Zhang Wenkun, Liang Ningning, Wang Zhe, Cai Ailong, Wang Linyuan, Tang Chao, Zheng Zhizhong, Li Lei, Yan Bin, Hu Guoen
Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China.
Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.
Quant Imaging Med Surg. 2020 Oct;10(10):1940-1960. doi: 10.21037/qims-20-594.
Multi-energy computed tomography (MECT) based on a photon-counting detector is an emerging imaging modality that collects projections at several energy bins with a single scan. However, the limited number of photons collected into the divided, narrow energy bins results in high quantum noise levels in reconstructed images. This study aims to improve MECT image quality by minimizing noise levels while retaining image details.
A novel MECT reconstruction method was proposed by exploiting the nonlocal tensor similarity among interchannel images and spatial sparsity in single-channel images. Similar patches were initially extracted from the interchannel images in spectral and spatial domains, then stacked into a new three-order tensor. Intrinsic tensor sparsity regularization that combined the Tuker and canonical polyadic (CP) low-rank decomposition techniques were applied to exploit the nonlocal similarity of the formulated tensor. Spatial sparsity in single-channel images was modeled by total variation (TV) regularization that utilizes the compressibility of gradient image. A new MECT reconstruction model was established by simultaneously incorporating the intrinsic tensor sparsity and TV regularizations. The iterative alternating minimization method was utilized to solve the reconstruction model based on a flexible framework.
The proposed method was applied to the digital phantom and real mouse data to assess its feasibility and reliability. The reconstruction and decomposition results in the mouse data were encouraging and demonstrated the ability of the proposed method in noise suppression while preserving image details, not observed with other methods. Imaging data from the digital phantom illustrated this method as achieving the best intuitive reconstruction and decomposition results among all compared methods. They reduced the root mean square error (RMSE) by 89.75%, 50.75%, and 36.54% on the reconstructed images compared with analytic, TV-based, and tensor-based methods, respectively. This phenomenon was also observed with decomposition results, where the RMSE was also reduced by 97.96%, 67.74%, 72.05%, respectively.
In this study, we proposed a reconstruction method for photon counting detector-based MECT, using the intrinsic tensor sparsity and TV regularizations. Improvements in noise suppression and detail preservation in the digital phantom and real mouse data were validated by the qualitative and quantitative evaluations on the reconstruction and decomposition results, verifying the potential of the proposed method in MECT reconstruction.
基于光子计数探测器的多能量计算机断层扫描（MECT）是一种新兴的成像方式，它能在单次扫描中收集多个能量区间的投影数据。然而，收集到划分狭窄能量区间内的光子数量有限，导致重建图像中的量子噪声水平较高。本研究旨在通过最小化噪声水平同时保留图像细节来提高MECT图像质量。
通过利用通道间图像的非局部张量相似性和单通道图像中的空间稀疏性，提出了一种新颖的MECT重建方法。首先在光谱和空间域从通道间图像中提取相似块，然后堆叠成一个新的三阶张量。结合塔克（Tuker）和典范多向（CP）低秩分解技术的内在张量稀疏正则化被应用于利用所构建张量的非局部相似性。单通道图像中的空间稀疏性通过利用梯度图像可压缩性的全变分（TV）正则化来建模。通过同时纳入内在张量稀疏和TV正则化建立了一个新的MECT重建模型。基于灵活框架利用迭代交替最小化方法求解重建模型。
将所提出的方法应用于数字体模和真实小鼠数据，以评估其可行性和可靠性。小鼠数据的重建和分解结果令人鼓舞，证明了所提出方法在抑制噪声同时保留图像细节方面的能力，这是其他方法所未观察到的。数字体模的成像数据表明，在所有比较方法中，该方法实现了最佳的直观重建和分解结果。与解析法、基于TV的方法和基于张量的方法相比，它们在重建图像上分别将均方根误差（RMSE）降低了89.75%、(50.75%)和(36.54%)。在分解结果中也观察到了这种现象，其中RMSE也分别降低了(97.96%)、(67.74%)、(72.05%)。
在本研究中，我们提出了一种基于光子计数探测器的MECT重建方法，利用内在张量稀疏和TV正则化。通过对重建和分解结果的定性和定量评估验证了数字体模和真实小鼠数据在噪声抑制和细节保留方面的改进，证实了所提出方法在MECT重建中的潜力。
