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Model-Based Image Processing Algorithms for CT Image Reconstruction, Artifact Reduction and Segmentation.

机译：用于CT图像重建，伪影减少和分割的基于模型的图像处理算法。

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摘要

Model-based image processing is a collection of techniques that provides a systematic framework for solving inverse problems in imaging systems. In this dissertation, three problems that arise in CT imaging systems are addressed using the model- based approach: image reconstruction for the single energy X-ray CT with both 2D parallel-beam and 3D multi-slice geometries, simultaneous image reconstruction and beam hardening correction for the single energy X-ray CT, and simultaneous metal artifact reduction and image segmentation for CT images.;In the first topic, the methodology of model-based iterative reconstruction (MBIR) for solving CT image reconstruction problems is studied. Recent research indicates that the MRIR has potential to improve image quality and remove artifacts comparing to traditional filtered back-projection (FBP) methods. The MBIR algorithms for both 2D parallel-beam and 3D multi-slice helical CT geometries are developed using the formulation under the statistical framework and the reconstruction is solved using optimization techniques. The result on the real CT baggage dataset is presented, which illustrates the image quality improvement and noise and artifact reduction.;The second topic studies the beam hardening correction problem in the single-energy X-ray CT. Beam hardening is the effect that material preferably attenuates more low-energy X-ray than high-energy, and with a broad X-ray source spectrum, assumption that distinct materials can be separated according to their densities, a more accurate forward model that accounts for the X-ray spectrum is developed and a MBIR algorithm that incorporates this new model is proposed. The overall algorithms works by alternating estimation of the image and the unknown model parameters, therefore no additional information is required. Results on both the simulated and real CT scan data show that the proposed method significantly reduces metal streak artifacts in the reconstruction.;The third problem is the segmentation of CT images with metal artifacts and without the access to the CT data. Segmenting interesting objects from CT images has a wide range of applications in medical diagnosis and security inspection. How- ever, raw CT images often contain artifacts such as streak due to the dense metal objects, and these artifacts can make accurate segmentation difficult. A novel model- based approach that jointly estimates both the segmentation and the restored image is proposed and the unified cost function consists of three terms: 1) a data fidelity term that relates the raw and restored image and incorporates a streak mask; 2) a dictionary-based image prior which regularizes the restored image; 3) a term based on the continuous-relaxed Potts model which couples the restored image intensities and segmentation labels. Results on both simulated and real CT data are presented and support that the joint segmentation and MAR can produce superior results without the use of the raw CT data.
机译：基于模型的图像处理是技术的集合，这些技术提供了解决成像系统逆问题的系统框架。本文采用基于模型的方法解决了CT成像系统中出现的三个问题：具有2D平行光束和3D多层几何结构的单能X射线CT图像重建，同时图像重建和射束硬化单能X射线CT校正，同时减少金属伪影和CT图像分割。在第一个主题中，研究了用于解决CT图像重建问题的基于模型的迭代重建（MBIR）方法。最新研究表明，与传统的滤波反投影（FBP）方法相比，MRIR具有改善图像质量和消除伪影的潜力。在统计框架下使用公式开发了用于2D平行光束和3D多层螺旋CT几何形状的MBIR算法，并使用优化技术解决了重建问题。给出了真实的CT行李数据集的结果，说明了图像质量的改善以及噪声和伪影的减少。第二个主题是研究单能X射线CT中的光束硬化校正问题。束硬化是一种效果，即材料最好比高能量衰减更多的低能量X射线，并且具有宽泛的X射线源光谱，并假设可以根据其密度分离不同的材料，这是一个更准确的正向模型，针对X射线光谱进行了开发，并提出了包含该新模型的MBIR算法。总体算法通过交替估计图像和未知模型参数来工作，因此不需要其他信息。在模拟和真实CT扫描数据上的结果均表明，该方法可显着减少重建过程中的金属条纹伪影。第三个问题是带有金属伪影且无法访问CT数据的CT图像分割。从CT图像中分割出有趣的物体在医学诊断和安全检查中具有广泛的应用。但是，原始的CT图像通常会由于密集的金属物体而包含诸如条纹之类的伪影，这些伪影会使准确的分割变得困难。提出了一种新颖的基于模型的方法，该方法可以联合估计分割图像和还原图像，并且统一成本函数由三个术语组成：1）与原始图像和还原图像相关联并包含条纹掩模的数据保真度术语； 2）基于字典的图像，在此之前对恢复的图像进行规范化； 3）基于连续松弛Potts模型的术语，该术语将恢复的图像强度和分段标签耦合在一起。给出了模拟和真实CT数据的结果，并支持关节分割和MAR可以在不使用原始CT数据的情况下产生出色的结果。

著录项

  • 作者

    Jin, Pengchong.;

  • 作者单位

    Purdue University.;

  • 授予单位 Purdue University.;
  • 学科 Electrical engineering.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 100 p.
  • 总页数 100
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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