Yang Gongxin, Wang Haowei, Liu Ling, Ma Qifan, Shi Huimin, Yuan Ying
Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
J Imaging Inform Med. 2025 Feb 14. doi: 10.1007/s10278-024-01287-4.
This study aims to investigate the impact of combining deep learning image reconstruction (DLIR) and metal artifacts reduction (MAR) algorithms on the quality of CT images with metal implants under different scanning conditions. Four images of the maxillofacial region in pigs were taken using different metal implants for evaluation. The scans were conducted at three different dose levels (CTDIvol: 20/10/5 mGy). The images were reconstructed using three different methods: filtered back projection (FBP), adaptive statistical iterative reconstruction with Veo at a 50% level (AV50), and DLIR at three levels (low, medium, and high). Regions of interest (ROIs) were identified in various tissues (near/far/reference fat, muscle, bone) both with and without metal implants and artifacts. Parameters such as standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and metal artifact index (MAI) were calculated. Additionally, two experienced radiologists evaluated the subjective image quality (IQ) using a 5-point Likert scale. (1) Both observers rated MAR generated significantly lower artifact scores than non-MAR in all types of tissues (P < 0.01), except for the far shadow and bloom in bone (phantoms 1, 3, 4) and the far bloom in muscle (phantom 3) without significant differences (P = 1.0). (2) Under the same scanning condition, DLIR at three levels produced a smaller SD than those of FBP and AV50 (P < 0.05). (3) Compared to FBP and AV50, DLIR denoted a better reduction of MAI and improvement of SNR and CNR (P < 0.05) for most tissues between the four phantoms. (4) Subjective overall IQ was superior with the increasement of DLIR level (P < 0.05) and both observers agreed that DLIR produced better artifact reductions compared with FBP and AV50. The combination of DLIR and MAR algorithms can enhance image quality, significantly reduce metal artifacts, and offer high clinical value.
本研究旨在探讨深度学习图像重建（DLIR）和金属伪影减少（MAR）算法相结合，在不同扫描条件下对带有金属植入物的CT图像质量的影响。使用不同的金属植入物对猪的颌面区域进行了四张图像采集以作评估。扫描在三种不同剂量水平（CTDIvol：20/10/5 mGy）下进行。图像使用三种不同方法重建：滤波反投影（FBP）、Veo在50%水平的自适应统计迭代重建（AV50）以及三个水平（低、中、高）的DLIR。在有和没有金属植入物及伪影的各种组织（近/远/参考脂肪、肌肉、骨骼）中确定感兴趣区域（ROI）。计算了诸如标准差（SD）、信噪比（SNR）、对比噪声比（CNR）和金属伪影指数（MAI）等参数。此外，两名经验丰富的放射科医生使用5分李克特量表评估主观图像质量（IQ）。（1）除了骨骼中的远阴影和光晕（模型1、3、4）以及肌肉中的远光晕（模型3）无显著差异（P = 1.0）外，两位观察者均评定在所有类型组织中，MAR产生的伪影分数均显著低于非MAR（P < 0.01）。（2）在相同扫描条件下，三个水平的DLIR产生的SD均小于FBP和AV50（P < 0.05）。（3）与FBP和AV50相比，DLIR在四个模型中的大多数组织中显示出更好的MAI降低以及SNR和CNR改善（P < 0.05）。（4）主观总体IQ随着DLIR水平的提高而更优（P < 0.05），且两位观察者均认为与FBP和AV50相比，DLIR产生了更好的伪影减少效果。DLIR和MAR算法的组合可提高图像质量，显著减少金属伪影，并具有较高的临床价值。
