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风险受试者，确定 早期疾病进展，以及对神经退行性疾病疗法的评估反应。 几个大脑区域的萎缩是AD中已建立的生物标志物，它强烈 与AD神经病理学相关。子场量和皮质厚度的准确性 从当前MRI方法估计的限制是有限的，因为易于运动， 空间分辨率，大脑子结构之间的低对比度和电流的依赖性 图像质量的细分框架。简短的运动补偿MRI协议以映射 具有高空间分辨率的人类大脑，具有多个对比度，以及准确的和 迫切需要在计算高效的分割算法的早期检测和 患有神经退行性疾病的受试者的管理。 我们建议引入15分钟的运动稳定3-D采集和重建 以0.2 mm的各向同性分辨率恢复全脑MRI数据的方案，有几个 7T上的不同反转时间，以及分割算法，这些算法可靠 加速度。此框架与当前方法的关键区别，该方法依赖于MRI 数据1 mm分辨率是空间分辨率至0.2 mm的显着增加 多个conteast的可用性。在所有领域的创新都可以实现这种改进 数据处理管道，包括获取，重建和分析。这些 创新是由基于模型的深度学习框架准备和集成的 （modl）;该框架有助于联合剥削可用的先验信息， 包括磁化进化的运动和模型，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 框架将产生敏感的生物标志物，这将被认为比PET便宜 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的筛选工具（例如APOE，PSEN突变） 随访时间短的患者的评估进展。