Abstract：
OBJECTIVE: Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI.
METHODS: Deep learning-based detection of key brain landmarks on a whole-uterus echo planar imaging scan enables the subsequent fully automatic planning of the radiological single-shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times, and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. A comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality, and the resulting image quality was conducted.
RESULTS: Prospective automatic planning was performed in real-time without latency in all subjects. The landmark detection accuracy was 4.2 ± $$ \\pm $$ 2.6 mm for the fetal eyes and 6.5 ± $$ \\pm $$ 3.2 for the cerebellum, planning quality was 2.4/3 (compared to 2.6/3 for manual planning) and diagnostic image quality was 2.2 compared to 2.1 for manual planning.
CONCLUSIONS: Real-time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way toward simplifying the acquisition of fetal MRI thereby widening the availability of this modality in nonspecialist centers.
METHODS: Deep learning-based detection of key brain landmarks on a whole-uterus echo planar imaging scan enables the subsequent fully automatic planning of the radiological single-shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times, and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. A comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality, and the resulting image quality was conducted.
RESULTS: Prospective automatic planning was performed in real-time without latency in all subjects. The landmark detection accuracy was 4.2 ± $$ \\pm $$ 2.6 mm for the fetal eyes and 6.5 ± $$ \\pm $$ 3.2 for the cerebellum, planning quality was 2.4/3 (compared to 2.6/3 for manual planning) and diagnostic image quality was 2.2 compared to 2.1 for manual planning.
CONCLUSIONS: Real-time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way toward simplifying the acquisition of fetal MRI thereby widening the availability of this modality in nonspecialist centers.
摘要：
目的：在0.55TMRI上通过对放射学脑平面的全自动实时计划来扩大胎儿MRI的可用性。
方法：基于深度学习的全子宫回波平面成像扫描中的关键脑标志检测能够实现后续的放射学单次TurboSpin回波采集的全自动计划。地标检测管道在来自不同场强的120多个数据集上进行了训练，回声时间，和分辨率并进行定量评估。在20至37周之间的9名胎儿受试者中前瞻性地测试了整个自动计划解决方案。对所有步骤进行全面评估，手动和自动地标之间的距离，规划质量,并进行所得图像质量。
结果：在所有受试者中实时进行前瞻性自动计划，没有延迟。胎眼的界标检测精度为4.2±$$\pm$2.6mm，小脑为6.5±$\pm$3.2，规划质量为2.4/3(人工规划为2.6/3),诊断图像质量为2.2,人工规划为2.1.
结论：成功实现了所有三个关键胎儿脑平面的实时自动计划，并将为简化胎儿MRI的采集铺平道路，从而扩大了这种模式在非专科中心的可用性。
方法：基于深度学习的全子宫回波平面成像扫描中的关键脑标志检测能够实现后续的放射学单次TurboSpin回波采集的全自动计划。地标检测管道在来自不同场强的120多个数据集上进行了训练，回声时间，和分辨率并进行定量评估。在20至37周之间的9名胎儿受试者中前瞻性地测试了整个自动计划解决方案。对所有步骤进行全面评估，手动和自动地标之间的距离，规划质量,并进行所得图像质量。
结果：在所有受试者中实时进行前瞻性自动计划，没有延迟。胎眼的界标检测精度为4.2±$$\pm$2.6mm，小脑为6.5±$\pm$3.2，规划质量为2.4/3(人工规划为2.6/3),诊断图像质量为2.2,人工规划为2.1.
结论：成功实现了所有三个关键胎儿脑平面的实时自动计划，并将为简化胎儿MRI的采集铺平道路，从而扩大了这种模式在非专科中心的可用性。
