Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
Radiol Med. 2022 Oct;127(10):1098-1105. doi: 10.1007/s11547-022-01539-9. Epub 2022 Sep 7.
To compare liver MRI with AIR Recon Deep Learning™(ARDL) algorithm applied and turned-off (NON-DL) with conventional high-resolution acquisition (NAÏVE) sequences, in terms of quantitative and qualitative image analysis and scanning time.
This prospective study included fifty consecutive volunteers (31 female, mean age 55.5 ± 20 years) from September to November 2021. 1.5 T MRI was performed and included three sets of images: axial single-shot fast spin-echo (SSFSE) T2 images, diffusion-weighted images(DWI) and apparent diffusion coefficient(ADC) maps acquired with both ARDL and NAÏVE protocol; the NON-DL images, were also assessed. Two radiologists in consensus drew fixed regions of interest in liver parenchyma to calculate signal-to-noise-ratio (SNR) and contrast to-noise-ratio (CNR). Subjective image quality was assessed by two other radiologists independently with a five-point Likert scale. Acquisition time was recorded.
SSFSE T2 objective analysis showed higher SNR and CNR for ARDL vs NAÏVE, ARDL vs NON-DL(all P < 0.013). Regarding DWI, no differences were found for SNR with ARDL vs NAÏVE and, ARDL vs NON-DL (all P > 0.2517).CNR was higher for ARDL vs NON-DL(P = 0.0170), whereas no differences were found between ARDL and NAÏVE(P = 1). No differences were observed for all three comparisons, in terms of SNR and CNR, for ADC maps (all P > 0.32). Qualitative analysis for all sequences showed better overall image quality for ARDL with lower truncation artifacts, higher sharpness and contrast (all P < 0.0070) with excellent inter-rater agreement (k ≥ 0.8143). Acquisition time was lower in ARDL sequences compared to NAÏVE (SSFSE T2 = 19.08 ± 2.5 s vs. 24.1 ± 2 s and DWI = 207.3 ± 54 s vs. 513.6 ± 98.6 s, all P < 0.0001).
ARDL applied on upper abdomen showed overall better image quality and reduced scanning time compared with NAÏVE protocol.
比较肝脏 MRI 与 AIR Recon Deep Learning™（ARDL）算法应用（DL）与未应用（非 DL），包括定量和定性图像分析以及扫描时间。
本前瞻性研究纳入 2021 年 9 月至 11 月期间的 50 名连续志愿者（31 名女性，平均年龄 55.5±20 岁）。采用 1.5T MRI 进行检查，包括三组图像：轴位单次激发快速自旋回波（SSFSE）T2 图像、扩散加权成像（DWI）和表观扩散系数（ADC）图，采用 ARDL 和 NAÏVE 方案采集；还评估了非 DL 图像。两位放射科医生通过共识在肝实质中画出固定的感兴趣区，以计算信噪比（SNR）和对比噪声比（CNR）。另外两位放射科医生独立使用 5 分李克特量表评估主观图像质量。记录采集时间。
SSFSE T2 客观分析显示 ARDL 与 NAÏVE、ARDL 与 NON-DL 相比 SNR 和 CNR 均较高（均 P<0.013）。在 DWI 方面，ARDL 与 NAÏVE 的 SNR 无差异，与 NON-DL 的 SNR 无差异（均 P>0.2517）。ARDL 与 NON-DL 的 CNR 较高（P=0.0170），而 ARDL 与 NAÏVE 的 CNR 无差异（P=1）。对于 ADC 图的 SNR 和 CNR，所有三种比较均无差异（均 P>0.32）。所有序列的定性分析均显示，ARDL 的整体图像质量更好，截断伪影更少，清晰度和对比度更高（均 P<0.0070），并且具有良好的组内一致性（k≥0.8143）。与 NAÏVE 序列相比，ARDL 序列的采集时间更短（SSFSE T2=19.08±2.5s 与 24.1±2s，DWI=207.3±54s 与 513.6±98.6s，均 P<0.0001）。
与 NAÏVE 方案相比，上腹部 ARDL 的整体图像质量更好，扫描时间更短。
