Compressed Sensing MRI reconstructs images of the body's internal anatomy from undersampled measurements, thereby reducing the scan time - the time subjects need to remain still. Recently, deep neural networks have shown great potential for reconstructing high-fidelity images from highly undersampled measurements in the frequency space. However, one needs to train multiple models for different undersampling patterns and desired output image resolutions, since most networks operate on a fixed discretization. Such approaches are highly impractical in clinical settings, where undersampling patterns and image resolutions are frequently changed to accommodate different real-time imaging and diagnostic requirements.
View on arXiv@article{jatyani2025_2410.16290,
title={ A Unified Model for Compressed Sensing MRI Across Undersampling Patterns },
author={ Armeet Singh Jatyani and Jiayun Wang and Aditi Chandrashekar and Zihui Wu and Miguel Liu-Schiaffini and Bahareh Tolooshams and Anima Anandkumar },
journal={arXiv preprint arXiv:2410.16290},
year={ 2025 }
}