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

TL;DR

This paper introduces UPCMR, a universal deep learning model with learnable prompts for improved cardiac MRI reconstruction across various sampling modes, significantly enhancing image quality and adaptability.

Contribution

The paper presents a novel universal unrolled model with learnable prompts that adapt to different sampling patterns in cardiac MRI reconstruction.

Findings

Significantly improves image quality across all sampling scenarios.

Demonstrates strong adaptability and generalization in CMR reconstruction.

Outperforms traditional methods on the CMRxRecon2024 dataset.

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.