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

确定，筛选高危对象迫切需要敏感的成像生物标记物 早期疾病进展，并评估神经退行性疾病的治疗反应。 几个脑区的萎缩是AD的一个公认的生物标志物，它强烈地 与阿尔茨海默病神经病理学相关。子场体积和皮质厚度的准确性 由于运动的脆弱性，目前的MRI方法估计是有限的，低 空间分辨率、脑亚结构之间的低对比度以及电流的依赖性 分割框架对图像质量的影响。用于映射的运动补偿MRI短协议 人脑在高空间分辨率下具有多个对比度，以及准确和 计算高效的分割算法迫切需要用于早期检测和 神经退行性疾病受试者的管理。 我们建议引入15分钟的运动-稳健的3-D采集和重建 一种基于0.2 mm各向同性分辨率的全脑MRI数据恢复方案 7T上的不同反转时间，以及对 加速。该框架与当前依赖于MRI的方法的关键区别在于 数据分辨率为1毫米，是空间分辨率相当显著的提高到0.2毫米以及 多场比赛的可用性。这一改进得益于所有领域的创新 数据处理流水线，包括采集、重建和分析。这些 基于模型的深度学习框架促进和整合了创新 (MODL)；这一框架有助于联合开发可用的先验信息， 包括运动和磁化演化模型，使用卷积神经网络 从样本数据中学习解剖信息的块。成功地完成这项工作 框架将产生敏感的生物标记物，这将比PET便宜得多 而且不涉及辐射暴露。随着7T临床扫描仪变得越来越普遍，这 框架可作为高危对象(如APOE、PSEN突变)的筛查工具 并评估随访时间较短的患者的进展情况。

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.