  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.
