Training samples-optimizing based dictionary learning algorithm for MR sparse superresolution reconstruction. (January 2018)

• Record Type: Journal Article
• Authors: Li, Jun-Bao; Liu, Huanyu; Pan, Jeng-Shyang; Yao, Hongxun
• Is Part Of: Biomedical signal processing and control. Volume 39(2018)
• Page Start: 177
• Page End: 184
• Publication Date: 2018-01
• Subjects: Super-resolution imaging -- Dictionary diversity -- Sparse reconstruction -- Magnetic resonance imaging; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.08.007
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The sparse reconstruction is improved for MR image super-resolution. The framework of sample-optimized dictionary training is proposed. The dictionary diversity is measured by Gray-consistency&Gradient jointed method. The optimal images are selected based on the complexity for dictionary training. Abstract: Magnetic Resonance (MR) imaging is widely used in diseases diagnosis. The hardware imaging arrives the limitation of resolution, and the high radiation intensity and time of magnetic hurts the human body. The software-based image super-resolution technology is prospective to solve the problem, especially with good excellent performance by sparse reconstruction-based image super-resolution. Dictionary generating is crucial issue of effecting the performance of the super-resolution algorithm, because of without considering the potential discriminative information during dictionary generating. For this problem, we propose the training samples-optimized dictionary learning algorithm for MR sparse super-resolution reconstruction. The gray-consistency & gradient joined diversity-based dictionary representation method is proposed to select the optimal images for the dictionary training. The dictionary training method is evaluated with the framework of sparse reconstruction-based MR imaging. Results show that the proposed dictionary selection framework is feasible and effective to improve the quality of sparse reconstruction-based MR super-resolution.