MR-guided Focused Ultrasound (MRgFUS) is a novel, groundbreaking technology that allows incisionless surgery. In essential tremor, the target spot is the Ventralis Intermidius Thalamic (Vim) nucleus, located in the hemisphere contralateral to the affected body part.
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Respiratory binning of free-breathing magnetic resonance imaging data reduces motion blurring; however, it exacerbates noise and introduces severe artifacts due to undersampling. Deep neural networks can remove artifacts and noise but usually require high-quality ground truth images for training. This study aimed to develop a network that can be trained without this requirement.
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The aim of this study was to investigate the impact of a novel iterative denoising and image enhancement technique in T1-weighted precontrast and postcontrast volume-interpolated breath-hold examination (VIBE) of the abdomen on image quality, noise levels, and diagnostic confidence without change of acquisition parameters.
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