KP-INR: A Dual-Branch Implicit Neural Representation Model for Cardiac Cine MRI Reconstruction

• URL: http://arxiv.org/abs/2508.12147v1
• Date: Sat, 16 Aug 2025 20:02:14 GMT
• Title: KP-INR: A Dual-Branch Implicit Neural Representation Model for Cardiac Cine MRI Reconstruction
• Authors: Donghang Lyu, Marius Staring, Mariya Doneva, Hildo J. Lamb, Nicola Pezzotti,
• Abstract summary: Implicit Neural Representation (INR) methods have shown promise in unsupervised reconstruction by learning coordinate-to-value mappings from undersampled data.<n>We propose KP-INR, a dual-branch INR method operating in k-space for cardiac cine MRI reconstruction.
• Score: 1.4214002697449326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cardiac Magnetic Resonance (CMR) imaging is a non-invasive method for assessing cardiac structure, function, and blood flow. Cine MRI extends this by capturing heart motion, providing detailed insights into cardiac mechanics. To reduce scan time and breath-hold discomfort, fast acquisition techniques have been utilized at the cost of lowering image quality. Recently, Implicit Neural Representation (INR) methods have shown promise in unsupervised reconstruction by learning coordinate-to-value mappings from undersampled data, enabling high-quality image recovery. However, current existing INR methods primarily focus on using coordinate-based positional embeddings to learn the mapping, while overlooking the feature representations of the target point and its neighboring context. In this work, we propose KP-INR, a dual-branch INR method operating in k-space for cardiac cine MRI reconstruction: one branch processes the positional embedding of k-space coordinates, while the other learns from local multi-scale k-space feature representations at those coordinates. By enabling cross-branch interaction and approximating the target k-space values from both branches, KP-INR can achieve strong performance on challenging Cartesian k-space data. Experiments on the CMRxRecon2024 dataset confirms its improved performance over baseline models and highlights its potential in this field.

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