Spectral estimation for magnetic resonance spectroscopic imaging with spatial sparsity constraints

Qiang Ning, Chao Ma, Zhi-Pei Liang

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

Abstract

This paper addresses the long-standing spectral quantitation problem in magnetic resonance spectroscopic imaging (MRSI). Although a large body of work has been done to develop robust solutions to the problem for practical MRSI applications, the problem remains challenging due to low signal-to-noise ratio (SNR) and model nonlinearity. Building on the existing work on the use of prior knowledge (in the form of spectral basis) for spectral estimation, this paper reformulates spectral quantitation as a joint estimation problem, and utilizes a regularization framework to enforce spatial constraints (e.g., spatial smoothness or transform sparsity) on the spectral parameters. Simulation and experimental results show that the proposed method, by exploiting both the spatial and spectral characteristics of the underlying signals, can significantly improve the estimation accuracy of the spectral parameters over state-of-the-art methods.

Original languageEnglish (US)
Title of host publication2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PublisherIEEE Computer Society
Pages1482-1485
Number of pages4
Volume2015-July
ISBN (Electronic)9781479923748
DOIs
StatePublished - Jul 21 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Other

Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States
CityBrooklyn
Period4/16/154/19/15

Fingerprint

Magnetic resonance
Magnetic Resonance Imaging
Imaging techniques
Signal-To-Noise Ratio
Joints
Signal to noise ratio

Keywords

  • Cramér-Rao bound
  • MRSI
  • sparsity constraint
  • spatial regularization
  • spectral estimation

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Ning, Q., Ma, C., & Liang, Z-P. (2015). Spectral estimation for magnetic resonance spectroscopic imaging with spatial sparsity constraints. In 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015 (Vol. 2015-July, pp. 1482-1485). [7164157] IEEE Computer Society. https://doi.org/10.1109/ISBI.2015.7164157

Spectral estimation for magnetic resonance spectroscopic imaging with spatial sparsity constraints. / Ning, Qiang; Ma, Chao; Liang, Zhi-Pei.

2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015. Vol. 2015-July IEEE Computer Society, 2015. p. 1482-1485 7164157.

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

Ning, Q, Ma, C & Liang, Z-P 2015, Spectral estimation for magnetic resonance spectroscopic imaging with spatial sparsity constraints. in 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015. vol. 2015-July, 7164157, IEEE Computer Society, pp. 1482-1485, 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, United States, 4/16/15. https://doi.org/10.1109/ISBI.2015.7164157
Ning Q, Ma C, Liang Z-P. Spectral estimation for magnetic resonance spectroscopic imaging with spatial sparsity constraints. In 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015. Vol. 2015-July. IEEE Computer Society. 2015. p. 1482-1485. 7164157 https://doi.org/10.1109/ISBI.2015.7164157
Ning, Qiang ; Ma, Chao ; Liang, Zhi-Pei. / Spectral estimation for magnetic resonance spectroscopic imaging with spatial sparsity constraints. 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015. Vol. 2015-July IEEE Computer Society, 2015. pp. 1482-1485
@inproceedings{690a41b8eea542d5851453cf216f58e7,
title = "Spectral estimation for magnetic resonance spectroscopic imaging with spatial sparsity constraints",
abstract = "This paper addresses the long-standing spectral quantitation problem in magnetic resonance spectroscopic imaging (MRSI). Although a large body of work has been done to develop robust solutions to the problem for practical MRSI applications, the problem remains challenging due to low signal-to-noise ratio (SNR) and model nonlinearity. Building on the existing work on the use of prior knowledge (in the form of spectral basis) for spectral estimation, this paper reformulates spectral quantitation as a joint estimation problem, and utilizes a regularization framework to enforce spatial constraints (e.g., spatial smoothness or transform sparsity) on the spectral parameters. Simulation and experimental results show that the proposed method, by exploiting both the spatial and spectral characteristics of the underlying signals, can significantly improve the estimation accuracy of the spectral parameters over state-of-the-art methods.",
keywords = "Cram{\'e}r-Rao bound, MRSI, sparsity constraint, spatial regularization, spectral estimation",
author = "Qiang Ning and Chao Ma and Zhi-Pei Liang",
year = "2015",
month = "7",
day = "21",
doi = "10.1109/ISBI.2015.7164157",
language = "English (US)",
volume = "2015-July",
pages = "1482--1485",
booktitle = "2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015",
publisher = "IEEE Computer Society",

}

TY - GEN

T1 - Spectral estimation for magnetic resonance spectroscopic imaging with spatial sparsity constraints

AU - Ning, Qiang

AU - Ma, Chao

AU - Liang, Zhi-Pei

PY - 2015/7/21

Y1 - 2015/7/21

N2 - This paper addresses the long-standing spectral quantitation problem in magnetic resonance spectroscopic imaging (MRSI). Although a large body of work has been done to develop robust solutions to the problem for practical MRSI applications, the problem remains challenging due to low signal-to-noise ratio (SNR) and model nonlinearity. Building on the existing work on the use of prior knowledge (in the form of spectral basis) for spectral estimation, this paper reformulates spectral quantitation as a joint estimation problem, and utilizes a regularization framework to enforce spatial constraints (e.g., spatial smoothness or transform sparsity) on the spectral parameters. Simulation and experimental results show that the proposed method, by exploiting both the spatial and spectral characteristics of the underlying signals, can significantly improve the estimation accuracy of the spectral parameters over state-of-the-art methods.

AB - This paper addresses the long-standing spectral quantitation problem in magnetic resonance spectroscopic imaging (MRSI). Although a large body of work has been done to develop robust solutions to the problem for practical MRSI applications, the problem remains challenging due to low signal-to-noise ratio (SNR) and model nonlinearity. Building on the existing work on the use of prior knowledge (in the form of spectral basis) for spectral estimation, this paper reformulates spectral quantitation as a joint estimation problem, and utilizes a regularization framework to enforce spatial constraints (e.g., spatial smoothness or transform sparsity) on the spectral parameters. Simulation and experimental results show that the proposed method, by exploiting both the spatial and spectral characteristics of the underlying signals, can significantly improve the estimation accuracy of the spectral parameters over state-of-the-art methods.

KW - Cramér-Rao bound

KW - MRSI

KW - sparsity constraint

KW - spatial regularization

KW - spectral estimation

UR - http://www.scopus.com/inward/record.url?scp=84944319933&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84944319933&partnerID=8YFLogxK

U2 - 10.1109/ISBI.2015.7164157

DO - 10.1109/ISBI.2015.7164157

M3 - Conference contribution

VL - 2015-July

SP - 1482

EP - 1485

BT - 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015

PB - IEEE Computer Society

ER -