Anatomically constrained reconstruction from noisy data
Anatomically constrained reconstruction from noisy data
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DOI：
10.1002/mrm.21536
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发表时间：
2008-04-01
影响因子：
3.3
通讯作者：
Liang, Zhi-Pei
中科院分区：
文献类型：
--
作者：
Haldar, Justin P.;Hernando, Diego;Liang, Zhi-Pei
在许多重要的成像应用中，噪声是一个主要问题。为了提高数据的信噪比，实验往往集中在采集低频k空间数据上。本文提出了一种新的方案来支持在这些环境下的扩展k空间采样。结果表明，结合解剖学先验信息的统计建模可以有效地缓解扩展采样带来的信噪比下降。该方法与大多数现有的解剖受限成像方法有很大不同，后者依赖解剖信息来实现超分辨率。该方法的优点是与超分辨率方法相比，所需的解剖信息不那么精确。给出了理论和实验结果，以表征所提方案的性能。
Noise is a major concern in many important imaging applications. To improve data signal-to-noise ratio (SNR), experiments often focus on collecting low-frequency k-space data. This article proposes a new scheme to enable extended k-space sampling in these contexts. It is shown that the degradation in SNR associated with extended sampling can be effectively mitigated by using statistical modeling in concert with anatomical prior information. The method represents a significant departure from most existing anatomically constrained imaging methods, which rely on anatomical information to achieve super-resolution. The method has the advantage that less accurate anatomical information is required relative to super-resolution approaches. Theoretical and experimental results are provided to characterize the performance of the proposed scheme.