title: Example-based restoration of high-resolution magnetic resonance image acquisitions.
abstract: Increasing scan resolution in magnetic resonance imaging is possible with advances in acquisition technology. The increase in resolution, however, comes at the expense of severe image noise. The current approach is to acquire multiple images and average them to restore the lost quality. This approach is expensive as it requires a large number of acquisitions to achieve quality comparable to lower resolution images. We propose an image restoration method for reducing the number of required acquisitions. The method leverages a high-quality lower-resolution image of the same subject and a database of pairs of high-quality low/high-resolution images acquired from different individuals. Experimental results show that the proposed method decreases noise levels and improves contrast differences between fine-scale structures, yielding high signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Comparisons with the current standard method of averaging approach and state-of-the-art non-local means denoising demonstrate the method's advantages.
authors: Martinos center for biomedical imaging, MGH, Harvard Medical School, USA.
authors: Ender
authors: E
authors: Konukoglu
authors: Martinos center for biomedical imaging, MGH, Harvard Medical School, USA.
authors: Andre
authors: A
authors: van der Kouwe
authors: Martinos center for biomedical imaging, MGH, Harvard Medical School, USA.
authors: Mert Rory
authors: MR
authors: Sabuncu
authors: Martinos center for biomedical imaging, MGH, Harvard Medical School, USA.
authors: Bruce
authors: B
authors: Fischl
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity