迭代重建
动态增强MRI
计算机科学
背景(考古学)
成像体模
反问题
投影(关系代数)
主成分分析
人工智能
图像分辨率
计算机视觉
动态成像
稀疏逼近
算法
数学
模式识别(心理学)
磁共振成像
图像(数学)
图像处理
物理
光学
放射科
数学分析
数字图像处理
古生物学
生物
医学
作者
Benjamin Tremoulheac,Νικόλαος Δικαίος,David Atkinson,Simon Arridge
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2014-04-30
卷期号:33 (8): 1689-1701
被引量:123
标识
DOI:10.1109/tmi.2014.2321190
摘要
Dynamic magnetic resonance imaging (MRI) is used in multiple clinical applications, but can still benefit from higher spatial or temporal resolution. A dynamic MR image reconstruction method from partial (k, t)-space measurements is introduced that recovers and inherently separates the information in the dynamic scene. The reconstruction model is based on a low-rank plus sparse decomposition prior, which is related to robust principal component analysis. An algorithm is proposed to solve the convex optimization problem based on an alternating direction method of multipliers. The method is validated with numerical phantom simulations and cardiac MRI data against state of the art dynamic MRI reconstruction methods. Results suggest that using the proposed approach as a means of regularizing the inverse problem remains competitive with state of the art reconstruction techniques. Additionally, the decomposition induced by the reconstruction is shown to help in the context of motion estimation in dynamic contrast enhanced MRI.
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