Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels, i.e., the tissue heterogeneity, and has been recognized as important biomarkers in various clinical tasks. Spectral computed tomography (CT) is believed to be able to enrich tissue texture by providing different voxel contrast images using different X-ray energies. Therefore, this paper aims to address two related issues for clinical usage of spectral CT, especially the photon counting CT (PCCT): (1) texture enhancement by spectral CT image reconstruction, and (2) spectral energy enriched tissue texture for improved lesion classification. For issue (1), we recently proposed a tissue-specific texture prior in addition to low rank prior for the individual energy-channel low-count image reconstruction problems in PCCT under the Bayesian theory. Reconstruction results showed the proposed method outperforms existing methods of total variation (TV), low-rank TV and tensor dictionary learning in terms of not only preserving texture features but also suppressing image noise. For issue (2), this paper will investigate three models to incorporate the enriched texture by PCCT in accordance with three types of inputs: one is the spectral images, another is the co-occurrence matrices (CMs) extracted from the spectral images, and the third one is the Haralick features (HF) extracted from the CMs. Studies were performed on simulated photon counting data by introducing attenuation-energy response curve to the traditional CT images from energy integration detectors. Classification results showed the spectral CT enriched texture model can improve the area under the receiver operating characteristic curve (AUC) score by 7.3%, 0.42% and 3.0% for the spectral images, CMs and HFs respectively on the five-energy spectral data over the original single energy data only. The CM- and HF-inputs can achieve the best AUC of 0.934 and 0.927. This texture themed study shows the insight that incorporating clinical important prior information, e.g., tissue texture in this paper, into the medical imaging, such as the upstream image reconstruction, the downstream diagnosis, and so on, can benefit the clinical tasks.

中文翻译：

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用于病变分类的光谱计算机断层扫描中的能量增强组织纹理

组织纹理反映了图像体素灰度对比度的空间分布，即组织异质性，已被公认为各种临床任务中的重要生物标志物。光谱计算机断层扫描 (CT) 被认为能够通过使用不同的 X 射线能量提供不同的体素对比度图像来丰富组织纹理。因此，本文旨在解决光谱 CT 临床使用的两个相关问题，尤其是光子计数 CT (PCCT)：(1) 光谱 CT 图像重建的纹理增强，和 (2) 光谱能量丰富的组织纹理，用于改进病变分类. 对于问题 (1)，我们最近在贝叶斯理论下针对 PCCT 中的单个能量通道低计数图像重建问题提出了一种组织特定纹理先验和低秩先验。重建结果表明，所提出的方法在保留纹理特征和抑制图像噪声方面优于现有的总变异（TV）、低秩TV和张量字典学习方法。对于问题（2），本文将根据三种类型的输入研究通过 PCCT 合并丰富纹理的三种模型：一种是光谱图像，另一种是从光谱图像中提取的共生矩阵（CM），以及第三个是从 CM 中提取的 Haralick 特征（HF）。通过将衰减-能量响应曲线引入能量积分探测器的传统 CT 图像，对模拟光子计数数据进行了研究。分类结果表明，光谱 CT 富集纹理模型可以将光谱图像、CMs 和 HFs 的接收器操作特征曲线 (AUC) 得分下面积分别提高 7.3%、0.42% 和 3.0%。仅原始单一能源数据。CM 和 HF 输入可以达到 0.934 和 0.927 的最佳 AUC。这项以纹理为主题的研究表明，将临床重要的先验信息（例如本文中的组织纹理）纳入医学成像（例如上游图像重建、下游诊断等）可以有益于临床任务。CM 和 HF 输入可以达到 0.934 和 0.927 的最佳 AUC。这项以纹理为主题的研究表明，将临床重要的先验信息（例如本文中的组织纹理）纳入医学成像（例如上游图像重建、下游诊断等）可以有益于临床任务。CM 和 HF 输入可以达到 0.934 和 0.927 的最佳 AUC。这项以纹理为主题的研究表明，将临床重要的先验信息（例如本文中的组织纹理）纳入医学成像（例如上游图像重建、下游诊断等）可以有益于临床任务。