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类型 综合研究 预答辩日期 2018-03-08
开始(开题)日期 2016-12-02 论文结束日期 2017-12-02
地点 东南大学四牌楼校区群贤楼二楼会议室 论文选题来源 非立项    论文字数 7.8 (万字)
题目 特征稀疏表示的低剂量CT成像方法研究
主题词 低剂量CT,特征字典,稀疏表示,区别性特征表示,噪声伪影
摘要 作为一种临床成像技术,X射线计算机断层成像 (Computed Tomography, CT) 以其空间分辨率高、扫描时间短及病人成本低的优势,在疾病筛选、诊断、急救、介入治疗及疗效监督中广泛使用。然而过量X射线照射可诱发癌症、白血病或增加其它生理性风险,因此CT中的辐射问题也越来越受到人们重视。目前,比较直接且有效的降低辐射剂量的方法是降低X射线管电流,但上述扫描模式下投影数据会受到严重量子噪声污染,解析重建的图像质量急剧退化,图像中的斑点噪声和条状伪影影响了临床分析和诊断。如何提高低剂量CT (Low Dose CT, LDCT) 扫描下的成像质量一直是CT研究领域的难点问题,研究适合临床应用的LDCT成像技术具有重要的科学研究价值和临床使用价值。 综上,针对低X射线管电流扫描的成像质量下降问题,本论文通过结合适当的特征稀疏表示方法,围绕图像重建、图像域后处理及投影域降噪三个方面进行了深入研究,来实现LDCT优质成像。具体内容包括以下几个方面: (1) 三维特征约束的LDCT重建算法。 近几年,特征字典学习和稀疏表示方法在信号/图像处理领域中取得了较大的进展。为此,论文将高维特征字典与稀疏表示的优势引入到LDCT重建中,提出了三维特征约束重建算法。文中首先通过对高质量的临床CT图像样本进行特征选取和学习,来构造特征字典;然后将含丰富三维解剖组织特征原子的特征字典引入到约束项中,对由噪声投影数据引起的病态重建问题进行特征约束,使图像更新向有利方向进行;最后,给出了算法的交替迭代求解公式。基于仿真数据和临床数据的实验结果验证了算法的有效性,与经典重建算法相比,本文所提算法能够更好的保持图像的结构细节和去除条状伪影。 (2) 区别性特征表示的LDCT图像后处理算法。 特征字典学习方法已被证实能够很好的表示CT图像特征。然而,当噪声和条状伪影过强时,其伪影结构仍然能够被有效表示出来,无法完全去除。为克服该问题,本文进一步拓展和提高特征字典在CT图像中的表示能力,创新性地提出了区别性特征表示的方法。该方法从形态学结构差异的角度出发,认为LDCT图像等于有效的解剖组织成分与低剂量扫描导致的无效噪声伪影成分之和,分解出的解剖组织成分可以作为处理后的高质量CT图像。通过构建含不同特征信息的过完备区别性字典,其中同时包含丰富的解剖特征原子和噪声伪影特征原子,来区别性表示LDCT图像,进而分解出其中有效成分。腹部LDCT对比实验结果表明,该方法能够获得更好的处理效果,具有极大的临床应用前景。 (3) 区别性先验及先验图像约束的LDCT重建算法。 在区别性特征表示模型的基础上,结合先验图像压缩感知重建,提出了区别性先验及先验图像约束的LDCT重建算法。为解决先验图像和当前待重建图像之间的结构不匹配问题,本文首先将区别性特征表示方法处理后的解析重建图像作为先验图像;进一步,分别采用解剖结构特征图像块样本和噪声伪影特征图像块样本构建的特征字典,对重建图像解剖组织成分和噪声伪影残差成分进行区别性约束,来设计重建模型。该重建模型克服了经典PICCS模型对先验图像的依赖以及不同来源的先验图像与待重建图像的匹配问题,并采用区别性先验约束,来提高重建图像质量。实验结果表明该方法能有效的抑制噪声伪影和保持解剖组织细节。 (4) 基于提高投影数据一致性的LDCT成像算法。 LDCT图像重建中,由于数据的不一致性,直接使用扫描后的噪声投影数据将必然导致图像严重退化。数据保真项要求图像重投影要与投影数据一致,而采集到的噪声投影数据通常与期望图像的重投影不一致,这是LDCT重建中的本质问题。针对这一问题,本文提出了一种新的弦图复原算法,弦图区别性特征表示。通过对投影数据进行一致性处理,来提高最终的成像质量。该方法是通过预先构建的含有区别性特征的组合字典,对LDCT投影数据进行三维特征分解表示,分离出投影衰减成分和噪声成分,并将分解后的投影进行一致性条件约束处理以进一步提高数据的可靠性。基于仿真、临床及小鼠的投影数据对比实验表明,该算法为提高LDCT成像质量提供了一种新的途径。
英文题目 FEATURE SPARSE REPRESENTATION BASED LOW DOSE CT IMAGING
英文主题词 Low dose computerized tomography, Feature dictionary, Sparse representation, Discriminative feature representation, Noise-artifact
英文摘要 X-ray computed tomography (CT) is now a widely used clinical imaging modality for screening and diagnosis, emergency medicine, image-guided interventions, and monitoring of therapeutic responses with its high spatial resolution, short scanning time and low patient cost. But the excessive X-ray radiation may induce cancer, leukemia, or increase biological risk, so radiation doses associated with CT have received serious concern. Among all the methods proposed so far to obtain low radiation doses, the most directly and practical used method is to lower the X-ray tube current, however reduce tube currents make the projection data corrupted by quantum noise. Meanwhile, the associated reconstructed image usually suffers from serious noise and streak artifacts, which has negative influence for clinical diagnosis and analysis. Therefore, how to improve the quality of reconstruction image under the lower radiation dose CT scan has been the difficult problem in CT research field, and has the important scientific research value and the clinical use value. As above, to overcome quality degradation of CT