ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance Imaging

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer methods and programs in biomedicine Pub Date : 2024-08-22 DOI:10.1016/j.cmpb.2024.108377
Dimitrios Karkalousos , Ivana Išgum , Henk A. Marquering , Matthan W.A. Caan
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引用次数: 0

Abstract

Background and Objectives:

Artificial intelligence (AI) is revolutionizing Magnetic Resonance Imaging (MRI) along the acquisition and processing chain. Advanced AI frameworks have been applied in various successive tasks, such as image reconstruction, quantitative parameter map estimation, and image segmentation. However, existing frameworks are often designed to perform tasks independently of each other or are focused on specific models or single datasets, limiting generalization. This work introduces the Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC), a novel open-source toolbox that streamlines AI applications for accelerated MRI reconstruction and analysis. ATOMMIC implements several tasks using deep learning (DL) models and enables MultiTask Learning (MTL) to perform related tasks in an integrated manner, targeting generalization in the MRI domain.

Methods:

We conducted a comprehensive literature review and analyzed 12,479 GitHub repositories to assess the current landscape of AI frameworks for MRI. Subsequently, we demonstrate how ATOMMIC standardizes workflows and improves data interoperability, enabling effective benchmarking of various DL models across MRI tasks and datasets. To showcase ATOMMIC’s capabilities, we evaluated twenty-five DL models on eight publicly available datasets, focusing on accelerated MRI reconstruction, segmentation, quantitative parameter map estimation, and joint accelerated MRI reconstruction and segmentation using MTL.

Results:

ATOMMIC’s high-performance training and testing capabilities, utilizing multiple GPUs and mixed precision support, enable efficient benchmarking of multiple models across various tasks. The framework’s modular architecture implements each task through a collection of data loaders, models, loss functions, evaluation metrics, and pre-processing transformations, facilitating seamless integration of new tasks, datasets, and models. Our findings demonstrate that ATOMMIC supports MTL for multiple MRI tasks with harmonized complex-valued and real-valued data support while maintaining active development and documentation. Task-specific evaluations demonstrate that physics-based models outperform other approaches in reconstructing highly accelerated acquisitions. These high-quality reconstruction models also show superior accuracy in estimating quantitative parameter maps. Furthermore, when combining high-performing reconstruction models with robust segmentation networks through MTL, performance is improved in both tasks.

Conclusions:

ATOMMIC advances MRI reconstruction and analysis by leveraging MTL and ensuring consistency across tasks, models, and datasets. This comprehensive framework serves as a versatile platform for researchers to use existing AI methods and develop new approaches in medical imaging.

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ATOMMIC:多任务医学影像一致性高级工具箱,促进磁共振成像从采集到分析的人工智能应用
背景与目标:人工智能(AI)正在彻底改变磁共振成像(MRI)的采集和处理链。先进的人工智能框架已被应用于各种连续任务,如图像重建、定量参数图估计和图像分割。然而,现有的框架往往是为独立执行任务而设计的,或者侧重于特定模型或单一数据集,从而限制了通用性。这项工作介绍了多任务医学影像一致性高级工具箱(ATOMMIC),这是一个新颖的开源工具箱,可简化加速核磁共振成像重建和分析的人工智能应用。ATOMMIC利用深度学习(DL)模型实现了多项任务,并使多任务学习(MTL)能够以集成的方式执行相关任务,目标是在核磁共振成像领域实现通用化。方法:我们进行了全面的文献综述,并分析了12479个GitHub软件仓库,以评估当前核磁共振成像人工智能框架的现状。随后,我们展示了 ATOMMIC 如何标准化工作流程并提高数据互操作性,从而在核磁共振成像任务和数据集上对各种 DL 模型进行有效的基准测试。为了展示ATOMMIC的能力,我们在八个公开可用的数据集上评估了25个DL模型,重点是加速核磁共振成像重建、分割、定量参数图估计以及使用MTL的联合加速核磁共振成像重建和分割。该框架的模块化架构通过一系列数据加载器、模型、损失函数、评估指标和预处理转换来实现每项任务,从而促进了新任务、数据集和模型的无缝集成。我们的研究结果表明,ATOMMIC 支持多种核磁共振成像任务的 MTL,并提供统一的复值和实值数据支持,同时保持积极的开发和文档编制。针对特定任务的评估表明,基于物理的模型在重建高度加速的采集方面优于其他方法。这些高质量的重建模型在估算定量参数图方面也表现出更高的准确性。结论:ATOMMIC 利用 MTL 并确保任务、模型和数据集之间的一致性,推动了 MRI 重建和分析的发展。这个全面的框架为研究人员使用现有的人工智能方法和开发医学成像新方法提供了一个多功能平台。
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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