Liu Jin, Wu Fan, Zhan Guorui, Wang Kun, Zhang Yikun, Hu Dianlin, Chen Yang
College of Computer and Information, Anhui Polytechnic University, Wuhu, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China.
College of Computer and Information, Anhui Polytechnic University, Wuhu, China.
Comput Methods Programs Biomed. 2025 Apr;261:108597. doi: 10.1016/j.cmpb.2025.108597. Epub 2025 Jan 9.
Dual-energy computed tomography (DECT) enables the differentiation of different materials. Additionally, DECT images consist of multiple scans of the same sample, revealing information similarity within the energy domain. To leverage this information similarity and address safety concerns related to excessive radiation exposure in DECT imaging, sparse view DECT imaging is proposed as a solution. However, this imaging method can impact image quality. Therefore, this paper presents a hybrid spectrum data generative diffusion reconstruction model (HSGDM) to improve imaging quality.
To exploit the spectral similarity of DECT, we use interleaved angles for sparse scanning to obtain low- and high-energy CT images with complementary incomplete views. Furthermore, we organize low- and high-energy CT image views into multichannel forms for training and inference and promote information exchange between low-energy features and high-energy features, thus improving the reconstruction quality while reducing the radiation dose. In the HSGDM, we build two types of diffusion model constraint terms trained by the image space and wavelet space. The wavelet space diffusion model exploits mainly the orientation and scale features of artifacts. By integrating the image space diffusion model, we establish a hybrid constraint for the iterative reconstruction framework. Ultimately, we transform the iterative approach into a cohesive sampling process guided by the measurement data, which collaboratively produces high-quality and consistent reconstructions of sparse view DECT.
Compared with the comparison methods, this approach is competitive in terms of the precision of the CT values, the preservation of details, and the elimination of artifacts. In the reconstruction of 30 sparse views, with increases of 3.51 dB for the peak signal-to-noise ratio (PSNR), 0.03 for the structural similarity index measure (SSIM), and a reduction of 74.47 for the Fréchet inception distance (FID) score on the test dataset. In the ablation study, we determined the effectiveness of our proposed hybrid prior, consisting of the wavelet prior module and the image prior module, by comparing the visual effects and quantitative results of the methods using an image space model, a wavelet space model, and our hybrid model approach. Both qualitative and quantitative analyses of the results indicate that the proposed method performs well in sparse DECT reconstruction tasks.
We have developed a unified optimized mathematical model that integrates the image space and wavelet space prior knowledge into an iterative model. This model is more practical and interpretable than existing approaches are. The experimental results demonstrate the competitive performance of the proposed model.
双能计算机断层扫描（DECT）能够区分不同材料。此外，DECT图像由对同一样本的多次扫描组成，揭示了能量域内的信息相似性。为了利用这种信息相似性并解决DECT成像中与过度辐射暴露相关的安全问题，提出了稀疏视图DECT成像作为解决方案。然而，这种成像方法会影响图像质量。因此，本文提出了一种混合光谱数据生成扩散重建模型（HSGDM）来提高成像质量。
为了利用DECT的光谱相似性，我们使用交错角度进行稀疏扫描，以获得具有互补不完整视图的低能和高能CT图像。此外，我们将低能和高能CT图像视图组织成多通道形式进行训练和推理，并促进低能特征和高能特征之间的信息交换，从而在降低辐射剂量的同时提高重建质量。在HSGDM中，我们构建了两种由图像空间和小波空间训练的扩散模型约束项。小波空间扩散模型主要利用伪影的方向和尺度特征。通过整合图像空间扩散模型，我们为迭代重建框架建立了混合约束。最终，我们将迭代方法转化为由测量数据引导的凝聚采样过程，协同产生稀疏视图DECT的高质量和一致重建。
与比较方法相比，该方法在CT值精度、细节保留和伪影消除方面具有竞争力。在30个稀疏视图的重建中，测试数据集上的峰值信噪比（PSNR）提高了3.51 dB，结构相似性指数测量（SSIM）提高了0.03，弗雷歇初始距离（FID）得分降低了74.47。在消融研究中，我们通过比较使用图像空间模型、小波空间模型和我们的混合模型方法的视觉效果和定量结果，确定了我们提出的由小波先验模块和图像先验模块组成的混合先验的有效性。结果的定性和定量分析均表明，该方法在稀疏DECT重建任务中表现良好。
我们开发了一个统一的优化数学模型，将图像空间和小波空间的先验知识集成到一个迭代模型中。该模型比现有方法更实用且更具可解释性。实验结果证明了所提模型的竞争力。
