{"ID":2885527,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.04051","arxiv_id":"2508.04051","title":"Towards Globally Predictable k-Space Interpolation: A White-box Transformer Approach","abstract":"Interpolating missing data in k-space is essential for accelerating imaging. However, existing methods, including convolutional neural network-based deep learning, primarily exploit local predictability while overlooking the inherent global dependencies in k-space. Recently, Transformers have demonstrated remarkable success in natural language processing and image analysis due to their ability to capture long-range dependencies. This inspires the use of Transformers for k-space interpolation to better exploit its global structure. However, their lack of interpretability raises concerns regarding the reliability of interpolated data. To address this limitation, we propose GPI-WT, a white-box Transformer framework based on Globally Predictable Interpolation (GPI) for k-space. Specifically, we formulate GPI from the perspective of annihilation as a novel k-space structured low-rank (SLR) model. The global annihilation filters in the SLR model are treated as learnable parameters, and the subgradients of the SLR model naturally induce a learnable attention mechanism. By unfolding the subgradient-based optimization algorithm of SLR into a cascaded network, we construct the first white-box Transformer specifically designed for accelerated MRI. Experimental results demonstrate that the proposed method significantly outperforms state-of-the-art approaches in k-space interpolation accuracy while providing superior interpretability.","short_abstract":"Interpolating missing data in k-space is essential for accelerating imaging. However, existing methods, including convolutional neural network-based deep learning, primarily exploit local predictability while overlooking the inherent global dependencies in k-space. Recently, Transformers have demonstrated remarkable su...","url_abs":"https://arxiv.org/abs/2508.04051","url_pdf":"https://arxiv.org/pdf/2508.04051v1","authors":"[\"Chen Luo\",\"Qiyu Jin\",\"Taofeng Xie\",\"Xuemei Wang\",\"Huayu Wang\",\"Congcong Liu\",\"Liming Tang\",\"Guoqing Chen\",\"Zhuo-Xu Cui\",\"Dong Liang\"]","published":"2025-08-06T03:24:28Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"math.OC\"]","methods":"[\"Transformer\"]","has_code":false}