Optimization of null point in Look-Locker images for myocardial late gadolinium enhancement imaging using deep learning and a smartphone
Optimization of null point in Look-Locker images for myocardial late gadolinium enhancement imaging using deep learning and a smartphone
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DOI：
10.1007/s00330-023-09465-8
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发表时间：
2023-02
影响因子：
5.9
通讯作者：
Y. Ohta;E. Tateishi;Y. Morita;Tatsuya Nishii;Akiyuki Kotoku;H. Horinouchi;Midori Fukuyama;T. Fukuda
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文献类型：
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作者：
Y. Ohta;E. Tateishi;Y. Morita;Tatsuya Nishii;Akiyuki Kotoku;H. Horinouchi;Midori Fukuyama;T. Fukuda
目的利用卷积神经网络(CNN)从Look-Locker童子军图像中确定最佳反转时间(TI)，并探讨使用智能手机校正TI的可行性。方法在这项回顾研究中，从2017-2020年间连续1113次心脏MR检查中使用Look-Locker方法提取TI-Scout图像，并进行心肌晚期Gd增强。参考TI零点由有经验的放射科医生和有经验的心脏病专家独立目测，并进行定量测量。开发了一个用于评估TI偏离零点的CNN，并在PC和智能手机应用程序中实现了该方法。智能手机捕捉了4K或300万像素显示器上的图像，并确定了CNN在每个显示器上的表现。计算了在PC和智能手机上使用深度学习的最佳、欠校正率和过校正率。结果对于PC，96.4%(772/749)的图像被归类为最佳，欠矫正率和过矫正率分别为1.2%(9/749)和2.4%(18/749)。对于4K图像，93.5%(700/749)的图像为最佳，欠矫正率为3.9%(29/749)，过矫正率为2.7%(20/749)。对于300万像素的图像，89.6%(671/749)的图像被归类为最佳，欠校正率和过校正率分别为3.3%(25/749)和7.0%(53/749)。在基于患者的评估中，被归类为最佳范围内的受试者使用CNN从72.0%(77/107)增加到91.6%(98/107)。结论使用深度学习和智能手机对Look-Locker图像进行TI优化是可行的。关键点·深度学习模型将TI-Scout图像校正到LGE成像的最佳零点内。·通过使用智能手机在监视器上捕获TI-Scout图像，可以立即确定TI与零点的偏差。·使用该模型，可以将TI零点设置为与经验丰富的放射技术专家相同的程度。
ObjectivesTo determine the optimal inversion time (TI) from Look-Locker scout images using a convolutional neural network (CNN) and to investigate the feasibility of correcting TI using a smartphone.MethodsIn this retrospective study, TI-scout images were extracted using a Look-Locker approach from 1113 consecutive cardiac MR examinations performed between 2017 and 2020 with myocardial late gadolinium enhancement. Reference TI null points were independently determined visually by an experienced radiologist and an experienced cardiologist, and quantitatively measured. A CNN was developed to evaluate deviation of TI from the null point and then implemented in PC and smartphone applications. Images on 4 K or 3-megapixel monitors were captured by a smartphone, and CNN performance on each monitor was determined. Optimal, undercorrection, and overcorrection rates using deep learning on the PC and smartphone were calculated. For patient analysis, TI category differences in pre- and post-correction were evaluated using the TI null point used in late gadolinium enhancement imaging.ResultsFor PC, 96.4% (772/749) of images were classified as optimal, with under- and overcorrection rates of 1.2% (9/749) and 2.4% (18/749), respectively. For 4 K images, 93.5% (700/749) of images were classified as optimal, with under- and overcorrection rates of 3.9% (29/749) and 2.7% (20/749), respectively. For 3-megapixel images, 89.6% (671/749) of images were classified as optimal, with under- and overcorrection rates of 3.3% (25/749) and 7.0% (53/749), respectively. On patient-based evaluations, subjects classified as within optimal range increased from 72.0% (77/107) to 91.6% (98/107) using the CNN.ConclusionsOptimizing TI on Look-Locker images was feasible using deep learning and a smartphone.Key Points• A deep learning model corrected TI-scout images to within optimal null point for LGE imaging.• By capturing the TI-scout image on the monitor with a smartphone, the deviation of the TI from the null point can be immediately determined.• Using this model, TI null points can be set to the same degree as that by an experienced radiological technologist.