左心房LGE-MRI质量评估与临床可用性数据集
包含60个LGE-MRI图像-文本对，用于左心房图像质量评估和临床可用性判断，由专家标注五个质量标准。
基本信息
资源简介
该数据集包含60个LGE-MRI图像-文本对，来自20名左心房纤维化/房颤患者的中央切片，由专家放射科医生按噪声、运动伪影、左心房边界准确性、肺静脉区域准确性、分割不足严重性五个标准评分（0-3分），并附加临床可用性二元标签。数据用于训练视觉语言模型实现自动图像质量评估，以辅助心脏消融规划。
下载信息
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使用方式
数据集说明
左心房LGE-MRI质量评估与临床可用性数据集 对应论文数据集（arXiv 预印本）。
数据获取指引
- 打开论文页面获取作者与项目信息：https://arxiv.org/abs/2608.21180v1
- 论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
- 获取到实际数据链接后，按对应平台标准方式下载。
论文摘要：Abstract:LGE cardiac MRI is widely used for left atrial fibrosis assessment and ablation planning in atrial fibrillation patients as knowledge of fibrotic tissue regions identified from LGE-MRI is critical for catheter ablation. Often, poor quality images used during ablation planning can cause mis-localization of ablation targets, directly impacting procedure safety and outcome. The decision of whether a scan meets the minimum quality threshold for ablation planning is currently made informally by the reviewing radiologist and is not captured by any automated system, yet it is arguably the most safety-critical output of the image quality assessment (IQA) process. However, variations in image quality caused by noise, motion artifacts, and poor boundary definition significantly compromise the reliability of downstream segmentation and clinical decision-making tasks. Manual quality assessment by expert radiologists is subjective and difficult to scale, while existing automated methods produce scalar scores without interpretable clinical reasoning. In this work, we propose a two-stage vision language model (VLM) framework for clinically grounded image quality assessment of left atrial LGE-MRI. In the first stage, a fine-tuned VLM generates structured radiology-style quality reports predicting five radiologist-defined criteria: Noise, Motion Artifact, LA Boundary Accuracy, PV Region Accuracy, and Under-segmentation Severity. In the second stage, a GPT-based reasoning module maps the predicted quality and reports to a structured quality scores and binary clinical usability decis
精度瓶颈？数据缺失？
当前公开数据无法满足您的算法精度？千方提供针对 心房颤动 的高质量、多模态真实临床数据定制解决方案。
