Spatiotemporal denoising of MR spectroscopic imaging data by low-rank approximations

Author

Nguyen, Hien M. ; Peng, Xi ; Do, Minh N. ; Liang, Zhi-Pei

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA

fYear

2011

fDate

March 30 2011-April 2 2011

Firstpage

857

Lastpage

860

Abstract

This paper addresses the denoising problem associated with magnetic resonance spectroscopic imaging (MRSI), where low signal-to-noise ratio (SNR) has been a critical problem. A new scheme is proposed, which exploits two low-rank structures that exist in MRSI data, one due to partial separability and the other is due to linear predictability. Experimental results from practical data demonstrate that the proposed method provides an effective way to denoise MRSI data while preserving spatial-spectral features in a wide range of SNR values.

Keywords

biomedical MRI; biomedical optical imaging; image denoising; medical image processing; spatiotemporal phenomena; MR spectroscopic imaging data; low signal-noise ratio; low-rank approximations; low-rank structures; magnetic resonance spectroscopic imaging; spatial-spectral features; spatiotemporal denoising; Approximation methods; Image reconstruction; Imaging; Noise measurement; Noise reduction; Signal to noise ratio; Cadzow enhancement; MR spectroscopic imaging; denoising; low-rank approximation; partially-separable functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on

Conference_Location

Chicago, IL

ISSN

1945-7928

Print_ISBN

978-1-4244-4127-3

Electronic_ISBN

1945-7928

Type

conf

DOI

10.1109/ISBI.2011.5872539

Filename

5872539