Ichikawa Yasutaka, Kanii Yoshinori, Yamazaki Akio, Kobayashi Mai, 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 Apr;38(2):1236-1244. doi: 10.1007/s10278-024-01214-7. Epub 2024 Aug 13.
To evaluate the usefulness of low-keV multiphasic computed tomography (CT) with deep learning image reconstruction (DLIR) in improving the delineation of pancreatic ductal adenocarcinoma (PDAC) compared to conventional hybrid iterative reconstruction (HIR). Thirty-five patients with PDAC who underwent multiphasic CT were retrospectively evaluated. Raw data were reconstructed with two energy levels (40 keV and 70 keV) of virtual monochromatic imaging (VMI) using HIR (ASiR-V50%) and DLIR (TrueFidelity-H). Contrast-to-noise ratio (CNR) was calculated from the CT values within regions of interest in tumor and normal pancreas in the pancreatic parenchymal phase images. Lesion conspicuity of PDAC in pancreatic parenchymal phase on 40-keV HIR, 40-keV DLIR, and 70-keV DLIR images was qualitatively rated on a 5-point scale, using 70-keV HIR images as reference (score 1 = poor; score 3 = equivalent to reference; score 5 = excellent) by two radiologists. CNR of 40-keV DLIR images (median 10.4, interquartile range (IQR) 7.8-14.9) was significantly higher than that of the other VMIs (40 keV HIR, median 6.2, IQR 4.4-8.5, P < 0.0001; 70-keV DLIR, median 6.3, IQR 5.1-9.9, P = 0.0002; 70-keV HIR, median 4.2, IQR 3.1-6.1, P < 0.0001). CNR of 40-keV DLIR images were significantly better than those of the 40-keV HIR and 70-keV HIR images by 72 ± 22% and 211 ± 340%, respectively. Lesion conspicuity scores on 40-keV DLIR images (observer 1, 4.5 ± 0.7; observer 2, 3.4 ± 0.5) were significantly higher than on 40-keV HIR (observer 1, 3.3 ± 0.9, P < 0.0001; observer 2, 3.1 ± 0.4, P = 0.013). DLIR is a promising reconstruction method to improve PDAC delineation in 40-keV VMI at the pancreatic parenchymal phase compared to conventional HIR.
为评估与传统混合迭代重建（HIR）相比，低keV多期计算机断层扫描（CT）联合深度学习图像重建（DLIR）在改善胰腺导管腺癌（PDAC）轮廓显示方面的效用。对35例接受多期CT检查的PDAC患者进行回顾性评估。原始数据使用HIR（自适应统计迭代重建-V50%）和DLIR（真保真度-H）以两个能量水平（40 keV和70 keV）的虚拟单色成像（VMI）进行重建。在胰腺实质期图像中，根据肿瘤和正常胰腺感兴趣区域内的CT值计算对比度噪声比（CNR）。由两名放射科医生以70 keV HIR图像为参考（评分1 = 差；评分3 = 与参考等效；评分5 = 优），对40 keV HIR、40 keV DLIR和70 keV DLIR图像上PDAC在胰腺实质期的病变清晰度进行5分制定性评分。40 keV DLIR图像的CNR（中位数10.4，四分位数间距（IQR）7.8 - 14.9）显著高于其他VMI（40 keV HIR，中位数6.2，IQR 4.4 - 8.5，P < 0.0001；70 keV DLIR，中位数6.3，IQR 5.1 - 9.9，P = 0.0002；70 keV HIR，中位数4.2，IQR 3.1 - 6.1，P < 0.0001）。40 keV DLIR图像的CNR分别比40 keV HIR和70 keV HIR图像显著提高72±22%和211±340%。40 keV DLIR图像上的病变清晰度评分（观察者1，4.5±0.7；观察者2，3.4±0.5）显著高于40 keV HIR（观察者1，3.3±0.9，P < 0.0001；观察者2，3.1±0.4，P = 0.013）。与传统HIR相比，DLIR是一种有前景的重建方法，可改善40 keV VMI在胰腺实质期对PDAC的轮廓显示。
