Keywords: Machine Learning/Artificial Intelligence, BrainFor brain volume measurement (BVM), High-resolution (HR) MR images have shown to provide accurate results at small subcortical areas. However, prolonged scan time remains a classical challenge for 3D MRI. We implemented a combined technique, deep learning based super-resolution (DL-SR) and low-resolution Compressed Sensing (CS) 3D-TFE-T1W with acceleration factor 4 to generate Super-resolution (SR) images under one minute scan time. The results show that DL-SR model is able to improve image resolution, in which no significant differences (p>0.01) in quantitative volumetric measurement between reference and DL-SR at subcortical regions, except for caudate region.
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