Discriminative feature representation: an effective postprocessing solution to low dose CT imaging
Discriminative feature representation: an effective postprocessing solution to low dose CT imaging
复制标题
判别性特征表示：低剂量 CT 成像的有效后处理解决方案
DOI：
10.1088/1361-6560/aa5c24
复制
发表时间：
2017-03-21
影响因子：
3.5
通讯作者：
Luo, Limin
中科院分区：
文献类型：
--
作者：
Chen, Yang;Liu, Jin;Luo, Limin
本文提出了一种简洁有效的低剂量计算机断层扫描（LDCT）图像处理的判别特征表示（DFR）方法，该方法是目前医学成像领域的一个难题。该方法将LDCT图像假设为理想的高剂量CT （HDCT）三维特征和不理想的噪声-伪影三维特征（低剂量扫描方案引起的噪声和伪影特征的组合术语）的叠加，并利用分解后的HDCT特征为处理后的LDCT图像提供更高的质量。DFR算法使用由表示HDCT特征的原子和噪声伪影特征组成的特征字典来求解目标HDCT特征。在本研究中，使用从同一台CT扫描仪收集的物理幻象图像作为目标临床LDCT图像进行处理，有效地构建了特征字典。该方法对不同类型CT机的参数设置具有较好的鲁棒性。该方法可直接用于处理DICOM格式的LDCT图像，对现有CT系统具有较好的适用性。与腹部LDCT数据的对比实验验证了该方法的良好性能。
This paper proposes a concise and effective approach termed discriminative feature representation (DFR) for low dose computerized tomography (LDCT) image processing, which is currently a challenging problem in medical imaging field. This DFR method assumes LDCT images as the superposition of desirable high dose CT (HDCT) 3D features and undesirable noise-artifact 3D features (the combined term of noise and artifact features induced by low dose scan protocols), and the decomposed HDCT features are used to provide the processed LDCT images with higher quality. The target HDCT features are solved via the DFR algorithm using a featured dictionary composed by atoms representing HDCT features and noise-artifact features. In this study, the featured dictionary is efficiently built using physical phantom images collected from the same CT scanner as the target clinical LDCT images to process. The proposed DFR method also has good robustness in parameter setting for different CT scanner types. This DFR method can be directly applied to process DICOM formatted LDCT images, and has good applicability to current CT systems. Comparative experiments with abdomen LDCT data validate the good performance of the proposed approach.