Department of Radiology, Osaka University Graduate School of Medicine.
Department of Radiology, Osaka University Hospital.
Magn Reson Med Sci. 2024 Apr 1;23(2):214-224. doi: 10.2463/mrms.mp.2022-0111. Epub 2023 Mar 29.
To compare the effects of deep learning reconstruction (DLR) on respiratory-triggered T2-weighted MRI of the liver between single-shot fast spin-echo (SSFSE) and fast spin-echo (FSE) sequences.
Respiratory-triggered fat-suppressed liver T2-weighted MRI was obtained with the FSE and SSFSE sequences at the same spatial resolution in 55 patients. Conventional reconstruction (CR) and DLR were applied to each sequence, and the SNR and liver-to-lesion contrast were measured on FSE-CR, FSE-DLR, SSFSE-CR, and SSFSE-DLR images. Image quality was independently assessed by three radiologists. The results of the qualitative and quantitative analyses were compared among the four types of images using repeated-measures analysis of variance or Friedman's test for normally and non-normally distributed data, respectively, and a visual grading characteristics (VGC) analysis was performed to evaluate the image quality improvement by DLR on the FSE and SSFSE sequences.
The liver SNR was lowest on SSFSE-CR and highest on FSE-DLR and SSFSE-DLR (P < 0.01). The liver-to-lesion contrast did not differ significantly among the four types of images. Qualitatively, noise scores were worst on SSFSE-CR but best on SSFSE-DLR because DLR significantly reduced noise (P < 0.01). In contrast, artifact scores were worst both on FSE-CR and FSE-DLR (P < 0.01) because DLR did not reduce the artifacts. Lesion conspicuity was significantly improved by DLR compared with CR in the SSFSE (P < 0.01) but not in FSE sequences for all readers. Overall image quality was significantly improved by DLR compared with CR for all readers in the SSFSE (P < 0.01) but only one reader in the FSE (P < 0.01). The mean area under the VGC curve values for the FSE-DLR and SSFSE-DLR sequences were 0.65 and 0.94, respectively.
In liver T2-weighted MRI, DLR produced more marked improvements in image quality in SSFSE than in FSE.
比较单次激发快速自旋回波（SSFSE）与快速自旋回波（FSE）序列在肝脏呼吸触发 T2 加权 MRI 中深度学习重建（DLR）的效果。
对 55 例患者在相同空间分辨率下进行 FSE 和 SSFSE 序列的呼吸触发脂肪抑制肝 T2 加权 MRI。对每个序列应用常规重建（CR）和 DLR，并在 FSE-CR、FSE-DLR、SSFSE-CR 和 SSFSE-DLR 图像上测量 SNR 和肝与病灶对比度。三位放射科医生独立评估图像质量。采用重复测量方差分析或 Friedman 检验分别对正态和非正态分布数据进行四组图像之间的定性和定量分析比较，采用视觉分级特征（VGC）分析评估 FSE 和 SSFSE 序列上 DLR 对图像质量的改善效果。
SSFSE-CR 时肝 SNR 最低，FSE-DLR 和 SSFSE-DLR 时最高（P＜0.01）。四组图像的肝与病灶对比度无显著差异。定性评估时，SSFSE-CR 的噪声评分最差，但 SSFSE-DLR 的噪声评分最好，因为 DLR 显著降低了噪声（P＜0.01）。相反，FSE-CR 和 FSE-DLR 的伪影评分最差（P＜0.01），因为 DLR 未降低伪影。与 CR 相比，所有读者在 SSFSE 中 DLR 均显著提高了病灶的对比（P＜0.01），但在 FSE 序列中无一读者如此。与 CR 相比，所有读者在 SSFSE 中 DLR 均显著改善了整体图像质量（P＜0.01），但在 FSE 中仅一位读者如此（P＜0.01）。FSE-DLR 和 SSFSE-DLR 序列的 VGC 曲线下面积均值分别为 0.65 和 0.94。
在肝脏 T2 加权 MRI 中，与 FSE 相比，SSFSE 中 DLR 对图像质量的改善更为显著。
