Abstract
AI is revolutionizing MRI along the acquisition and processing chain. Advanced AI frameworks have been developed to apply AI in various successive tasks, such as image reconstruction, quantitative parameter map estimation, and image segmentation. Existing frameworks are often designed to perform tasks independently or are focused on specific models or datasets, limiting generalization. We introduce ATOMMIC, an open-source toolbox that streamlines AI applications for accelerated MRI reconstruction and analysis. ATOMMIC implements several tasks using DL networks and enables MultiTask Learning (MTL) to perform related tasks integrated, targeting generalization in the MRI domain. We first review the current state of AI frameworks for MRI through a comprehensive literature search and by parsing 12,479 GitHub repositories. We benchmark 25 DL models on eight publicly available datasets to present distinct applications of ATOMMIC on accelerated MRI reconstruction, image segmentation, quantitative parameter map estimation, and joint accelerated MRI reconstruction and image segmentation utilizing MTL. Our findings demonstrate that ATOMMIC is the only MTL framework with harmonized complex-valued and real-valued data support. Evaluations on single tasks show that physics-based models, which enforce data consistency by leveraging the physical properties of MRI, outperform other models in reconstructing highly accelerated acquisitions. Physics-based models that produce high reconstruction quality can accurately estimate quantitative parameter maps. When high-performing reconstruction models are combined with robust segmentation networks utilizing MTL, performance is improved in both tasks. ATOMMIC facilitates MRI reconstruction and analysis by standardizing workflows, enhancing data interoperability, integrating unique features like MTL, and effectively benchmarking DL models.
Abstract (translated)
人工智能正在改变MRI获取和处理链。已经开发了高级AI框架，用于在各种连续的任务中应用AI，如图像重建、定量参数图估计和图像分割。现有的框架通常被设计为独立执行任务或专注于特定的模型或数据集，从而限制了其泛化能力。我们介绍了一个名为ATOMMIC的开源工具箱，用于加速MRI重建和分析，该工具箱使用DL网络来执行多个任务，并使多任务学习（MTL）能够集成相关任务，以提高在MRI领域的泛化能力。我们首先通过全面的文献搜索回顾了AI框架在MRI领域的现状，并通过解析12,479个GitHub仓库，对25个DL模型在8个公开可用的数据集上的性能进行了基准测试，以展示ATOMMIC在加速MRI重建、图像分割、定量参数图估计和联合加速MRI重建和图像分割方面的应用。我们的研究结果表明，ATOMMIC是唯一一个支持标准化复杂值和实值数据的MTL框架。在单任务评估中，利用MRI的物理特性来确保数据一致性的物理基础模型在其他模型的重建中优于其他模型。具有高重建质量的物理基础模型可以准确估计定量参数图。当高性能重建模型与使用MTL的鲁棒分割网络结合时，性能在两个任务上都得到了提高。ATOMMIC通过标准化工作流程、增强数据互操作性、集成独特的MTL功能和有效基准测试DL模型，促进了MRI重建和分析。
URL
https://arxiv.org/abs/2404.19665
