基于深度学习增强的低剂量 18F-氟脱氧葡萄糖正电子发射断层扫描/磁共振成像的初步经验。
Initial Experience With Low-Dose 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging With Deep Learning Enhancement.
机构信息
From the Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI.
出版信息
J Comput Assist Tomogr. 2021;45(4):637-642. doi: 10.1097/RCT.0000000000001174.
OBJECTIVE
To demonstrate the utility of deep learning enhancement (DLE) to achieve diagnostic quality low-dose positron emission tomography (PET)/magnetic resonance (MR) imaging.
METHODS
Twenty subjects with known Crohn disease underwent simultaneous PET/MR imaging after intravenous administration of approximately 185 MBq of 18F-fluorodeoxyglucose (FDG). Five image sets were generated: (1) standard-of-care (reference), (2) low-dose (ie, using 20% of PET counts), (3) DLE-enhanced low-dose using PET data as input, (4) DLE-enhanced low-dose using PET and MR data as input, and (5) DLE-enhanced using no PET data input. Image sets were evaluated by both quantitative metrics and qualitatively by expert readers.
RESULTS
Although low-dose images (series 2) and images with no PET data input (series 5) were nondiagnostic, DLE of the low-dose images (series 3 and 4) achieved diagnostic quality images that scored more favorably than reference (series 1), both qualitatively and quantitatively.
CONCLUSIONS
Deep learning enhancement has the potential to enable a 90% reduction of radiotracer while achieving diagnostic quality images.
目的
展示深度学习增强（DLE）在实现诊断质量的低剂量正电子发射断层扫描（PET）/磁共振（MR）成像中的效用。
方法
20 名已知患有克罗恩病的患者在静脉注射约 185MBq 的 18F-氟脱氧葡萄糖（FDG）后接受了同时 PET/MR 成像。生成了五组图像：（1）标准护理（参考），（2）低剂量（即，使用 20%的 PET 计数），（3）使用 PET 数据作为输入的 DLE 增强低剂量，（4）使用 PET 和 MR 数据作为输入的 DLE 增强低剂量，以及（5）使用无 PET 数据输入的 DLE 增强。通过定量指标和专家读者的定性评估对图像集进行了评估。
结果
尽管低剂量图像（系列 2）和无 PET 数据输入的图像（系列 5）无法诊断，但低剂量图像的 DLE（系列 3 和 4）实现了诊断质量的图像，无论是在定性还是定量方面，都比参考（系列 1）更有利。
结论
深度学习增强有可能实现放射性示踪剂减少 90%，同时获得诊断质量的图像。
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