Podgorsak Alexander R, Shiraz Bhurwani Mohammad Mahdi, Ionita Ciprian N
Canon Stroke and Vascular Research Center, 875 Ellicott Street, Buffalo, NY, 14203, USA.
Medical Physics Program, State University of New York at Buffalo, 955 Main Street, Buffalo, NY, 14203, USA.
Med Phys. 2021 Feb;48(2):615-626. doi: 10.1002/mp.14504. Epub 2020 Dec 30.
Computed tomography image reconstruction using truncated or sparsely acquired projection data to reduce radiation dose, iodine volume, and patient motion artifacts has been widely investigated. To continue these efforts, we investigated the use of machine learning-based reconstruction techniques using deep convolutional generative adversarial networks (DCGANs) and evaluated its effect using standard imaging metrics.
Ten thousand head computed tomography (CT) scans were collected from the 2019 RSNA Intracranial Hemorrhage Detection and Classification Challenge dataset. Sinograms were simulated and then resampled in both a one-third truncated and one-third sparse manner. DCGANs were tasked with correcting the incomplete projection data, either in the sinogram domain where the full sinogram was recovered by the DCGAN and then reconstructed, or the reconstruction domain where the incomplete data were first reconstructed and the sparse or truncation artifacts were corrected by the DCGAN. Seventy-five hundred images were used for network training and 2500 were withheld for network assessment using mean absolute error (MAE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR) between results of different correction techniques. Image data from a quality-assurance phantom were also resampled in the two manners and corrected and reconstructed for network performance assessment using line profiles across high-contrast features, the modulation transfer function (MTF), noise power spectrum (NPS), and Hounsfield Unit (HU) linearity analysis.
Better agreement with the fully sampled reconstructions were achieved from sparse acquisition corrected in the sinogram domain and the truncated acquisition corrected in the reconstruction domain. MAE, SSIM, and PSNR showed quantitative improvement from the DCGAN correction techniques. HU linearity of the reconstructions was maintained by the correction techniques for the sparse and truncated acquisitions. MTF curves reached the 10% modulation cutoff frequency at 5.86 lp/cm for the truncated corrected reconstruction compared with 2.98 lp/cm for the truncated uncorrected reconstruction, and 5.36 lp/cm for the sparse corrected reconstruction compared with around 2.91 lp/cm for the sparse uncorrected reconstruction. NPS analyses yielded better agreement across a range of frequencies between the resampled corrected phantom and truth reconstructions.
We demonstrated the use of DCGANs for CT-image correction from sparse and truncated simulated projection data, while preserving imaging quality of the fully sampled projection data.
利用截断或稀疏采集的投影数据进行计算机断层扫描图像重建以降低辐射剂量、碘用量和患者运动伪影，这一研究已广泛开展。为延续这些工作，我们研究了使用基于深度学习卷积生成对抗网络（DCGAN）的机器学习重建技术，并使用标准成像指标评估其效果。
从2019年RSNA颅内出血检测与分类挑战赛数据集中收集了一万例头部计算机断层扫描（CT）图像。模拟正弦图，然后分别以三分之一截断和三分之一稀疏的方式进行重采样。DCGAN的任务是校正不完整的投影数据，既可以在正弦图域中由DCGAN恢复完整正弦图后再进行重建，也可以在重建域中先对不完整数据进行重建，然后由DCGAN校正稀疏或截断伪影。使用不同校正技术结果之间的平均绝对误差（MAE）、结构相似性指数测量（SSIM）和峰值信噪比（PSNR），将7500幅图像用于网络训练，2500幅图像用于网络评估。质量保证体模的图像数据也以两种方式进行重采样，并进行校正和重建，以使用跨高对比度特征的线轮廓、调制传递函数（MTF）噪声功率谱（NPS）和亨氏单位（HU）线性分析来评估网络性能。
在正弦图域校正的稀疏采集和重建域校正的截断采集与全采样重建之间取得了更好的一致性。MAE、SSIM和PSNR显示出DCGAN校正技术在定量上的改进。校正技术保持了稀疏和截断采集重建的HU线性。截断校正重建的MTF曲线在调制频率为10%时截止频率为5.86 lp/cm，而截断未校正重建为2.98 lp/cm；稀疏校正重建为5.36 lp/cm，而稀疏未校正重建约为2.91 lp/cm。NPS分析在重采样校正体模和真实重建之间的一系列频率上产生了更好的一致性。
我们证明了使用DCGAN对稀疏和截断模拟投影数据进行CT图像校正，同时保留全采样投影数据的成像质量。
