UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction

• URL: http://arxiv.org/abs/2502.14899v1
• Date: Tue, 18 Feb 2025 07:44:35 GMT
• Title: UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction
• Authors: Donghang Lyu, Chinmay Rao, Marius Staring, Matthias J. P. van Osch, Mariya Doneva, Hildo J. Lamb, Nicola Pezzotti,
• Abstract summary: We introduce UPCMR, a universal unrolled model designed for cardiac magnetic resonance imaging reconstruction.<n>It incorporates two kinds of learnable prompts, undersampling-specific prompt and spatial-specific prompt, and integrates them with a UNet structure in each block.<n>It highly enhances reconstructed image quality across all random sampling scenarios through an effective training strategy.
• Score: 1.2773749417703923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cardiac magnetic resonance imaging (CMR) is vital for diagnosing heart diseases, but long scan time remains a major drawback. To address this, accelerated imaging techniques have been introduced by undersampling k-space, which reduces the quality of the resulting images. Recent deep learning advancements aim to speed up scanning while preserving quality, but adapting to various sampling modes and undersampling factors remains challenging. Therefore, building a universal model is a promising direction. In this work, we introduce UPCMR, a universal unrolled model designed for CMR reconstruction. This model incorporates two kinds of learnable prompts, undersampling-specific prompt and spatial-specific prompt, and integrates them with a UNet structure in each block. Overall, by using the CMRxRecon2024 challenge dataset for training and validation, the UPCMR model highly enhances reconstructed image quality across all random sampling scenarios through an effective training strategy compared to some traditional methods, demonstrating strong adaptability potential for this task.

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