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Highly accelerated parameter mapping using model-based alternating reconstruction coupling fitting
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AI
DOI：10.1088/1361-6560/ad5bb8.png)
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摘要
En 中文
Objective.A model-based alternating reconstruction coupling fitting, termed Model-based Alternating Reconstruction COupling fitting (MARCO), is proposed for accurate and fast magnetic resonance parameter mapping.Approach.MARCO utilizes the signal model as a regularization by minimizing the bias between the image series and the signal produced by the suitable signal model based on iteratively updated parameter maps when reconstructing. The technique can incorporate prior knowledge of both image series and parameters by adding sparsity constraints. The optimization problem is decomposed into three subproblems and solved through three alternating steps involving reconstruction and nonlinear least-square fitting, which can produce both contrast-weighted images and parameter maps simultaneously.Main results.The algorithm is applied toT(2)mapping with extended phase graph algorithm integrated and validated on undersampled multi-echo spin-echo data from both phantom and in vivo sources. Compared with traditional compressed sensing and model-based methods, the proposed approach yields more accurateT(2)maps with more details at high acceleration factors.Significance.The proposed method provides a basic framework for quantitative MR relaxometry, theoretically applicable to all quantitative MR relaxometry. It has the potential to improve the diagnostic utility of quantitative imaging techniques.
Keyword:
model-based reconstruction
quantitative MRI
sparsity modeling
T-2 mapping
期刊
IF:
3.4
论文数:
1.4W
被引数:
3.1W
机构
引用论文
Accelerating MR Parameter Mapping Using Sparsity-Promoting Regularization in Parametric Dimension
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