Department of Radiology, Nagoya University Graduate School of Medicine.
Department of Otorhinolaryngology, Nagoya University Graduate School of Medicine.
Magn Reson Med Sci. 2021 Sep 1;20(3):272-279. doi: 10.2463/mrms.mp.2020-0082. Epub 2020 Aug 21.
MRI of endolymphatic hydrops (EH) 4 h after intravenous administration of a single dose of gadolinium-based contrast agent is used for clinical examination in some institutions; however, further improvement in image quality would be valuable for wider clinical utility. Denoising using deep learning reconstruction (Advanced Intelligent Clear-IQ Engine [AiCE]) has been reported for CT and MR. The purpose of this study was to compare the contrast-to-noise ratio of endolymph to perilymph (CNR) between the improved hybrid of reversed image of the positive endolymph signal and the native image of the perilymph signal multiplied with the heavily T-weighted MR cisternography (iHYDROPS-Mi2) images, which used AiCE for the three source images (i.e. positive endolymph image [PEI], positive perilymph image [PPI], MR cisternography [MRC]) to those that did not use AiCE. We also examined if there was a difference between iHYDROPS-Mi2 images with and without AiCE for degree of visual grading of EH and in endolymphatic area [EL] ratios.
Nine patients with suspicion of EH were imaged on a 3T MR scanner. iHYDROPS images were generated by subtraction of PEI images from PPI images. iHYDROPS-Mi2 images were then generated by multiplying MRC with iHYDROPS images. The CNR and EL ratio were measured on the iHYDROPS-Mi2 images. Degree of radiologist visual grading for EH was evaluated.
Mean CNR ± standard deviation was 1681.8 ± 845.2 without AiCE and 7738.6 ± 5149.2 with AiCE (P = 0.00002). There was no significant difference in EL ratio for images with and without AiCE. Radiologist grading for EH agreed completely between the 2 image types in both the cochlea and vestibule.
The CNR of iHYDROPS-Mi2 images with AiCE had more than a fourfold increase compared with that without AiCE. Use of AiCE did not adversely affect radiologist grading of EH.
在一些机构中，静脉注射单剂量钆基对比剂后 4 小时行内淋巴积水（EH）的 MRI 检查用于临床检查；然而，进一步提高图像质量对于更广泛的临床应用将是有价值的。使用深度学习重建（高级智能清晰 IQ 引擎[AiCE]）对 CT 和 MR 进行去噪已经有报道。本研究的目的是比较使用 AiCE 处理三个源图像（即正内淋巴图像[PEI]、正外淋巴图像[PPI]、MR 脑池造影[MRC]）的混合反转正内淋巴信号图像和原始外淋巴信号图像与未使用 AiCE 的混合反转正内淋巴信号图像和原始外淋巴信号图像的内淋巴与外淋巴的对比噪声比（CNR），以评估增强混合反转正内淋巴信号图像与原始外淋巴信号图像乘积（iHYDROPS-Mi2）图像的效果。我们还检查了是否存在增强混合反转正内淋巴信号图像与原始外淋巴信号图像乘积（iHYDROPS-Mi2）图像有无 AiCE 对内淋巴积水（EH）的视觉分级程度和内淋巴面积[EL]比值的差异。
对 9 例疑似 EH 的患者在 3T MR 扫描仪上进行成像。通过从 PPI 图像中减去 PEI 图像来生成 iHYDROPS 图像。然后，通过将 MRC 与 iHYDROPS 图像相乘来生成 iHYDROPS-Mi2 图像。在 iHYDROPS-Mi2 图像上测量 CNR 和 EL 比值。评估放射科医生对 EH 的视觉分级程度。
无 AiCE 时的平均 CNR ±标准差为 1681.8 ± 845.2，有 AiCE 时为 7738.6 ± 5149.2（P = 0.00002）。有无 AiCE 的图像的 EL 比值无显著差异。EH 的放射科医生分级在耳蜗和前庭的两种图像类型之间完全一致。
与无 AiCE 的 iHYDROPS-Mi2 图像相比，具有 AiCE 的 iHYDROPS-Mi2 图像的 CNR 增加了四倍以上。使用 AiCE 不会对 EH 的放射科医生分级产生不利影响。
