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Purpose: To improve the image resolution of magnetic resonance imaging (MRI), conventional interpolation methods are commonly used to magnify images via various image processing approaches; however, these methods tend to produce artifacts. While super-resolution (SR) schemes have been introduced as an alternative approach to apply medical imaging, previous studies applied SR only to medical images in 8-bit image format. This study aimed to evaluate the effectiveness of sparse-coding super-resolution (ScSR) for improving the image quality of reconstructed high-resolution MR images in 16-bit digital imaging and communications in medicine (DICOM) image format. Materials and Methods: Fifty-nine T1-weighted images (T1), 84 T2-weighted images (T2), 85 fluid attenuated inversion recovery (FLAIR) images, and 30 diffusion-weighted images (DWI) were sampled from The Repository of Molecular Brain Neoplasia Data as testing datasets, and 1307 non-medical images were sampled from the McGill Calibrated Color Image Database as a training dataset. We first trained the ScSR to prepare dictionaries, in which the relationship between low- and high-resolution images was learned. Using these dictionaries, a high-resolution image was reconstructed from a 16-bit DICOM low-resolution image downscaled from the original test image. We compared the image quality of ScSR and 4 interpolation methods (nearest neighbor, bilinear, bicubic, and Lanczos interpolations). For quantitative evaluation, we measured the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Results: The PSNRs and SSIMs for the ScSR were significantly higher than those of the interpolation methods for all 4 MRI sequences (PSNR: p p < 0.05, respectively). Conclusion: ScSR provides significantly higher image quality in terms of enhancing the resolution of MR images (T1, T2, FLAIR, and DWI) in 16-bit DICOM format compared to the interpolation methods.

Application of Sparse-Coding Super-Resolution to 16-Bit DICOM Images for Improving the Image Resolution in MRI