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20212026
most citedCMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

15 citations · 52 across the 21 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

eess.IV2024

Towards Ground-truth-free Evaluation of Any Segmentation in Medical Images

Ahjol Senbi, Tianyu Huang, Fei Lyu +8

We explore the feasibility and potential of building a ground-truth-free evaluation model to assess the quality of segmentations generated by the Segment Anything Model (SAM) and i…

eess.IV2024

Sampling-Pattern-Agnostic MRI Reconstruction through Adaptive Consistency Enforcement with Diffusion Model

Anurag Malyala, Zhenlin Zhang, Chengyan Wang +1

Magnetic Resonance Imaging (MRI) is a powerful, non-invasive diagnostic tool; however, its clinical applicability is constrained by prolonged acquisition times. Whilst present deep…

eess.IV2024

An Empirical Study on the Fairness of Foundation Models for Multi-Organ Image Segmentation

Qin Li, Yizhe Zhang, Yan Li +11

The segmentation foundation model, e.g., Segment Anything Model (SAM), has attracted increasing interest in the medical image community. Early pioneering studies primarily concentr…

eess.IV2024★ 1 cited

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

Zi Wang, Fanwen Wang, Chen Qin +29

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 w…

eess.IV2024

Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI

Yirong Zhou, Chengyan Wang, Mengtian Lu +17

In cardiac Magnetic Resonance Imaging (MRI) analysis, simultaneous myocardial segmentation and T2 quantification are crucial for assessing myocardial pathologies. Existing methods…

eess.IV2024★ 2 cited

The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

Jun Lyu, Chen Qin, Shuo Wang +47

Cardiac MRI, crucial for evaluating heart structure and function, faces limitations like slow imaging and motion artifacts. Undersampling reconstruction, especially data-driven alg…

eess.IV2024★ 2 cited

Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI

Zi Wang, Min Xiao, Yirong Zhou +13

Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction…