PittsburghV1, Main, Exploration, bibRecord, 004132

Accelerated MR Parameter Mapping with Low-Rank and Sparsity Constraints

Identifieur interne : 004132 ( Main/Exploration ); précédent : 004131; suivant : 004133

Accelerated MR Parameter Mapping with Low-Rank and Sparsity Constraints

Auteurs : Bo Zhao [États-Unis] ; Wenmiao Lu [États-Unis] ; T. Kevin Hitchens [États-Unis] ; Fan Lam [États-Unis] ; Chien Ho [États-Unis] ; Zhi-Pei Liang [États-Unis]

Source :

Magnetic resonance in medicine [ 0740-3194 ] ; 2014.

RBID : PMC:4344441

Descripteurs français

English descriptors

KwdEn :
- Algorithms, Brain (anatomy & histology), Data Compression (methods), Female, Humans, Image Enhancement (methods), Image Interpretation, Computer-Assisted (methods), Magnetic Resonance Imaging (methods), Male, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted.
MESH :
- anatomy & histology : Brain.
- methods : Data Compression, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging.
- Algorithms, Female, Humans, Male, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted.

Abstract

Purpose:

To enable accurate MR parameter mapping with accelerated data acquisition, utilizing recent advances in constrained imaging with sparse sampling.

Theory and Methods:

A new constrained reconstruction method based on low-rank and sparsity constraints is proposed to accelerate MR parameter mapping. More specifically, the proposed method simultaneously imposes low-rank and joint sparse structures on contrast-weighted image sequences within a unified mathematical formulation. With a pre-estimated subspace, this formulation results in a convex optimization problem, which is solved using an efficient numerical algorithm based on the alternating direction method of multipliers.

Results:

To evaluate the performance of the proposed method, two application examples were considered: i) T₂ mapping of the human brain, and ii) T₁ mapping of the rat brain. For each application, the proposed method was evaluated at both moderate and high acceleration levels. Additionally, the proposed method was compared with two state-of-the-art methods that only use a single low-rank or joint sparsity constraint. The results demonstrate that the proposed method can achieve accurate parameter estimation with both moderately and highly undersampled data. Although all methods performed fairly well with moderately undersampled data, the proposed method achieved much better performance (e.g., more accurate parameter values) than the other two methods with highly undersampled data.

Conclusions:

Simultaneously imposing low-rank and sparsity constraints can effectively improve the accuracy of fast MR parameter mapping with sparse sampling.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4344441

DOI: 10.1002/mrm.25421
PubMed: 25163720
PubMed Central: 4344441

Affiliations:

Le document en format XML

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<front><div type="abstract" xml:lang="en"><sec id="S1"><title>Purpose:</title>
<p id="P1">To enable accurate MR parameter mapping with accelerated data acquisition, utilizing recent advances in constrained imaging with sparse sampling.</p>
</sec>
<sec id="S2"><title>Theory and Methods:</title>
<p id="P2">A new constrained reconstruction method based on low-rank and sparsity constraints is proposed to accelerate MR parameter mapping. More specifically, the proposed method simultaneously imposes low-rank and joint sparse structures on contrast-weighted image sequences within a unified mathematical formulation. With a pre-estimated subspace, this formulation results in a convex optimization problem, which is solved using an efficient numerical algorithm based on the alternating direction method of multipliers.</p>
</sec>
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<p id="P3">To evaluate the performance of the proposed method, two application examples were considered: i) <italic>T</italic>
<sub>2</sub>
 mapping of the human brain, and ii) <italic>T</italic>
<sub>1</sub>
 mapping of the rat brain. For each application, the proposed method was evaluated at both moderate and high acceleration levels. Additionally, the proposed method was compared with two state-of-the-art methods that only use a single low-rank or joint sparsity constraint. The results demonstrate that the proposed method can achieve accurate parameter estimation with both moderately and highly undersampled data. Although all methods performed fairly well with moderately undersampled data, the proposed method achieved much better performance (e.g., more accurate parameter values) than the other two methods with highly undersampled data.</p>
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Accelerated MR Parameter Mapping with Low-Rank and Sparsity Constraints

Accelerated MR Parameter Mapping with Low-Rank and Sparsity Constraints

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