Ichikawa Yasutaka, Hasegawa Daisuke, Domae Kensuke, Nagata Motonori, Sakuma Hajime
Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
J Imaging Inform Med. 2025 May 12. doi: 10.1007/s10278-025-01529-z.
The purpose of this study was to investigate the utility of deep learning image reconstruction at medium and high intensity levels (DLIR-M and DLIR-H, respectively) for better delineation of liver metastases in pre-contrast and post-contrast CT, compared to conventional hybrid iterative reconstruction (IR) methods. Forty-one patients with liver metastases who underwent abdominal CT were studied. The raw data were reconstructed with three different algorithms: hybrid IR (ASiR-V 50%), DLIR-M (TrueFildelity-M), and DLIR-H (TrueFildelity-H). Three experienced radiologists independently rated the lesion conspicuity of liver metastases on a qualitative 5-point scale (score 1 = very poor; score 5 = excellent). The observers also selected each image series for pre- and post-contrast CT per patient that was considered most preferable for liver metastases assessment. For pre-contrast CT, lesion conspicuity scores for DLIR-H and DLIR-M were significantly higher than those for hybrid IR for two of the three observers, while there was no significant difference for one observer. For post-contrast CT, the lesion conspicuity scores for DLIR-H images were significantly higher than those for DLIR-M images for two of the three observers on post-contrast CT (Observer 1: DLIR-H, 4.3 ± 0.8 vs. DLIR-M, 3.9 ± 0.9, p = 0.0006; Observer 3: DLIR-H, 4.6 ± 0.6 vs. DLIR-M, 4.3 ± 0.6, p = 0.0013). For post-contrast CT, all observers most often selected DLIR-H as the best reconstruction method for the diagnosis of liver metastases. However, in the pre-contrast CT, there was variation among the three observers in determining the most preferred image reconstruction method, and DLIR was not necessarily preferred over hybrid IR for the diagnosis of liver metastases.
本研究的目的是调查中、高强度水平的深度学习图像重建（分别为DLIR-M和DLIR-H）相比于传统混合迭代重建（IR）方法，在平扫及增强CT中对肝转移瘤更好的勾画效用。对41例接受腹部CT检查的肝转移瘤患者进行了研究。原始数据采用三种不同算法重建：混合IR（ASiR-V 50%）、DLIR-M（TrueFildelity-M）和DLIR-H（TrueFildelity-H）。三名经验丰富的放射科医生独立地根据定性的5分制对肝转移瘤的病灶清晰度进行评分（1分 = 非常差；5分 = 优秀）。观察者还为每位患者选择了他们认为最适合肝转移瘤评估的平扫及增强CT的每个图像序列。对于平扫CT，三名观察者中有两名观察者认为DLIR-H和DLIR-M的病灶清晰度评分显著高于混合IR，而一名观察者认为无显著差异。对于增强CT，三名观察者中有两名观察者认为增强CT上DLIR-H图像的病灶清晰度评分显著高于DLIR-M图像（观察者1：DLIR-H，4.3±0.8 vs. DLIR-M，3.9±0.9，p = 0.0006；观察者3：DLIR-H，4.6±0.6 vs. DLIR-M，4.3±0.6，p = 0.0013）。对于增强CT，所有观察者最常选择DLIR-H作为诊断肝转移瘤的最佳重建方法。然而，在平扫CT中，三名观察者在确定最优选的图像重建方法时存在差异，并且在诊断肝转移瘤方面，DLIR不一定比混合IR更受青睐。
