| Abstract: |
| The inverse problem of accelerated Magnetic Resonance Imaging (MRI) reconstruction can be solved through Bayesian estimation, where high-quality images are recovered from undersampled measurements in the frequency domain (k-space). Deep learning has driven remarkable progress through iterative optimization schemes, effectively solving the inverse problem end-to-end from acquisition to analysis. Downstream analysis tasks such as quantitative parameter map estimation and segmentation depend directly on reconstruction quality, coupling inverse and inference problems. Existing frameworks typically address these tasks independently, breaking the acquisition and analysis chain and limiting generalization. We present the Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC), an open-source toolbox that unifies AI-driven MRI reconstruction and analysis by natively supporting multitask learning. ATOMMIC enables end-to-end optimization from k-space to task-specific outputs by implementing over 25 deep learning models with harmonized complex-valued and real-valued data support, standardized workflows, and diverse undersampling schemes. Extensive reproducible benchmarks across eight public datasets demonstrate that physics-based models excel at highly accelerated reconstruction and that multitask learning consistently improves performance on both reconstruction and downstream tasks compared to sequential single-task approaches. ATOMMIC provides a comprehensive framework for integrating and evaluating datasets, models, and tasks, and is open-sourced under Apache 2.0 license at \url{https://github.com/wdika/atommic}. |
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