Multi-contrast reconstruction with Bayesian compressed sensing.
Multi-contrast reconstruction with Bayesian compressed sensing.
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DOI：
10.1002/mrm.22956
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发表时间：
2011-12
影响因子：
3.3
通讯作者：
Adalsteinsson, Elfar
中科院分区：
文献类型：
--
作者：
Bilgic, Berkin;Goyal, Vivek K.;Adalsteinsson, Elfar
结构MRI的临床成像通常依赖于在几种不同的造影剂制备下对相同感兴趣区域的多次采集。这项工作提出了一种重建算法的基础上贝叶斯压缩感知联合重建一组图像从欠采样的k空间数据具有更高的保真度比单独或联合重建的图像时，由先前提出的算法，M-FOTORS。联合推理问题是在分层贝叶斯设置中制定的，其中求解每个逆问题对应于找到与每个图像相关联的参数（这里是图像梯度系数）。针对单个体积空间位置的跨对比度的图像梯度的方差是单个超参数。来自相同解剖区域但具有不同对比度属性的所有图像有助于超参数的估计，并且一旦找到它们，则独立地使用属于每个图像的k空间数据来推断图像梯度。因此，利用了跨对比度的图像空间结构的共性，而没有跨对比度的相关性的有问题的假设。实例表明，改进的重建质量（高达4倍的均方根误差）相比，以前的压缩感知算法，并显示联合反演下的分层贝叶斯模型的好处。
Clinical imaging with structural MRI routinely relies on multiple acquisitions of the same region of interest under several different contrast preparations. This work presents a reconstruction algorithm based on Bayesian compressed sensing to jointly reconstruct a set of images from undersampled k-space data with higher fidelity than when the images are reconstructed either individually or jointly by a previously proposed algorithm, M-FOCUSS. The joint inference problem is formulated in a hierarchical Bayesian setting, wherein solving each of the inverse problems corresponds to finding the parameters (here, image gradient coefficients) associated with each of the images. The variance of image gradients across contrasts for a single volumetric spatial position is a single hyperparameter. All of the images from the same anatomical region, but with different contrast properties, contribute to the estimation of the hyperparameters, and once they are found, the k-space data belonging to each image are used independently to infer the image gradients. Thus, commonality of image spatial structure across contrasts is exploited without the problematic assumption of correlation across contrasts. Examples demonstrate improved reconstruction quality (up to a factor of 4 in root-mean-square error) compared to previous compressed sensing algorithms and show the benefit of joint inversion under a hierarchical Bayesian model.
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影响因子：
4.8
作者：
Rohlfing, Torsten;Zahr, Natalie M.;Sullivan, Edith V.;Pfefferbaum, Adolf
通讯作者：
Pfefferbaum, Adolf
影响因子：
5.4
作者：
Malioutov, D;Çetin, M;Willsky, AS
通讯作者：
Willsky, AS
影响因子：
3.3
作者：
JEZZARD, P;BALABAN, RS
通讯作者：
BALABAN, RS
影响因子：
5.4
作者：
Ji, Shihao;Dunson, David;Carin, Lawrence
通讯作者：
Carin, Lawrence
DOI：
10.1137/080730822
发表时间：
2010-01-20
期刊：
SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics
影响因子：
--
作者：
Zelinski AC;Goyal VK;Adalsteinsson E
通讯作者：
Adalsteinsson E