image caused by low X-ray tube current scanner protocols, this dissertation focuses on incorporating suitable feature sparse representation into the statistical iterative reconstruction, CT image processing and projection data restoration frameworks for better performance of LDCT imaging. The main innovative works are summarized as follows: (1) 3D feature constrained reconstruction for LDCT imaging. A great deal of progress has been made for the study of signal/image processing based on feature dictionary learning (DL) and sparse representation in recent years. In this dissertation, inspired by the idea of high-dimensional feature dictionary and sparse representation techniques, we introduced them into the statistical iterative reconstruction scheme and constructed a 3D feature constrained reconstruction (3D-FCR) algorithm for LDCT. First, the available high quality standard-dose CT (SDCT) images which can serve as sample data so as to constructed 3D tissue feature dictionary by feature extraction and training. Then, rich 3D anatomy tissue features in the forms of dictionary atoms were incorporated as constraints to overcome the ill-posedness of reconstruction with noisy measurements in CT, which is of great benefit to image update. At last, an alternating iterative estimation scheme was given. Experiments on both phantom and clinical data were conducted to validate the performance of this method. Compared with the classic reconstruction methods, the proposed method has the excellent performance in protection of the tissue detail and removal of streak artifact. (2) Discriminative feature representation for LDCT image processing. Successful application of DL method has been reported in LDCT image processing. It is often the case that strong noise and streak artifacts (with strong intensities) can also be sparsely representable and as a result cannot be removed effectively. To overcome this problem, we further explored the improvement of CT image feature representation ability and proposed a novel method called the discriminative feature representation (DFR). This DFR method assumes LDCT images as the superposition of desirable anatomical component and undesirable noise-artifact component induced by low dose scan protocols, and the decomposed anatomical component was used to provide the processed LDCT images with higher quality. The significant morphological distinction between anatomical structure and noise-artifact structure is leveraged in algorithm devising. The target anatomical component was solved via the DFR algorithm using a featured dictionary composed by atoms representing anatomical features and noise-artifact features. Comparative experiments with abdomen LDCT data validated the good performance of the proposed approach, and it will have good clinical application prospects. (3) Discriminative prior - prior image constrained compressed sensing reconstruction for LDCT imaging. Based on discriminative feature representation and prior image constrained compressed sensing reconstruction model, a joint estimation framework termed discriminative prior - prior image constrained compressed sensing (DP-PICCS) reconstruction was proposed. To address the mismatch problem between