Abstract：
BACKGROUND: Dual-energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings.
OBJECTIVE: This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT.
METHODS: The proposed framework combines iterative decomposition and deep learning-based image prior in a generative adversarial network (GAN) architecture. In the generator module, a data-fidelity loss is introduced to enforce the measurement consistency in material decomposition. In the discriminator module, the discriminator is trained to differentiate the low-noise material-specific images from the high-noise images. In this scheme, paired images of DECT and ground-truth material-specific images are not required for the model training. Once trained, the generator can perform image-domain material decomposition with noise suppression in a single step.
RESULTS: In the simulation studies of head and lung digital phantoms, the proposed method reduced the standard deviation (SD) in decomposed images by 97% and 91% from the values in direct inversion results. It also generated decomposed images with structural similarity index measures (SSIMs) greater than 0.95 against the ground truth. In the clinical head and lung patient studies, the proposed method suppressed the SD by 95% and 93% compared to the decomposed images of matrix inversion.
CONCLUSIONS: Since the invention of DECT, noise amplification during material decomposition has been one of the biggest challenges, impeding its quantitative use in clinical practice. The proposed method performs accurate material decomposition with efficient noise suppression. Furthermore, the proposed method is within an unsupervised-learning framework, which does not require paired data for model training and resolves the issue of lack of ground-truth data in clinical scenarios.
OBJECTIVE: This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT.
METHODS: The proposed framework combines iterative decomposition and deep learning-based image prior in a generative adversarial network (GAN) architecture. In the generator module, a data-fidelity loss is introduced to enforce the measurement consistency in material decomposition. In the discriminator module, the discriminator is trained to differentiate the low-noise material-specific images from the high-noise images. In this scheme, paired images of DECT and ground-truth material-specific images are not required for the model training. Once trained, the generator can perform image-domain material decomposition with noise suppression in a single step.
RESULTS: In the simulation studies of head and lung digital phantoms, the proposed method reduced the standard deviation (SD) in decomposed images by 97% and 91% from the values in direct inversion results. It also generated decomposed images with structural similarity index measures (SSIMs) greater than 0.95 against the ground truth. In the clinical head and lung patient studies, the proposed method suppressed the SD by 95% and 93% compared to the decomposed images of matrix inversion.
CONCLUSIONS: Since the invention of DECT, noise amplification during material decomposition has been one of the biggest challenges, impeding its quantitative use in clinical practice. The proposed method performs accurate material decomposition with efficient noise suppression. Furthermore, the proposed method is within an unsupervised-learning framework, which does not require paired data for model training and resolves the issue of lack of ground-truth data in clinical scenarios.
摘要：
背景：双能量计算机断层扫描（DECT）和材料分解在定量医学成像中起着至关重要的作用。然而,分解过程可能会受到显著的噪声放大，导致严重降低的图像信噪比（SNR）。虽然现有的迭代算法使用不同的图像先验来执行噪声抑制，这些启发式图像先验无法准确表示目标图像流形的特征。尽管已经报道了基于深度学习的分解方法，这些方法在监督学习框架中，需要配对数据进行训练，这在临床环境中不容易获得。
目的：这项工作旨在开发一种具有数据测量一致性的无监督学习框架，用于DECT中的图像域材料分解。
方法：所提出的框架在生成对抗网络（GAN）架构中结合了迭代分解和基于深度学习的图像先验。在发电机模块中，引入了数据保真度损失，以加强材料分解中的测量一致性。在鉴别器模块中，所述鉴别器被训练以将低噪声材料特定图像与高噪声图像区分开。在这个方案中,模型训练不需要DECT和地面实况材料特定图像的成对图像。一旦受过训练，生成器可以在单个步骤中执行具有噪声抑制的图像域材料分解。
结果：在头和肺数字体模的模拟研究中，与直接反演结果相比，所提出的方法将分解图像中的标准偏差（SD）降低了97％和91％。它还生成了结构相似性指数度量（SSIM）大于0.95的分解图像。在临床头部和肺部患者研究中，与矩阵求逆的分解图像相比，该方法将SD抑制了95％和93％。
结论：自DECT发明以来，材料分解过程中的噪声放大一直是最大的挑战之一，阻碍其在临床实践中的定量使用。所提出的方法通过有效的噪声抑制来执行精确的材料分解。此外，所提出的方法是在一个无监督学习框架内，这不需要配对数据进行模型训练，并解决了临床场景中缺乏地面实况数据的问题。
目的：这项工作旨在开发一种具有数据测量一致性的无监督学习框架，用于DECT中的图像域材料分解。
方法：所提出的框架在生成对抗网络（GAN）架构中结合了迭代分解和基于深度学习的图像先验。在发电机模块中，引入了数据保真度损失，以加强材料分解中的测量一致性。在鉴别器模块中，所述鉴别器被训练以将低噪声材料特定图像与高噪声图像区分开。在这个方案中,模型训练不需要DECT和地面实况材料特定图像的成对图像。一旦受过训练，生成器可以在单个步骤中执行具有噪声抑制的图像域材料分解。
结果：在头和肺数字体模的模拟研究中，与直接反演结果相比，所提出的方法将分解图像中的标准偏差（SD）降低了97％和91％。它还生成了结构相似性指数度量（SSIM）大于0.95的分解图像。在临床头部和肺部患者研究中，与矩阵求逆的分解图像相比，该方法将SD抑制了95％和93％。
结论：自DECT发明以来，材料分解过程中的噪声放大一直是最大的挑战之一，阻碍其在临床实践中的定量使用。所提出的方法通过有效的噪声抑制来执行精确的材料分解。此外，所提出的方法是在一个无监督学习框架内，这不需要配对数据进行模型训练，并解决了临床场景中缺乏地面实况数据的问题。
