Koo Seul Ah, Jung Yunsub, Um Kyoung A, Kim Tae Hoon, Kim Ji Young, Park Chul Hwan
Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea.
Research Team, GE Healthcare Korea, Seoul 04637, Republic of Korea.
J Clin Med. 2023 May 16;12(10):3501. doi: 10.3390/jcm12103501.
This study evaluated the feasibility of deep-learning-based image reconstruction (DLIR) on coronary computed tomography angiography (CCTA). By using a 20 cm water phantom, the noise reduction ratio and noise power spectrum were evaluated according to the different reconstruction methods. Then 46 patients who underwent CCTA were retrospectively enrolled. CCTA was performed using the 16 cm coverage axial volume scan technique. All CT images were reconstructed using filtered back projection (FBP); three model-based iterative reconstructions (MBIR) of 40%, 60%, and 80%; and three DLIR algorithms: low (L), medium (M), and high (H). Quantitative and qualitative image qualities of CCTA were compared according to the reconstruction methods. In the phantom study, the noise reduction ratios of MBIR-40%, MBIR-60%, MBIR-80%, DLIR-L, DLIR-M, and DLIR-H were 26.7 ± 0.2%, 39.5 ± 0.5%, 51.7 ± 0.4%, 33.1 ± 0.8%, 43.2 ± 0.8%, and 53.5 ± 0.1%, respectively. The pattern of the noise power spectrum of the DLIR images was more similar to FBP images than MBIR images. In a CCTA study, CCTA yielded a significantly lower noise index with DLIR-H reconstruction than with the other reconstruction methods. DLIR-H showed a higher SNR and CNR than MBIR ( < 0.05). The qualitative image quality of CCTA with DLIR-H was significantly higher than that of MBIR-80% or FBP. The DLIR algorithm was feasible and yielded a better image quality than the FBP or MBIR algorithms on CCTA.
本研究评估了基于深度学习的图像重建（DLIR）在冠状动脉计算机断层扫描血管造影（CCTA）中的可行性。通过使用一个20厘米的水模体，根据不同的重建方法评估降噪率和噪声功率谱。然后回顾性纳入了46例行CCTA的患者。使用16厘米覆盖范围的轴向容积扫描技术进行CCTA。所有CT图像均采用滤波反投影（FBP）重建；三种基于模型的迭代重建（MBIR），分别为40%、60%和80%；以及三种DLIR算法：低（L）、中（M）和高（H）。根据重建方法比较CCTA的定量和定性图像质量。在模体研究中，MBIR-40%、MBIR-60%、MBIR-80%、DLIR-L、DLIR-M和DLIR-H的降噪率分别为26.7±0.2%、39.5±0.5%、51.7±0.4%、33.1±0.8%、43.2±0.8%和53.5±0.1%。与MBIR图像相比，DLIR图像的噪声功率谱模式与FBP图像更相似。在一项CCTA研究中，与其他重建方法相比，DLIR-H重建的CCTA产生的噪声指数显著更低。DLIR-H显示出比MBIR更高的信噪比（SNR）和对比噪声比（CNR）（<0.05）。DLIR-H的CCTA定性图像质量显著高于MBIR-80%或FBP。DLIR算法是可行的，并且在CCTA上产生的图像质量优于FBP或MBIR算法。
