Iterative sparse reconstruction of spectral domain OCT signal

Deconvolving optical coherence tomography (OCT) image from the system's point spread function (PSF) can enhance image quality. While conventional approach performs deconvolution after image reconstruction, a novel OCT image processing technique is proposed and validated to perform iterative sparse reconstruction that simultaneously deconvolves object from PSF.

Two researchers, Assistant Prof. Xuan Liu from Michigan Technological University and Prof. Jin U. Kang from The Johns Hopkins University, proposed a novel optical coherence tomography (OCT) image processing technique to enhance the resolution and the signal to noise ratio (SNR) of image. It is reported in Chinese Optics Letters Volume 12, No. 5, 2014 (/col/abstract.cfm?uri=col-12-5-051701).

One of the big technical thrusts in OCT is improving the OCT imaging quality with cost-effective software approaches, such as deconvolution. In previous studies involving OCT image deconvolution, images were first reconstructed with standard OCT signal processing procedure and afterwards deconvolved with the system point spread function (PSF). However, the performance of these reconstruction-deconvolution approaches is highly dependent on the noise level of the raw spectral data. Moreover, these approaches would further decrease the image SNR after deconvolution. The OCT image processing technique developed by Assistant Prof. Xuan Liu and Prof. Jin U. Kang could successfully overcome these problems.

The spectral shape of the broadband light source of OCT system has been taken into consideration in their iterative sparse reconstruction. Therefore, their method essentially deconvolves the PSF from the blurred image during image reconstruction rather than after reconstruction. Results obtained from numerical simulations and experimental OCT imaging show that their method could effectively deconvolve the axial PSF from the blurred image during reconstruction and simultaneously preserve the SNR of an OCT image.

Although iterative algorithm by nature is computationally massive, their method can be accelerated using graphic processing unit (GPU) for real time OCT imaging, which will be their future work.

新算法提高光学相干层析成像的成像质量

片说明：对OCT图像的PSF进行解卷积可以提高成像质量。传统方法都是在图像重建后再作解卷积，本研究提出的新技术能够对OCT图像同时进行重建和解卷积。

光学相干层析成像，即OCT (optical coherence tomography)，可以对光学散射介质如生物组织等进行扫描，获得的三维图像分辨率可以达到微米级。OCT技术在艺术品保存和医疗诊断设备等领域中都有广泛应用。其中，频域光学相干断层扫描（SD OCT）是目前比较先进的一种OCT技术，这种扫描方式的信噪比较高，获得信号的速度也比较快。近期，美国密歇根理工大学的助理教授Xuan Liu和约翰•霍普金斯大学的教授Jin U. Kang共同提出了一种能提高频域OCT的成像质量的新迭代算法。该研究成果发表在Chinese Optics Letters 2014年第5期上（ http://www.opticsinfobase.org/col/abstract.cfm?uri=col-12-5-051701）。

    该算法是在稀疏信号重建的迭代算法中考虑了OCT系统宽带光源的光谱形状，首次对OCT图像同时进行重建和解卷积，并通过最小化L-1范数，提高了OCT图像的清晰度和信噪比。

    研究中，他们分别用数值模拟方法和实验OCT数据检验了稀疏信号重建的迭代算法的性能，结果表明这种算法能在图像重建过程中有效地对模糊图像的轴向点分布函数（PSF）进行解卷积，抑制旁瓣，提高OCT图像的清晰度和信噪比。在后续工作中，他们将进一步通过图形处理器（GPU）来加快信号处理，克服迭代算法计算量大的缺陷，以实现实时OCT成像。