Non-iterative image reconstruction from sparse magnetic resonance imaging radial data without priors

Autor: Gengsheng L. Zeng, Edward V. R. DiBella
Jazyk: angličtina
Rok vydání: 2020
Předmět:
lcsh:NC1-1940
Visual Arts and Performing Arts
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Medicine (miscellaneous)
Iterative reconstruction
030204 cardiovascular system & hematology
lcsh:Computer applications to medicine. Medical informatics
030218 nuclear medicine & medical imaging
Image (mathematics)
Tomographic image reconstruction
Fast magnetic resonance imaging
Computer graphics
Analytics reconstruction
03 medical and health sciences
0302 clinical medicine
lcsh:Drawing. Design. Illustration
Prior probability
Computer Science (miscellaneous)
medicine
Under-sampled measurements
lcsh:Computer software
medicine.diagnostic_test
Magnetic resonance imaging
Computer Graphics and Computer-Aided Design
Filtered backprojection
Compressed sensing
lcsh:QA76.75-76.765
Norm (mathematics)
lcsh:R858-859.7
Original Article
Computer Vision and Pattern Recognition
Algorithm
Software
Zdroj: Visual Computing for Industry, Biomedicine, and Art, Vol 3, Iss 1, Pp 1-8 (2020)
Visual Computing for Industry, Biomedicine, and Art
ISSN: 2524-4442
DOI: 10.1186/s42492-020-00044-y
Popis: The state-of-the-art approaches for image reconstruction using under-sampled k-space data are compressed sensing based. They are iterative algorithms that optimize objective functions with spatial and/or temporal constraints. This paper proposes a non-iterative algorithm to estimate the un-measured data and then to reconstruct the image with the efficient filtered backprojection algorithm. The feasibility of the proposed method is demonstrated with a patient magnetic resonance imaging study. The proposed method is also compared with the state-of-the-art iterative compressed-sensing image reconstruction method using the total-variation optimization norm.
Databáze: OpenAIRE
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