IEEE Trans Med Imaging. 2020 Oct;39(10):2996-3007. doi: 10.1109/TMI.2020.2983414. Epub 2020 Mar 26.
Photon-counting spectral computed tomography (CT) is capable of material characterization and can improve diagnostic performance over traditional clinical CT. However, it suffers from photon count starving for each individual energy channel which may cause severe artifacts in the reconstructed images. Furthermore, since the images in different energy channels describe the same object, there are high correlations among different channels. To make full use of the inter-channel correlations and minimize the count starving effect while maintaining clinically meaningful texture information, this paper combines a region-specific texture model with a low-rank correlation descriptor as an a priori regularization to explore a superior texture preserving Bayesian reconstruction of spectral CT. Specifically, the inter-channel correlations are characterized by the low-rank representation, and the inner-channel regional textures are modeled by a texture preserving Markov random field. In other words, this paper integrates the spectral and spatial information into a unified Bayesian reconstruction framework. The widely-used Split-Bregman algorithm is employed to minimize the objective function because of the non-differentiable property of the low-rank representation. To evaluate the tissue texture preserving performance of the proposed method for each channel, three references are built for comparison: one is the traditional CT image from energy integration detection. The second one is spectral images from dual-energy CT. The third one is individual channels images from custom-made photon-counting spectral CT. As expected, the proposed method produced promising results in terms of not only preserving texture features but also suppressing image noise in each channel, comparing to existing methods of total variation (TV), low-rank TV and tensor dictionary learning, by both visual inspection and quantitative indexes of root mean square error, peak signal to noise ratio, structural similarity and feature similarity.
光子计数能谱 CT 能够对物质进行特征分析，并能提高传统临床 CT 的诊断性能。然而，它受到每个单独能量通道的光子计数饥饿的影响，这可能会导致重建图像中的严重伪影。此外，由于不同能量通道中的图像描述的是同一个物体，因此不同通道之间存在高度相关性。为了充分利用通道间的相关性，并在保持临床有意义的纹理信息的同时最小化计数饥饿效应，本文将具有特定区域纹理的模型与低秩相关描述符相结合，作为先验正则化项，以探索光谱 CT 的优越的纹理保持贝叶斯重建。具体来说，通道间的相关性由低秩表示来描述，而通道内的区域纹理由纹理保持马尔可夫随机场来建模。换句话说，本文将光谱和空间信息集成到一个统一的贝叶斯重建框架中。由于低秩表示的不可微特性，广泛使用的分裂布格曼算法被用来最小化目标函数。为了评估所提出的方法在每个通道上对组织纹理保持性能，构建了三个参考：一个是能量积分检测的传统 CT 图像。第二个是双能 CT 的光谱图像。第三个是定制的光子计数能谱 CT 的单个通道图像。正如预期的那样，与现有的全变分（TV）、低秩 TV 和张量字典学习方法相比，所提出的方法在保持纹理特征和抑制每个通道的图像噪声方面都产生了有希望的结果，不仅通过视觉检查，而且通过均方根误差、峰值信噪比、结构相似性和特征相似性等定量指标进行评估。
