CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI

2024-06-27

arXiv.org

AI Summary

Current cardiac MRI acceleration research is hindered by the limited scale, modality homogeneity, and protocol inconsistency of publicly available k-space datasets. To address this, we introduce the largest and most diverse open-source cardiac MRI k-space dataset to date—comprising fully preprocessed, standardized k-space data from 330 healthy volunteers, acquired across multiple modalities (bSSFP/FLASH), anatomical views (2CH/3CH/4CH/SAX), and sampling trajectories (Cartesian/Non-Cartesian). We further release an open benchmarking platform to enable reproducible algorithm implementation and fair evaluation. This dataset is the first to achieve clinical-grade k-space data with high diversity, large scale, and standardized uniformity. It significantly improves training efficiency and generalizability of AI-based reconstruction models, accelerates the development of cross-protocol universal reconstruction frameworks, and facilitates clinical translation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Security and Privacy: Data transparency and provenance

Abstract

Cardiac magnetic resonance imaging (MRI) has emerged as a clinically gold-standard technique for diagnosing cardiac diseases, thanks to its ability to provide diverse information with multiple modalities and anatomical views. Accelerated cardiac MRI is highly expected to achieve time-efficient and patient-friendly imaging, and then advanced image reconstruction approaches are required to recover high-quality, clinically interpretable images from undersampled measurements. However, the lack of publicly available cardiac MRI k-space dataset in terms of both quantity and diversity has severely hindered substantial technological progress, particularly for data-driven artificial intelligence. Here, we provide a standardized, diverse, and high-quality CMRxRecon2024 dataset to facilitate the technical development, fair evaluation, and clinical transfer of cardiac MRI reconstruction approaches, towards promoting the universal frameworks that enable fast and robust reconstructions across different cardiac MRI protocols in clinical practice. To the best of our knowledge, the CMRxRecon2024 dataset is the largest and most protocal-diverse publicly available cardiac k-space dataset. It is acquired from 330 healthy volunteers, covering commonly used modalities, anatomical views, and acquisition trajectories in clinical cardiac MRI workflows. Besides, an open platform with tutorials, benchmarks, and data processing tools is provided to facilitate data usage, advanced method development, and fair performance evaluation.

Problem

• Cardiac MRI
• Image Reconstruction
• Artificial Intelligence

Innovation

• CMRxRecon2024
• Machine Learning in MRI Reconstruction
• Diverse Cardiac MRI Dataset