Department of Radiology, National Cerebral and Cardiovascular Center, Suita City, Osaka, 564-8565, Japan.
Eur Radiol. 2023 Jul;33(7):4688-4697. doi: 10.1007/s00330-023-09465-8. Epub 2023 Feb 21.
To 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.
In 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.
For 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.
Optimizing TI on Look-Locker images was feasible using deep learning and a smartphone.
• 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.
使用卷积神经网络（CNN）确定 Look-Locker 扫描图像的最佳反转时间（TI），并探讨使用智能手机校正 TI 的可行性。
本回顾性研究从 2017 年至 2020 年期间进行的 1113 例连续心脏磁共振检查中提取了使用 Look-Locker 方法的 TI 扫描图像，这些检查均进行了心肌晚期钆增强检查。由一位有经验的放射科医生和一位有经验的心脏病专家独立确定 TI 零参考点的视觉和定量测量。开发了一个 CNN 来评估 TI 偏离零参考点的程度，然后将其实现到 PC 和智能手机应用程序中。使用智能手机在 4K 或 300 万像素的显示器上捕获图像，并确定 CNN 在每个显示器上的性能。在 PC 和智能手机上使用深度学习计算最佳、欠校正和过校正的概率。对于患者分析，使用晚期钆增强成像中使用的 TI 零参考点来评估校正前后 TI 分类的差异。
对于 PC，96.4%（772/749）的图像被归类为最佳，过校正和欠校正的比例分别为 1.2%（9/749）和 2.4%（18/749）。对于 4K 图像，93.5%（700/749）的图像被归类为最佳，过校正和欠校正的比例分别为 3.9%（29/749）和 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，过校正和欠校正的比例分别为 3.3%（25/749）和 7.0%（53/749）。在基于患者的评估中，使用 CNN 将分类为最佳范围内的患者比例从 72.0%（77/107）增加到 91.6%（98/107）。
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。
深度学习模型将 TI 扫描图像校正到 LGE 成像的最佳零参考点。
通过使用智能手机在监视器上捕获 TI 扫描图像，可以立即确定 TI 偏离零参考点的程度。
使用该模型可以将 TI 零参考点设置到与有经验的放射技师相同的程度。
