基于U-net的脑脊液分布预测与脑室反流分级
U-net based prediction of cerebrospinal fluid distribution and ventricular reflux grading
AI总结：
本研究提出基于U-net的监督学习模型，利用注射后早期MRI数据预测脑脊液示踪剂分布，证明仅用前两小时影像即可达到与使用后期扫描相当的预测性能，并与专家脑室反流分级一致，为减少MR成像、降低成本提供了依据。
摘要：
既往研究表明，脑脊液（CSF）在大脑废物清除过程中发挥着至关重要的作用，且其流动模式的改变与中枢神经系统的多种疾病相关。本研究探讨了深度学习在预测经鞘内注射的钆基脑脊液造影剂（示踪剂）在人脑中的分布方面的潜力。为此，我们利用了注射前后多个时间点采集的T1加权磁共振成像（MRI）扫描。我们提出了一种基于U-net的监督学习模型，用于预测24小时后示踪剂达到峰值时的像素级信号增强。模型性能根据训练期间提供的不同示踪剂分布阶段进行评估，包括基于注射前基线扫描的预测。我们的研究结果表明，仅使用注射后最初两小时的成像数据进行训练，所得到的示踪剂流动预测效果与使用额外后期扫描训练的模型相当。与神经放射科医生的脑室反流分级进行的验证证实了模型预测与专家评估的一致性。这些结果表明，基于深度学习的脑脊液流动预测方法值得更多关注，因为在不对临床分析造成影响的前提下减少MR成像次数，可以提高效率、改善患者福祉并降低医疗成本。
英文摘要：
Previous work indicates evidence that cerebrospinal fluid (CSF) plays a crucial role in brain waste clearance processes, and that altered flow patterns are associated with various diseases of the central nervous system. In this study, we investigate the potential of deep learning to predict the distribution in human brain of a gadolinium-based CSF contrast agent (tracer) administered intrathecal. For this, T1-weighted magnetic resonance imaging (MRI) scans taken at multiple time points before and after injection were utilized. We propose a U-net-based supervised learning model to predict pixel-wise signal increase at its peak after 24 hours. Performance is evaluated based on different tracer distribution stages provided during training, including predictions from baseline scans taken before injection. Our findings show that training with imaging data from only the first two hours post-injection yields tracer flow predictions comparable to models trained with additional later-stage scans. Validation against ventricular reflux gradings from neuroradiologists confirmed alignment with expert evaluations. These results demonstrate that deep learning-based methods for CSF flow prediction deserve more attention, as minimizing MR imaging without compromising clinical analysis could enhance efficiency, improve patient well-being, and lower healthcare costs.
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