研究目的
To investigate the effects of single-image super resolution on Fourier-based and image-based methods of scale-up in myocardial scar imaging.
研究成果
Single-image super resolution significantly improves the errors of the images both qualitatively and quantitatively. However, the magnitude of improvement by super resolution compared with interpolation was relatively small in images with Fourier-based scale-up method. These findings provide evidence to support its potential use in myocardial scar imaging, but suggest that the current algorithm of super resolution may be less effective in a Fourier-based scale-up method than an image-based bicubic interpolation.
研究不足
The study did not evaluate the effect of super resolution on images of different slice thickness, did not explore resolution improvement greater than a factor of 4, applied super resolution to patient images using dictionaries created by high-resolution images of the swine heart, and the current super-resolution algorithm is limited to two-dimensional (2-D) images.
1:Experimental Design and Method Selection
The study applied an algorithm for single-image super resolution to myocardial scar imaging to quantitatively assess its effects. The algorithm uses sparse representation and operates by training a pair of low- and high-resolution dictionaries.
2:Sample Selection and Data Sources
High-resolution ex vivo late gadolinium enhancement (LGE) magnetic resonance imaging was performed in postinfarction swine hearts (n = 24). The swine hearts were divided into the training set (n = 14) and the test set (n = 10).
3:List of Experimental Equipment and Materials
1.5-Tesla scanner (Avanto, Siemens Medical Solutions), 3D gradient recalled echo (GRE) sequence, MATLAB R2013a (Mathworks, Inc., Natick, MA).
4:Experimental Procedures and Operational Workflow
In the training set, super-resolution dictionaries with pairs of small matching patches of the high- and low-resolution images were created. In the test set, super resolution recovered high-resolution images from low-resolution images using the dictionaries. The same algorithm was also applied to patient LGE (n = 4).
5:Data Analysis Methods
To measure the differences between the original high-resolution image and the interpolated image or the super resolution image, 4 separate indices were used: mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and universal image quality index (UIQI).
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