Sparse representation-based MRI super-resolution reconstruction

Yun-Heng Wang, Jiaqing Qiao, Jun-Bao Li, Ping Fu, Shu-Chuan Chu, John Roddick

    Research output: Contribution to journalArticlepeer-review

    50 Citations (Scopus)


    Magnetic Resonance Imaging (MRI) data collection is influenced by SNR, hardware, image time, and other factors. The super-resolution analysis is a critical way to improve the imaging quality. This work presents a framework of super-resolution MRI via sparse reconstruction, and this method is promising to solve the data collection limitations. A novel dictionary training method for sparse reconstruction for enhancing the similarity of sparse representations between the low resolution and high resolution MRI block pairs through simultaneous training two dictionaries. Low resolution MRI blocks generate the high resolution MRI blocks with proposed sparse representation (SR) coefficients. Comprehensive evaluations are implemented to test the feasibility and performance of the SR-MRI method on the real database.

    Original languageEnglish
    Pages (from-to)946-953
    Number of pages8
    Issue number1
    Publication statusPublished - 2014


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