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

项目总结 路易体痴呆(DLB)、帕金森氏病(PD)和阿尔茨海默病(AD)是最常见的痴呆症 所有国家和所有民族的flICT患者都患有衰弱的神经退行性疾病。其中一个 阿尔茨海默病相关痴呆(ADRD)DLB的国家研究重点是开发和验证成像 提高DLB与其他疾病鉴别诊断准确率的技术。磁共振 磁共振成像(MRI)是目前应用最广泛的神经病变诊断成像技术之一。 退化性疾病。然而，标准的T1和T2-MRI可能不能提供所需的敏感性和特异性fiCity 用于鉴别诊断DLB与AD和PD。最近，弥散磁共振成像(DMRI)，特别是fi的弥散张量 成像(DTI)对其中一些疾病的检测显示出更高的敏感性。然而，DTI是已知的 因为它无法处理大脑中普遍存在的复杂的fi-ber几何结构。然而，这一限制可能会超过- 通过使用复杂的数学模型和高角度分辨率的扩散成像 (哈迪)。我们的初步数据表明，从哈迪那里学到的微结构特征导致了高 敏感度和特异度fi城市在区分PD和对照组以及文献中的其他方面显示出区别。 使用来自T1-MRI的宏观结构特征来区分AD的不同阶段。这会激励人们 美国将通过多通道方法将微观和宏观结构特征结合起来，以区分DLB 与PD、AD和对照组比较。由于可能存在重叠，区分DLB、PD和AD具有挑战性 在临床症状上导致误诊。此外，区分它们具有非常重要的意义，因为fi 包括咨询在内的每种治疗方法都是不同的。我们提出了一种结合了多模式的方法 T1-MRI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领域的图像分类fi任务中取得了巨大的成功。然而，CNN 无法处理原生形式的HARDI数据，这些数据是非欧几里得上的函数DefiNed的样本 (弯曲的)域。这促使我们开发出一种新颖的高阶CNN，它是一种参数有效的、相互关联的网络。 可预测的几何深度学习网络具有改进的模型能力，我们称之为VolterraNet。 VolterraNet将为此类数据设计，目标是促进DLb、PD和AD的Classifi阳离子 组。此外，VolterraNet将自动定位大脑中具有明显fi歧视性的区域 在这些患者群体中。我们将在Hardi扫描上测试VolterraNet，这些扫描是从356个控制组、355个控制组中获得的 PD、216个DLB和240个AD扫描，这些扫描来自包括PDBP、1佛罗里达-ADRC和 PPMI。VolterraNet将使用标准的留k-out交叉验证方法进行验证，其精度为 召回措施。使用的黄金标准将是来自贡献研究的专家指定的临床诊断- 独立专家委员会(例如，ADRC的协商一致分配)。VolterraNet将对神经病学产生重大影响 社区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.