Technology and Tool Development for BACPAC: Qualitative and Quantitative Analysis of Accelerated Lumbar Spine MRI with Deep-Learning Based Image Reconstruction at 3T.
Technology and Tool Development for BACPAC: Qualitative and Quantitative Analysis of Accelerated Lumbar Spine MRI with Deep-Learning Based Image Reconstruction at 3T.
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DOI：
10.1093/pm/pnad035
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发表时间：
2023-08-04
期刊：
影响因子：
3.1
通讯作者：
Majumdar, Sharmila
中科院分区：
文献类型：
--
作者：
Han, Misung;Bahroos, Emma;Hess, Madeline E.;Chin, Cynthia T.;Gao, Kenneth T.;Shin, David D.;Villanueva-Meyer, Javier E.;Link, Thomas M.;Pedoia, Valentina;Majumdar, Sharmila
关键词：
评估快速采集与深度学习重建相结合是否可以为腰椎磁共振成像(MRI)提供与标准护理采集相媲美的诊断有用图像和定量评估。18名患者同时接受了标准序列和FAST序列的成像，每个序列均包括矢状位脂肪抑制T2加权序列、矢状位T1加权序列和轴向T2加权2D快速自旋回波序列。另外，使用供应商提供的具有三种不同降噪因子的深度学习重建来重建快速采集的数据。为了进行定性分析，三位放射科医生对标准图像以及具有和不具有深度学习重建的快速图像进行了五个不同类别的分级。为了进行定量分析，将卷积神经网络应用于矢状面T1加权图像，对椎间盘和椎体进行分割，得到椎间盘高度和椎体体积。基于定性分数的非劣性检验，没有深度学习重建的快速图像在大多数类别中都不如标准图像。然而，深度学习重建提高了平均得分，在45次比较中有24次观察到非劣势(全部使用矢状位T2加权图像，而与矢状位T1加权和轴位T2加权图像比较有4/5)。观察者间的变异性增加了50%和75%的降噪因子。具有50%和75%降噪因子的深度学习重建快速图像具有与标准图像相当的椎间盘高度和椎体体积(R20.86表示椎体高度，R2 0.98表示椎体体积)。这项研究表明，深度学习重建的快速采集图像具有提供非劣质图像质量和与标准临床图像相当的定量评估的潜力。
To evaluate whether combining fast acquisitions with deep-learning reconstruction can provide diagnostically useful images and quantitative assessment comparable to standard-of-care acquisitions for lumbar spine magnetic resonance imaging (MRI). Eighteen patients were imaged with both standard protocol and fast protocol using reduced signal averages, each protocol including sagittal fat-suppressed T2-weighted, sagittal T1-weighted, and axial T2-weighted 2D fast spin-echo sequences. Fast-acquisition data was additionally reconstructed using vendor-supplied deep-learning reconstruction with three different noise reduction factors. For qualitative analysis, standard images as well as fast images with and without deep-learning reconstruction were graded by three radiologists on five different categories. For quantitative analysis, convolutional neural networks were applied to sagittal T1-weighted images to segment intervertebral discs and vertebral bodies, and disc heights and vertebral body volumes were derived. Based on noninferiority testing on qualitative scores, fast images without deep-learning reconstruction were inferior to standard images for most categories. However, deep-learning reconstruction improved the average scores, and noninferiority was observed over 24 out of 45 comparisons (all with sagittal T2-weighted images while 4/5 comparisons with sagittal T1-weighted and axial T2-weighted images). Interobserver variability increased with 50 and 75% noise reduction factors. Deep-learning reconstructed fast images with 50% and 75% noise reduction factors had comparable disc heights and vertebral body volumes to standard images (r2 0.86 for disc heights and r2 0.98 for vertebral body volumes). This study demonstrated that deep-learning-reconstructed fast-acquisition images have the potential to provide noninferior image quality and comparable quantitative assessment to standard clinical images.
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影响因子：
3.3
作者：
Hammernik K;Klatzer T;Kobler E;Recht MP;Sodickson DK;Pock T;Knoll F
通讯作者：
Knoll F
影响因子：
19.7
作者：
Kim, Minjae;Kim, Ho Sung;Lebel, Marc R.
通讯作者：
Lebel, Marc R.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
DOI：
10.3174/ajnr.a4173
发表时间：
2015-04
期刊：
AJNR. American journal of neuroradiology
影响因子：
--
作者：
Brinjikji W;Luetmer PH;Comstock B;Bresnahan BW;Chen LE;Deyo RA;Halabi S;Turner JA;Avins AL;James K;Wald JT;Kallmes DF;Jarvik JG
通讯作者：
Jarvik JG
影响因子：
3.3
作者：
Griswold, MA;Jakob, PM;Haase, A
通讯作者：
Haase, A