Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, Brain, Neuro3D MRI enables thin slices at the cost of long scan times, causing practical challenges. Recently, deep-learning (DL) techniques have successfully accelerated MR scans. However, it is challenging to characterize the image quality (IQ) performance of DL methods by conventional metrics because IQ depends on applications, i.e., how images are used. We evaluate the IQ performance of DL-Speed, our DL-based acceleration method, for 3D T1-weighted MPRAGE brain scans, in 1) post-reconstruction subcortical structure segmentation, and 2) a reader study. The results imply DL-Speed can accelerate scans with reduction factor R=10 while maintaining IQ comparable to standard parallel imaging with R=2.1.
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