Seo Geojeong, Lee Sun Joo, Park Dae Hyun, Paeng Sung Hwa, Koerzdoerfer Gregor, Nickel Marcel Dominik, Sung Jaekon
Department of Radiology, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
Department of Orthopaedic Surgery, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
Skeletal Radiol. 2023 Dec;52(12):2451-2459. doi: 10.1007/s00256-023-04364-x. Epub 2023 May 26.
To validate the subjective image quality and lesion detectability of deep learning-accelerated Dixon (DL-Dixon) imaging of the cervical spine compared with routine Dixon imaging.
A total of 50 patients underwent sagittal routine Dixon and DL-Dixon imaging of the cervical spine. Acquisition parameters were compared and non-uniformity (NU) values were calculated. Two radiologists independently assessed the two imaging methods for subjective image quality and lesion detectability. Interreader and intermethod agreements were estimated with weighted kappa values.
Compared with the routine Dixon imaging, the DL-Dixon imaging reduced the acquisition time by 23.76%. The NU value is slightly higher in DL-Dixon imaging (p value: 0.015). DL-Dixon imaging showed superior visibility of all four anatomical structures (spinal cord, disc margin, dorsal root ganglion, and facet joint) for both readers (p value: < 0.001 ~ 0.002). The motion artifact scores were slightly higher in the DL-Dixon images than in routine Dixon images (p value = 0.785). Intermethod agreements were almost perfect for disc herniation, facet osteoarthritis, uncovertebral arthritis, central canal stenosis (κ range: 0.830 ~ 0.980, all p values < 0.001) and substantial to almost perfect for foraminal stenosis (κ = 0.955, 0.705 for each reader). There was an improvement in the interreader agreement of foraminal stenosis by DL-Dixon images, from moderate to substantial agreement.
The DLR sequence can substantially decrease the acquisition time of the Dixon sequence with subjective image quality at least as good as the conventional sequence. And no significant differences in lesion detectability were observed between the two sequence types.
验证与常规狄克逊成像相比，深度学习加速的颈椎狄克逊（DL - 狄克逊）成像的主观图像质量和病变可检测性。
共50例患者接受了颈椎矢状面常规狄克逊成像和DL - 狄克逊成像。比较采集参数并计算不均匀性（NU）值。两名放射科医生独立评估这两种成像方法的主观图像质量和病变可检测性。通过加权kappa值估计阅片者间和方法间的一致性。
与常规狄克逊成像相比，DL - 狄克逊成像将采集时间缩短了23.76%。DL - 狄克逊成像的NU值略高（p值：0.015）。对于两位阅片者，DL - 狄克逊成像显示所有四个解剖结构（脊髓、椎间盘边缘、背根神经节和小关节）的可视性均更佳（p值：<0.001至...0.002）。DL - 狄克逊图像中的运动伪影评分略高于常规狄克逊图像（p值 = 0.785）。对于椎间盘突出、小关节骨关节炎、钩椎关节关节炎、中央管狭窄，方法间一致性几乎完美（κ范围：0.830至0.980，所有p值<0.001），对于椎间孔狭窄则为实质性至几乎完美（κ = 0.955，每位阅片者分别为0.705）。DL - 狄克逊图像使椎间孔狭窄的阅片者间一致性从中等提高到实质性一致。
DLR序列可大幅缩短狄克逊序列的采集时间，且主观图像质量至少与传统序列一样好。两种序列类型在病变可检测性方面未观察到显著差异。
