Efficient resolution enhancement algorithm for compressive sensing magnetic resonance image reconstruction

Osama A. Omer, M. Atef Bassiouny, Ken’ichi Morooka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Magnetic resonance imaging (MRI) has been widely applied in a number of clinical and preclinical applications. However, the resolution of the reconstructed images using conventional algorithms are often insufficient to distinguish diagnostically crucial information due to limited measurements. In this paper, we consider the problem of reconstructing a high resolution (HR) MRI signal from very limited measurements. The proposed algorithm is based on compressed sensing, which combines wavelet sparsity with the sparsity of image gradients, where the magnetic resonance (MR) images are generally sparse in wavelet and gradient domain. The main goal of the proposed algorithm is to reconstruct the HR MR image directly from a few measurements. Unlike the compressed sensing (CS) MRI reconstruction algorithms, the proposed algorithm uses multi measurements to reconstruct HR image. Also, unlike the resolution enhancement algorithms, the proposed algorithm perform resolution enhancement of MR image simultaneously with the reconstruction process from few measurements. The proposed algorithm is compared with three state-of-the-art CS-MRI reconstruction algorithms in sense of signal-tonoise ratio and full-with-half-maximum values.

Original languageEnglish
Title of host publicationImage Analysis and Processing – ICIAP 2015 - 18th International Conference, Proceedings
EditorsVittorio Murino, Enrico Puppo, Vittorio Murino
PublisherSpringer Verlag
Pages519-527
Number of pages9
ISBN (Print)9783319232300
DOIs
Publication statusPublished - Jan 1 2015
Event18th International Conference on Image Analysis and Processing, ICIAP 2015 - Genoa, Italy
Duration: Sep 7 2015Sep 11 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9279
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on Image Analysis and Processing, ICIAP 2015
CountryItaly
CityGenoa
Period9/7/159/11/15

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Omer, O. A., Bassiouny, M. A., & Morooka, K. (2015). Efficient resolution enhancement algorithm for compressive sensing magnetic resonance image reconstruction. In V. Murino, E. Puppo, & V. Murino (Eds.), Image Analysis and Processing – ICIAP 2015 - 18th International Conference, Proceedings (pp. 519-527). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9279). Springer Verlag. https://doi.org/10.1007/978-3-319-23231-7_46