UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction
This work addresses accelerated cardiac MRI reconstruction for medical diagnosis, but it appears incremental as it builds on existing unrolled models with prompt integration.
The paper tackles the problem of long scan times in cardiac MRI by proposing UPCMR, a universal deep learning model for reconstructing images from undersampled data, which enhances image quality across various random sampling scenarios as validated on the CMRxRecon2024 dataset.
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.