Deep Learning for Detecting the Early Anatomical Effects of Alzheimer's Disease

深度学习检测阿尔茨海默病的早期解剖学影响

• 批准号: 10658045
• 负责人: Bruce Fischl
• 金额: $ 19.87万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10658045
• 关键词: Accounting; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Anatomic Models; Anatomy; Atrophic; Behavior; Cell Death; Clinical; Clinical Research; Clinical Trials; Computer software; Darkness; Data; Data Set; Detection; Development; Diagnosis; Dimensions; Disease; Early Diagnosis; Ensure; Generations; Hippocampus; Image; Impaired cognition; Label; Learning; Left; Lesion; Location; Magnetic Resonance Imaging; Manuals; Maps; Modality; Modernization; Neuroanatomy; Neurologic; Noise; Picture Archiving and Communication System; Positron-Emission Tomography; Predisposition; Procedures; Process; Progressive Disease; Property; Protocols documentation; Research; Scanning; Schedule; Sensitivity and Specificity; Series; Shapes; Site; Structure; Surrogate Endpoint; Techniques; Technology; Testing; Thalamic structure; Therapeutic Intervention; Time; Training; Treatment Efficacy; Validation; Variant; Ventricular; clinical application; clinical imaging; deep learning; deep neural network; design; disease classification; follow-up; imaging modality; improved; learning network; longitudinal analysis; neuropathology; simulation; therapeutically effective; tool; usability

Project Summary Longitudinal, within-subject approaches, have the potential to increase sensitivity and specificity, improving the efficiency of clinical trials by requiring fewer subjects and providing potential surrogate endpoints to assess therapeutic efficacy. There is also great potential that these tools will enable more sophisticated anatomical modeling to better understand the temporal dynamics of progression. In Alzheimer’s Disease in particular, early detection, prior to widespread and likely irreversible cell death, is crucial for the development of effective therapeutic interventions. However, longitudinal tools have not yet been optimized for use in clinical studies. Challenges include the reduction of noise across serial scans while providing each time point equal relative weighting to avoid bias; adequately and appropriately accounting for atrophy; and handling varying MRI contrast and distortion across time. In this proposal, we seek to improve longitudinal analysis in a number of ways, leveraging the power of modern deep learning to increase accuracy, make it applicable to any type of MRI contrast, radically reduce execution time, as well as make it usable in direct clinical applications. To achieve these aims we will employ newly developed image synthesis techniques to train networks to detect small, “true” anatomical change hidden within a set of large-scale “MRI” distortions, that will capture longitudinal differences in image acquisition such as gradient nonlinearities, field strength and B0 distortions, and sequence parameter variations. The change-detection network will be cascaded with a deep registration network that will learn to decompose the temporal warp into uninteresting MRI distortions and interesting anatomical effects, then both warp fields and the aligned images will be provided to a segmentation network to ensure no information is lost by the registration. The networks will learn to ignore MRI effects based on their stereotypical behavior (e.g. the one-dimensionality of B0 distortions, the spatial smoothness of gradient nonlinearities) and to detect the subtle anatomical changes such as increasing ventricular size or decreasing hippocampal volume. The result will be a set of robust contrast-and-distortion-agnostic tools that highlight potential disease effects for clinicians.

项目总结