基于自动畸变识别的无参考医学图像质量评估，提升MRI引导放疗精度。
A No-Reference Medical Image Quality Assessment Method Based on Automated Distortion Recognition Technology: Application to Preprocessing in MRI-guided Radiotherapy
- 通过多方向MSCN特征提取与AGGD参数估计，构建无参考质量指数。
- 预处理后图像质量指数提升79.6倍，细节识别能力提高6.5倍。
- 适用于MRI引导放疗中的畸变检测，适合临床质控与研究应用。
目的：开发一种基于自动化畸变识别的无参考图像质量评估方法，以提升MRI引导放疗（MRIgRT）的精度。方法：分析10例肝转移患者在Elekta Unity MR-LINAC上采集的106,000张MR图像。提出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可见性；2）利用多尺度中心化归一化（MSCN）系数在四个方向进行特征提取与方向分析，捕捉纹理属性与梯度信息，用于识别图像特征与潜在畸变；3）通过广义高斯分布（AGGD）参数估计与K-means聚类整合特征，计算综合质量指数（QI）。QI基于方向得分加权平均绝对偏差（MAD）计算，对异常值具有鲁棒性。采用留一交叉验证（LOO-CV）评估模型泛化能力。对比有无预处理下肿瘤跟踪算法性能，验证跟踪精度提升效果。结果：预处理显著改善图像质量，QI呈现显著正向变化，优于其他指标。归一化后，QI平均值为信噪比（CNR）的79.6倍，表明图像清晰度与对比度明显提升。其在细节识别上的敏感性分别达Tenengrad梯度和熵的6.5倍与1.7倍。肿瘤跟踪算法在使用预处理图像时准确率显著提高，验证了预处理的有效性。结论：本研究提出一种基于自动化畸变识别的新型无参考图像质量评估方法，为MRIgRT肿瘤追踪提供新质控工具，提升临床应用精度，推动医学图像质量评估标准化，具有重要临床与科研价值。
原文摘要 · Abstract (English)
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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