Higher Order Convolutional Neural Network for Classification of Lewy-body Diseases and Alzheimers Disease

项目摘要 路易体痴呆（DLB）、帕金森病（PD）和阿尔茨海默病（AD）是最常见的痴呆症。 使所有国家和所有民族的患者衰弱的神经退行性疾病。之一 阿尔茨海默病相关痴呆症（ADRD）的国家研究重点是开发和验证成像 技术，以提高DLB与其他疾病的鉴别诊断准确性。磁共振 成像（MRI）是目前用于检测神经系统疾病的最广泛使用的诊断成像技术之一。 退行性疾病然而，标准T1和T2-MRI可能无法提供所需的灵敏度和特异性 用于DLB与AD和PD的鉴别诊断。最近，扩散MRI（dMRI），特别是扩散张量 磁共振成像（DTI），表现出更好的灵敏度检测这些疾病的一些。然而，已知DTI 因为它无法科普大脑中普遍存在的复杂纤维几何形状。然而，这一限制可能会超过- 通过使用复杂的数学模型结合高角分辨率扩散成像 （哈迪）。我们的初步数据表明，从HARDI学到的微观结构特征导致高 在区分PD与对照和其他文献中的敏感性和特异性方面， 使用来自T1-MRI的宏观结构特征在AD的不同阶段之间进行比较。这促使 我们通过多模态方法将联合收割机的微观和宏观结构特征结合起来，以区分DLB vs. PD、AD和对照。由于可能存在重叠，因此区分DLB、PD和AD具有挑战性 临床症状不明显导致误诊。此外，区分它们非常重要，因为 包括咨询在内的治疗方法各不相同。我们提出了一种多模式的方法， T1和弥散MRI的优势，以实现这一目标。最近，卷积神经网络（CNN） 在计算机视觉和医学成像的图像分类任务中取得了巨大的成功。CNN然而 无法科普HARDI数据的原生形式，这些数据是定义在非欧几里德上的函数样本 （弯曲）域。这促使我们开发一种新的高阶CNN，它是一种参数有效的， 可预测的几何深度学习网络，具有改进的模型容量，我们称之为VolterraNet。 VolterraNet将针对此类数据进行设计，旨在促进DLB、PD和AD的分类 组此外，VolterraNet将自动定位大脑中具有显著歧视性的区域 这些患者群体。我们将在HARDI扫描上测试VolterraNet，该扫描是从356名对照组、355名 PD、216 DLB和240 AD扫描从包括PDBP、1 Florida-ADRC和 PPMI。VolterraNet将使用标准的Leave-k-out交叉验证方法进行验证，精度为 召回措施。使用的金标准将是来自贡献研究的专家指定的临床诊断。 ies（例如，来自ADRC的共识分配）。VolterraNet将对神经病学产生重大贝内 通过更好地检测和诊断几种神经退行性疾病。

PROJECT SUMMARY Dementia with Lewy body (DLB), Parkinsons Disease (PD) and Alzheimers Disease (AD) are among the most debilitating neurodegenerative disorders that afflict patients in all countries and of all nationalities. One of the Alzheimer related dementias (ADRD) national research priorities for DLB is to develop and validate imaging techniques to improve the differential diagnostic accuracy of DLB versus other diseases. Magnetic Resonance Imaging (MRI) is currently one of the most widely used diagnostic imaging techniques for detection of neuro- degenerative disorders. However, standard T1 and T2-MRI may not provide the needed sensitivity and specificity for differential diagnosis of DLB vs. AD and PD. Recently, Diffusion MRI (dMRI), specifically Diffusion Tensor Imaging (DTI), has exhibited better sensitivity to the detection of some of these disorders. However, DTI is known for its inability to cope with complex fiber geometries prevalent in the brain. This limitation can however be over- come by using sophisticated mathematical models in conjunction with high angular resolution diffusion imaging (HARDI). Our preliminary data suggests that learned micro-structural features from HARDI lead to high sensitivity and specificity in differentiating PD vs. control and others in literature have shown discrimina- tion between different stages of AD using macro-structural features derived from T1-MRI. This motivates us to combine micro- and macro-structural features via a multi-modality approach to differentiate DLB vs. PD, AD and controls. Differentiating between DLB, PD and AD is challenging because of possible overlap in clinical symptoms leading to misdiagnosis. Further, differentiating between them is of high significance since treatments including counseling for each are distinct. We propose a multi-modal approach that combines the advantages of T1- and diffusion-MRI to achieve this goal. Recently, convolutional neural nets (CNNs) have had great success in image classification tasks in computer vision and medical imaging. CNNs however can not cope with HARDI data in its native form, which are samples of functions defined on non-Euclidean (curved) domains. This motivates us to develop a novel higher order CNN that is a parameter efficient, inter- pretable geometric deep learning network possessing improved model capacity, which we call the VolterraNet. The VolterraNet will be designed for such data with the goal of facilitating the classification of DLB, PD and AD groups. Further, VolterraNet will automatically localize the regions in the brain that are significantly discriminatory of these patient groups. We will test the VolterraNet on HARDI scans acquired from a cohort of 356 Controls, 355 PD, 216 DLB and 240 AD scans obtained from a medley of data sites including the PDBP, 1Florida-ADRC and PPMI. The VolterraNet will be validated using the standard leave-k-out cross-validation method with the precision recall measure. The gold standard used will be the specialist-assigned clinical diagnosis from contributing stud- ies (e.g. consensus assignment from ADRCs). The VolterraNet will have significant benefits to the Neurology community through better detection and diagnosis of several neurodegenerative disorders.