稀疏光学相干断层扫描：优化光谱域光学相干断层扫描中的压缩感知
Sparse OCT: Optimizing compressed sensing in spectral domain optical coherence tomography.
作者信息
Liu Xuan, Kang Jin U
机构信息
Department of Electrical and Computer Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218 USA.
出版信息
Proc SPIE Int Soc Opt Eng. 2011;7904. doi: 10.1117/12.874058. Epub 2011 Feb 10.
Abstract
We applied compressed sensing (CS) to spectral domain optical coherence tomography (SD-OCT). Namely, CS was applied to the spectral data in reconstructing A-mode images. This would eliminate the need for a large amount of spectral data for image reconstruction and processing. We tested the CS method by randomly undersampling k-space SD-OCT signal. OCT images are reconstructed by solving an optimization problem that minimizes the l1 norm to enforce sparsity, subject to data consistency constraints. Variable density random sampling and uniform density random sampling were studied and compared, which shows the former undersampling scheme can achieve accurate signal recovery using less data.
摘要
我们将压缩感知（CS）应用于光谱域光学相干断层扫描（SD-OCT）。具体而言，在重建A模式图像时，将CS应用于光谱数据。这将消除图像重建和处理中对大量光谱数据的需求。我们通过对k空间SD-OCT信号进行随机欠采样来测试CS方法。通过解决一个优化问题来重建OCT图像，该优化问题在数据一致性约束下最小化l1范数以增强稀疏性。研究并比较了可变密度随机采样和均匀密度随机采样，结果表明前一种欠采样方案可以使用更少的数据实现准确的信号恢复。
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