PDF(Pubmed)
Abstract：
In this paper, we present a cascaded deep convolution neural network (CNN) for assessing enlarged perivascular space (ePVS) within the basal ganglia region using T2-weighted MRI. Enlarged perivascular spaces (ePVSs) are potential biomarkers for various neurodegenerative disorders, including dementia and Parkinson\'s disease. Accurate assessment of ePVS is crucial for early diagnosis and monitoring disease progression. Our approach first utilizes an ePVS enhancement CNN to improve ePVS visibility and then employs a quantification CNN to predict the number of ePVSs. The ePVS enhancement CNN selectively enhances the ePVS areas without the need for additional heuristic parameters, achieving a higher contrast-to-noise ratio (CNR) of 113.77 compared to Tophat, Clahe, and Laplacian-based enhancement algorithms. The subsequent ePVS quantification CNN was trained and validated using fourfold cross-validation on a dataset of 76 participants. The quantification CNN attained 88% accuracy at the image level and 94% accuracy at the subject level. These results demonstrate significant improvements over traditional algorithm-based methods, highlighting the robustness and reliability of our deep learning approach. The proposed cascaded deep CNN model not only enhances the visibility of ePVS but also provides accurate quantification, making it a promising tool for evaluating neurodegenerative disorders. This method offers a novel and significant advancement in the non-invasive assessment of ePVS, potentially aiding in early diagnosis and targeted treatment strategies.
摘要：
在本文中,我们提出了一个级联的深度卷积神经网络(CNN),用于使用T2加权MRI评估基底神经节区域血管周围间隙(ePVS)的扩大.血管周围间隙增大（ePVSs）是各种神经退行性疾病的潜在生物标志物，包括痴呆和帕金森病。ePVS的准确评估对于早期诊断和监测疾病进展至关重要。我们的方法首先利用ePVS增强CNN来提高ePVS可见性，然后采用量化CNN来预测ePVS的数量。ePVS增强CNN选择性地增强ePVS区域，而无需额外的启发式参数，与Tophat相比，实现了113.77的更高的对比度噪声比（CNR），Clahe,和基于拉普拉斯的增强算法。随后的ePVS量化CNN在76名参与者的数据集上使用四次交叉验证进行训练和验证。量化CNN在图像水平上达到88%的准确度，在受试者水平上达到94%的准确度。这些结果表明，相对于传统的基于算法的方法，突出了我们深度学习方法的健壮性和可靠性。提出的级联深度CNN模型不仅增强了ePVS的可见性，而且提供了准确的量化，使其成为评估神经退行性疾病的有前途的工具。该方法在ePVS的非侵入性评估中提供了新颖而重大的进步，可能有助于早期诊断和有针对性的治疗策略。
