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Statistical image reconstruction from limited projection data with intensity priors

机译：从具有强度先验的有限投影数据进行统计图像重建

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摘要

The radiation dose generated from x-ray computed tomography (CT) scans and its responsibility for increasing the risk of malignancy became a major concern in the medical imaging community. Accordingly, investigating possible approaches for image reconstruction from low-dose imaging protocols, which minimize the patient radiation exposure without affecting image quality, has become an issue of interest. Statistical reconstruction (SR) methods are known to achieve a superior image quality compared with conventional analytical methods. Effective physical noise modeling and possibilities of incorporating priors in the image reconstruction problem are the main advantages of the SR methods. Nevertheless, the high computation cost limits its wide use in clinical scanners. This paper presents a framework for SR in x-ray CT when the angular sampling rate of the projection data is low. The proposed framework is based on the fact that, in many CT imaging applications, some physical and anatomical structures and the corresponding attenuation information of the scanned object can be a priori known. Therefore, the x-ray attenuation distribution in some regions of the object can be expected prior to the reconstruction. Under this assumption, the proposed method is developed by incorporating this prior information into the image reconstruction objective function to suppress streak artifacts. We limit the prior information to only a set of intensity values that represent the average intensity of the normal and expected homogeneous regions within the scanned object. This prior information can be easily computed in several x-ray CT applications. Considering the theory of compressed sensing, the objective function is formulated using the 1 norm distance between the reconstructed image and the available intensity priors. Experimental comparative studies applied to simulated data and real data are used to evaluate the proposed method. The comparison indicates a significant improvement in image quality when the proposed method is used.
机译：X射线计算机断层扫描（CT）扫描产生的辐射剂量及其对增加恶性肿瘤风险的责任已成为医学影像界的主要关注点。因此，研究从低剂量成像方案重建图像的可能方法，该方法在不影响图像质量的情况下使患者的辐射暴露最小化，已成为人们关注的问题。已知统计重建（SR）方法与常规分析方法相比可实现出色的图像质量。有效的物理噪声建模以及在图像重建问题中整合先验的可能性是SR方法的主要优点。然而，高昂的计算成本限制了其在临床扫描仪中的广泛使用。当投影数据的角度采样率较低时，本文提出了X射线CT的SR框架。所提出的框架基于以下事实：在许多CT成像应用中，某些物理和解剖结构以及被扫描对象的相应衰减信息可能是先验的。因此，在重建之前可以预期在对象的某些区域中的x射线衰减分布。在这种假设下，通过将该先验信息合并到图像重建目标函数中来抑制条纹伪影，从而开发了所提出的方法。我们将先验信息限制为仅代表一组强度值，这些值代表扫描对象内正常区域和预期均匀区域的平均强度。可以在几种X射线CT应用程序中轻松计算该先验信息。考虑到压缩感测的理论，使用重建图像与可用强度先验之间的1范数距离来制定目标函数。应用于模拟数据和真实数据的实验比较研究用于评估该方法。当使用所提出的方法时，比较表明图像质量有了显着改善。

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