{"ID":2868002,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16846","arxiv_id":"2509.16846","title":"Learning Scan-Adaptive MRI Undersampling Patterns with Pre-Optimized Mask Supervision","abstract":"Deep learning techniques have gained considerable attention for their ability to accelerate MRI data acquisition while maintaining scan quality. In this work, we present a convolutional neural network (CNN) based framework for learning undersampling patterns directly from multi-coil MRI data. Unlike prior approaches that rely on in-training mask optimization, our method is trained with precomputed scan-adaptive optimized masks as supervised labels, enabling efficient and robust scan-specific sampling. The training procedure alternates between optimizing a reconstructor and a data-driven sampling network, which generates scan-specific sampling patterns from observed low-frequency $k$-space data. Experiments on the fastMRI multi-coil knee dataset demonstrate significant improvements in sampling efficiency and image reconstruction quality, providing a robust framework for enhancing MRI acquisition through deep learning.","short_abstract":"Deep learning techniques have gained considerable attention for their ability to accelerate MRI data acquisition while maintaining scan quality. In this work, we present a convolutional neural network (CNN) based framework for learning undersampling patterns directly from multi-coil MRI data. Unlike prior approaches th...","url_abs":"https://arxiv.org/abs/2509.16846","url_pdf":"https://arxiv.org/pdf/2509.16846v1","authors":"[\"Aryan Dhar\",\"Siddhant Gautam\",\"Saiprasad Ravishankar\"]","published":"2025-09-21T00:05:03Z","proceeding":"eess.IV","tasks":"[\"eess.IV\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}