Model Based Deep Learning Framework for Ultra-High Resolution Multi-Contrast MRI

基于模型的超高分辨率多对比 MRI 深度学习框架

• 批准号: 10534737
• 负责人: Mathews Jacob
• 金额: $ 69.9万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10534737
• 关键词: 3-Dimensional; Acceleration; Affect; Age; Algorithms; Alzheimer' s Disease; Anatomy; Area; Atrophic; Biological Assay; Biological Markers; Brain; Brain Mapping; Brain region; Cadaver; Clinical; Clinical Protocols; Compensation; Data; Data Set; Dependence; Development; Disease; Disease Progression; Early Diagnosis; Elderly; Evolution; Goals; Graph; Hippocampus; Human; Image; Joint repair; Joints; Learning; Life Expectancy; Machine Learning; Magnetic Resonance Imaging; Manuals; Maps; Measures; Methods; Modeling; Morphologic artifacts; Motion; Mutation; Neurodegenerative Disorders; Patients; Performance; Pharmaceutical Preparations; Phase; Positron-Emission Tomography; Protocols documentation; Public Health; Radial; Radiation exposure; Recovery; Reproducibility; Resolution; Sampling; Scanning; Scheme; Screening procedure; Shapes; Thick; Time; Training; Translating; Treatment Efficacy; Validation; Variant; brain magnetic resonance imaging; brain shape; cerebral atrophy; cognitive testing; convolutional neural network; data analysis pipeline; deep learning algorithm; deep learning model; direct application; entorhinal cortex; follow-up; high risk; high risk population; imaging biomarker; improved; in vivo; innovation; meter; mild cognitive impairment; nervous system disorder; neuropathology; novel; pre-clinical; reconstruction; respiratory; screening; segmentation algorithm; sex; treatment response; ultra high resolution

Sensitive imaging biomarkers are urgently needed for screening of high‐risk subjects, determine early disease progression, and assess response to therapies in neurodegenerative disorders. The atrophy of several brain regions is an established biomarker in AD, which strongly correlates with AD neuropathology. The accuracy of subfield volumes and cortical thickness estimated from current MRI methods is limited because of the vulnerability to motion, low spatial resolution, low contrast between brain sub‐structures, and dependence of current segmentation frameworks on image quality. Short motion‐compensated MRI protocols to map the human brain at high spatial resolution with multiple contrasts, along with accurate and computationally efficient segmentation algorithms, are urgently needed tor early detection and management of subjects with neurodegenerative disorders. We propose to introduce a 15‐minute motion‐robust 3‐D acquisition and reconstruction scheme to recover whole‐brain MRI data with 0.2 mm isotropic resolution with several different inversion times on 7T, along with segmentation algorithms that are robust to acceleration. The key difference of this framework from current approaches, which rely on MRI data 1 mm resolution, is the quite significant increase in spatial resolution to 0.2 mm as well as the availability of multiple conteasts. This improvement is enabled by innovations in all areas of the data‐processing pipeline, including acquisition, reconstruction, and analysis. These innovations are facilitated and integrated by the model based deep learning framework (MoDL); this framework facilitates the joint exploitation the available prior information, including motion and models for magnetization evolution, with convolutional neural network blocks that learn anatomical information from exemplar data. The successful completion of this framework will yield sensitive biomarkers, which will be considerably less expensive than PET and does not involve radiation exposure. As 7T clinical scanners become more common, this framework can emerge as a screening tool for high‐risk subjects (e.g. APOE, PSEN mutations) and assess progression in patients with short follow‐up duration.

迫切需要敏感的成像生物标志物来筛查高危受试者，确定 早期疾病进展，并评估神经退行性疾病治疗的反应。 几个大脑区域的萎缩是 AD 的一个既定生物标志物，它强烈 与 AD 神经病理学相关。 由于运动的脆弱性、低 空间分辨率、大脑亚结构之间的低对比度以及电流依赖性 图像质量分割框架。用于映射的短运动补偿 MRI 协议。 人类大脑以高空间分辨率、多重对比以及准确和 迫切需要计算高效的分割算法来进行早期检测和 患有神经退行性疾病的受试者的管理。 我们建议引入 15 分钟的运动稳健 3D 采集和重建 方案以 0.2 mm 各向同性分辨率恢复全脑 MRI 数据 7T 上的不同反转时间，以及稳健的分段算法 该框架与当前依赖 MRI 的方法的主要区别在于。 数据分辨率为 1 毫米，空间分辨率显着提高至 0.2 毫米以及 多种竞赛的可用性是通过各个领域的创新实现的。 数据处理流程，包括采集、重建和分析。 基于模型的深度学习框架促进和整合创新 （MoDL）；该框架有助于联合利用现有的先验信息， 包括磁化演化的运动和模型，以及卷积神经网络 从示例数据中学习解剖信息的块成功完成了这一任务。 框架将产生敏感的生物标志物，这将比 PET 便宜得多 并且不涉及辐射暴露，随着 7T 临床扫描仪变得越来越普遍，这 框架可以作为高风险受试者（例如 APOE、PSEN 突变）的筛查工具 并评估短期随访患者的进展情况。