PDF(Pubmed)
Abstract：
Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The \'CMRxRecon\' dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.
摘要：
心脏磁共振成像（CMR）已成为心脏病的有价值的诊断工具。然而,CMR的一个显著缺点是成像速度慢，导致患者吞吐量低，临床诊断质量受损。有限的时间分辨率还会导致患者不适，并在图像中引入伪影。进一步降低其整体质量和诊断价值。人们对基于深度学习的CMR成像算法越来越感兴趣，该算法可以从高度欠采样的k空间数据中重建高质量的图像。然而,深度学习方法的发展需要大量的训练数据集，到目前为止，还没有公开提供给CMR。为了解决这个差距，我们发布了一个包含多对比度的数据集，多视图，来自300名受试者的多层和多线圈CMR成像数据。成像研究包括心脏电影和标测序列。“CMRxRecon”数据集包含原始k空间数据和自动校准线。我们的目标是通过引入标准化的评估标准并使研究社区可以自由访问数据集，从而促进最先进的CMR图像重建的进步。
