arXiv 2412.065992024-12-10eess.IVcs.CVphysics.med-ph

基于自动失真识别技术的无参考医学图像质量评估方法：在MRI引导放疗预处理中的应用

A No-Reference Medical Image Quality Assessment Method Based on Automated Distortion Recognition Technology: Application to Preprocessing in MRI-guided Radiotherapy

Beijing University of Posts and Telecommunications（北京邮电大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家癌症中心/国家癌症临床医学研究中心/肿瘤医院）

Zilin Wang, Shengqi Chen, Jianrong Dai, Shirui Qin, Ying Cao, Ruiao Zhao, Guohua Wu, Yuan Tang, Jiayun Chen

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AI总结：

提出一种基于自动失真识别的无参考MR图像质量评估模型，通过MSCN、AGGD与K-means计算QI，验证其可显著评价预处理增益并提升MRIgRT肿瘤跟踪精度。

摘要：

目的：开发一种利用自动失真识别的无参考图像质量评估方法，以提高MRI引导放疗的精度。方法：我们分析了使用Elekta Unity MR-LINAC采集的、来自10例肝转移患者的106,000张MR图像。我们的无参考质量评估模型包括：1）图像预处理，以增强关键诊断特征的可见性；2）使用四个方向上的MSCN系数进行特征提取和方向分析，以捕捉纹理属性和梯度，这对识别图像特征和潜在失真至关重要；3）综合质量指数（QI）计算，通过AGGD参数估计和K-means聚类整合特征。QI基于方向得分的加权MAD计算，提供全面的图像质量度量，对异常值具有稳健性。采用LOO-CV评估模型的泛化能力和性能。比较肿瘤跟踪算法在有预处理和无预处理情况下的性能，以验证跟踪准确性的提升。结果：预处理显著改善了图像质量，QI呈现显著正向变化并优于其他指标。归一化后，QI的平均值是CNR的79.6倍，表明图像清晰度和对比度得到改善。它在细节识别方面也表现出更高的灵敏度，平均值分别为Tenengrad梯度和熵的6.5倍和1.7倍。肿瘤跟踪算法证实，使用预处理图像后跟踪准确性显著提高，验证了预处理的有效性。结论：本研究提出了一种基于自动失真识别的新型无参考图像质量评估方法，为MRIgRT肿瘤跟踪提供了新的质量控制工具。它提高了临床应用准确性，并促进医学图像质量评估的标准化，具有重要的临床和研究价值。

英文摘要：

Objective:To develop a no-reference image quality assessment method using automated distortion recognition to boost MRI-guided radiotherapy precision.Methods:We analyzed 106,000 MR images from 10 patients with liver metastasis,captured with the Elekta Unity MR-LINAC.Our No-Reference Quality Assessment Model includes:1)image preprocessing to enhance visibility of key diagnostic features;2)feature extraction and directional analysis using MSCN coefficients across four directions to capture textural attributes and gradients,vital for identifying image features and potential distortions;3)integrative Quality Index(QI)calculation,which integrates features via AGGD parameter estimation and K-means clustering.The QI,based on a weighted MAD computation of directional scores,provides a comprehensive image quality measure,robust against outliers.LOO-CV assessed model generalizability and performance.Tumor tracking algorithm performance was compared with and without preprocessing to verify tracking accuracy enhancements.Results:Preprocessing significantly improved image quality,with the QI showing substantial positive changes and surpassing other metrics.After normalization,the QI's average value was 79.6 times higher than CNR,indicating improved image definition and contrast.It also showed higher sensitivity in detail recognition with average values 6.5 times and 1.7 times higher than Tenengrad gradient and entropy.The tumor tracking algorithm confirmed significant tracking accuracy improvements with preprocessed images,validating preprocessing effectiveness.Conclusions:This study introduces a novel no-reference image quality evaluation method based on automated distortion recognition,offering a new quality control tool for MRIgRT tumor tracking.It enhances clinical application accuracy and facilitates medical image quality assessment standardization, with significant clinical and research value.

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