背景:黑体1(N1),黑质腹侧区最大的黑体区,通过长回波时间梯度回波MRI中的“N1符号”可识别。N1体征的缺失是帕金森病(PD)的重要诊断标志。然而,在临床实践中可视化和评估N1体征具有挑战性.
目的:使用深度学习方法从真实磁敏感加权成像中自动检测N1体征的存在或不存在。
方法:前瞻性。
方法:453名受试者,包括225名PD患者,120个健康对照(HCs),和108名患有其他运动障碍的患者,前瞻性招募,包括227名男性和226名女性。他们分为训练,验证,以及289、73和91例的测试队列,分别。
■3T时的3D梯度回波SWI序列;3T时的3D多回波策略性采集梯度回波成像;3T时的NM敏感3D梯度回波序列,MTC脉冲。
结果:一位具有5年经验的神经放射学家手动描绘了黑质区域。两名具有2年和36年经验的评估者在真实磁敏感加权成像(tSWI)上评估了N1体征,带高通滤波器的QSM,和与MTC数据相结合的幅度数据。我们提议NINet,神经模型,用于在tSWI图像中自动识别N1标志。
方法:我们使用接收器工作特性分析将NINet的性能与主观参考标准进行了比较,和决策曲线分析评估识别准确性。
结果:NINet在N1体征识别中获得了0.87(CI:0.76-0.89)的曲线下面积(AUC),超越其他模型和神经放射学家。NINet在tSWI图像中定位了推定的N1体征,准确率为67.3%。
结论:我们提出的NINet模型能够确定N1信号的存在与否,随着它的本地化,在使用MR图像评估PD时,有望提高诊断准确性。
方法:2技术效果:第一阶段。
BACKGROUND: Nigrosome 1 (N1), the largest nigrosome region in the ventrolateral area of the substantia nigra pars compacta, is identifiable by the \"N1 sign\" in long echo time gradient echo MRI. The N1 sign\'s absence is a vital Parkinson\'s disease (PD) diagnostic marker. However, it is challenging to visualize and assess the N1 sign in clinical practice.
OBJECTIVE: To automatically detect the presence or absence of the N1 sign from true susceptibility weighted imaging by using deep-learning method.
METHODS: Prospective.
METHODS: 453 subjects, including 225 PD patients, 120 healthy controls (HCs), and 108 patients with other movement disorders, were prospectively recruited including 227 males and 226 females. They were divided into training, validation, and test cohorts of 289, 73, and 91 cases, respectively.
UNASSIGNED: 3D gradient echo SWI sequence at 3T; 3D multiecho strategically acquired gradient echo imaging at 3T; NM-sensitive 3D gradient echo sequence with MTC pulse at 3T.
RESULTS: A neuroradiologist with 5 years of experience manually delineated substantia nigra regions. Two raters with 2 and 36 years of experience assessed the N1 sign on true susceptibility weighted imaging (tSWI), QSM with high-pass filter, and magnitude data combined with MTC data. We proposed NINet, a neural model, for automatic N1 sign identification in tSWI images.
METHODS: We compared the performance of NINet to the subjective reference standard using Receiver Operating Characteristic analyses, and a decision curve analysis assessed identification accuracy.
RESULTS: NINet achieved an area under the curve (AUC) of 0.87 (CI: 0.76-0.89) in N1 sign identification, surpassing other models and neuroradiologists. NINet localized the putative N1 sign within tSWI images with 67.3% accuracy.
CONCLUSIONS: Our proposed NINet model\'s capability to determine the presence or absence of the N1 sign, along with its localization, holds promise for enhancing diagnostic accuracy when evaluating PD using MR images.
METHODS: 2 TECHNICAL EFFICACY: Stage 1.