{"version":"1.0","type":"rich","title":"Gist 5dd4cc899220d90a2606a1f3376b5f6f","description":"This visualization demonstrates signal-to-noise ratio (SNR) calculation from ADNI phantom MRI data using a Jupyter notebook. It loads NIfTI images with nibabel, applies Otsu thresholding and histogram-based binning to segment structures, then labels connected components with scipy.ndimage. The workflow includes FOV validation, quaternion-based RAS coordinate transformation, and voxel volume checks using custom functions like `bin_data` and `clean_labels`, with matplotlib and prettyplotlib used for plotting. The notebook processes phantom scans to identify regions meeting required volume thresholds.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-5dd4cc899220d90a2606a1f3376b5f6f?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}