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基于雙層字典學(xué)習(xí)的低劑量CT圖像重建算法 Low dose CT image reconstruction algorithm based on thedouble layer dictionary learning method |
| 作者： 朱雪茹 李勇明 李傳明 李志超 王健 劉燕 |
| 單位：第三軍醫(yī)大學(xué)西南醫(yī)院放射科（重慶400038）2重慶大學(xué)通信工程學(xué)院（重慶400044） |
| 關(guān)鍵詞： 低劑量投影；K-SVD算法；稀疏編碼；雙層字典學(xué)習(xí)；CT重建 |
| 分類號：R318.04 |
| 出版年·卷·期（頁碼）：2017·36·6（584-590） |
| 摘要： |
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目的 低劑量投影條件下的CT圖像重建。方法 采用雙層K-奇異值分解(K- singular value decomposition，K-SVD)字典訓(xùn)練的學(xué)習(xí)方法進(jìn)行圖像的超分辨率重建。字典學(xué)習(xí)方法中采用K-SVD算法，稀疏編碼采用正交匹配追蹤(orthogonal matching pursuit，OMP)算法。該算法首先利用訓(xùn)練庫進(jìn)行第一層字典訓(xùn)練，然后利用第一層訓(xùn)練的字典對低分辨率圖像進(jìn)行重建。進(jìn)而將重建圖像作為第二層待重建圖像的輸入，這樣使得第二層輸入圖像含有較多的高頻細(xì)節(jié)信息，因此能在重構(gòu)的過程中恢復(fù)更多的細(xì)節(jié)信息，讓高分辨率重構(gòu)圖像達(dá)到較好的效果。結(jié)果 雙層字典重建效果明顯優(yōu)于K-SVD算法，重建圖像更接近于原始高分辨率CT圖像。結(jié)論 本研究對雙層字典訓(xùn)練學(xué)習(xí)的框架進(jìn)行反迭代投影的全局優(yōu)化改進(jìn)，改善了圖像的重建質(zhì)量。 |
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Objective Reconstruction of CT image under low dose projection.Methods In this thesis，we adopt double dictionary training method based on K-singular value decomposition(K-SVD)algorithm for super resolution reconstruction of images.In the dictionary learning method，the K-SVD algorithm is adopted，and the sparse coding is orthogonal matching pursuit(OMP)algorithm.Firstly，we use training library to train the first layer dictionary，and then based on the first layer trained dictionary reconstruct low resolution images.Secondly，we put the reconstructed images as the input of the second layer images to be constructed，which make the second layer input image with more high frequency information and restore details in the process of reconstruction.Results The double-layer dictionary reconstruction is superior to the K-SVD algorithm，and the reconstructed image is closer to the original high-resolution CT image.Conclusions In this paper，the global optimization for the inverse iterative projection of the double layer dictionary training is improved，and the quality of the reconstruction image is also improved. |
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