the prior image and target image for the DP-PICCS algorithm, this article applied DFR method to obtain high quality image volume by processing the FDK reconstruction volume. Additional, the DP-PICCS model utilizes discriminative prior knowledge via two feature dictionary discriminative constraints which built on atoms from the samples of anatomical structure feature patches and noise-artifacts residual feature patches, respectively. The new model overcomes the mismatch problem in current PICCS reconstruction, and adopt discriminative prior constraints to improve reconstruction image quality. Experimental results shown that the DP-PICCS method can lead to a promising improvement in terms of the suppressing noise-artifacts and preserving details structures. (4) Discriminative feature representation to improve projection data inconsistency for LDCT imaging. Directly using measured noisy projections to reconstruction can significantly deteriorate images due to the data inconsistency under LDCT scan protocols. The data consistency requirement imposed by fidelity term is intrinsically questionable due to the fact that the desirable reconstruction image reprojection always does not always match with the measured noisy projections in LDCT imaging. To deal with this problem, we proposed here a new sinogram restoration approach, the sinogram - discriminative feature representation (S-DFR) method, and the final improved LDCT images were reconstructed from restored projection data. First, the new approach works through a 3D representation based feature decomposition of the projected attenuation component and the noise component using a well-designed composite dictionary containing atoms with discriminative features. Then, the data consistency conditions (DCCs) were imposed on the decomposed projections to futher ensure data consistency. Its comparison to other competing methods through experiments on simulated, clinical and rat projection data demonstrated that the S-DFR method offers a sound alternative in LDCT imaging.
学术讨论
主办单位时间地点报告人报告主题
计算机科学与工程学院 2017.10.30 中山院 112 陈浩 3D Deep Learning and Its Application to Volumetric Image Processing
计算机科学与工程学院 2017.10.30 中山院 112 徐军 计算机的病理学应用
生物医学工程学院 2017.04.13 生物电子学实验室三楼会议室 姜明 Recent advances in accelerating x-ray tomography reconstruction with Mumford-Shah regularization
计算机科学与工程学院 2016.11.29 礼东二楼会议室 Jens Frahm Real-time MRI—A New Horizon
影像实验室 2017.05.09 群贤楼二楼会议室 刘进 基于残差卷积神经网络的低剂量CT成像处理
影像实验室 2016.11.27 群贤楼二楼会议室 刘进 低剂量CT成像中的数据不一致问题及解决办法
影像实验室 2016.06.13 群贤楼二楼会议室 刘进 基于区别性特征分解的低剂量CT成像算法
影像实验室 2015.03.20 群贤楼二楼会议室 刘进 CT图像重建原理及算法基础
     
学术会议
会议名称时间地点本人报告本人报告题目
2016 IEEE 13th International Symposium on Biomedical Imaging 2016,04.17 捷克布拉格 Low-Dose CBCT Reconstruction via 3D Dictionary Learning
2017 Fully Three-Dimensional Image Reconstruction in Radiology 2017.06.18 中国西安 Photon-counting spectral computed tomography study
     
代表作
论文名称
Low-Dose CBCT Reconstruction via 3D Dictionary Learning
Discriminative Feature Representation to Improve Projection Data Inconsistency for Low Dose CT Imagi
 
答辩委员会组成信息
姓名职称导师类别工作单位是否主席备注
夏灵 正高 教授 博导 浙江大学
曹汛 正高 教授 博导 南京大学
舒华忠 正高 教授 博导 东南大学
鲍旭东 正高 教授 硕导 东南大学
杨冠羽 副高 副教授 博导 东南大学
      
答辩秘书信息
姓名职称工作单位备注
於文雪 副高 副教授 东南大学