Qi Hui, Cui Dingye, Xu Shijie, Li Wei, Zeng Qingshi
Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, China.
Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Abdom Radiol (NY). 2024 Dec 21. doi: 10.1007/s00261-024-04760-4.
To assess the impact of artificial intelligence iterative reconstruction algorithms (AIIR) on image quality with phantom and clinical studies.
The phantom images were reconstructed with the hybrid iterative algorithm (HIR: Karl 3D-3, 5, 7, 9) and AIIR (grades 1-5) algorithm. Noise power spectra (NPS), task transfer functions (TTF) were measured, and additionally sharpness was assessed using a "blur metric" procedure. Sixty-two consecutive patients underwent standard-dose and low-dose unenhanced abdominal computed tomography (CT) scans, i.e., SDCT and LDCT groups, respectively. The SDCT images reconstructed using the Karl 3D-5, and the LDCT images reconstructed using the Karl 3D-5 and the AIIR-3 and 5, respectively. CT values, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were assessed for hepatic parenchyma and paravertebral muscles. Images were independently evaluated by two radiologists for image-quality, noise, sharpness, and lesion diagnostic confidence.
In the phantom study, AIIR algorithm provided higher TTF and NPS average spatial frequency compared to HIR. In the clinical study, there was no statistically significant difference in CT values among the four reconstruction images (p > 0.05). The LDCT group AIIR-3 obtained the lowest SD values and the highest mean CNR and SNR values compared to the other three groups (p < 0.05). For qualitative assessment, the image subjective characteristic scores of AIIR-5 in the LDCT group, compared with the SDCT group, were not statistically significant (p > 0.05).
AIIR reduces radiation dose levels by approximately 78% and still maintains the image quality of unenhanced abdominal CT compared to HIR with SDCT.
NCT06142539.
通过体模和临床研究评估人工智能迭代重建算法（AIIR）对图像质量的影响。
使用混合迭代算法（HIR：Karl 3D - 3、5、7、9）和AIIR（1 - 5级）算法重建体模图像。测量噪声功率谱（NPS）、任务传递函数（TTF），并使用“模糊度量”程序评估图像清晰度。连续62例患者分别接受标准剂量和低剂量非增强腹部计算机断层扫描（CT），即分别为SDCT组和LDCT组。SDCT图像使用Karl 3D - 5重建，LDCT图像分别使用Karl 3D - 5以及AIIR - 3和5重建。评估肝实质和椎旁肌的CT值、标准差（SD）、信噪比（SNR）和对比噪声比（CNR）。由两名放射科医生独立评估图像的质量、噪声、清晰度和病变诊断置信度。
在体模研究中，与HIR相比，AIIR算法提供了更高的TTF和NPS平均空间频率。在临床研究中，四张重建图像的CT值之间无统计学显著差异（p > 0.05）。与其他三组相比，LDCT组的AIIR - 3获得了最低的SD值和最高的平均CNR及SNR值（p < 0.05）。对于定性评估，LDCT组中AIIR - 5的图像主观特征评分与SDCT组相比无统计学显著差异（p > 0.05）。
与使用SDCT的HIR相比，AIIR可将辐射剂量水平降低约78%，并且仍能保持非增强腹部CT的图像质量。
NCT06142539。
