Conference proceeding
Super-resolution MRI using finite rate of innovation curves
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), Vol.2015-, pp.1248-1251
04/2015
DOI: 10.1109/ISBI.2015.7164100
Abstract
We propose a two-stage algorithm for the super-resolution of MR images from their low-frequency k-space samples. In the first stage we estimate a resolution-independent mask whose zeros represent the edges of the image. This builds off recent work extending the theory of sampling signals of finite rate of innovation (FRI) to two-dimensional curves. We enable its application to MRI by proposing extensions of the signal models allowed by FRI theory, and by developing a more robust and efficient means to determine the edge mask. In the second stage of the scheme, we recover the super-resolved MR image using the discretized edge mask as an image prior. We evaluate our scheme on simulated single-coil MR data obtained from analytical phantoms, and compare against total variation reconstructions. Our experiments show improved performance in both noiseless and noisy settings.
Details
- Title: Subtitle
- Super-resolution MRI using finite rate of innovation curves
- Creators
- Greg Ongie - Dept. of Math., Univ. of Iowa, Iowa City, IA, USAMathews Jacob - Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
- Resource Type
- Conference proceeding
- Publication Details
- 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), Vol.2015-, pp.1248-1251
- DOI
- 10.1109/ISBI.2015.7164100
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Publisher
- IEEE
- Language
- English
- Date published
- 04/2015
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984070969202771
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