applied to real acquisitions. Here, we present a DL framework trained on real ULF and HF‐MRIs to address these limitations and improve ULF‐derived brain volume analysis.