Search Results for author:
Found 2 papers, 0 papers with code
A No-Reference Medical Image Quality Assessment Method Based on Automated Distortion Recognition Technology: Application to Preprocessing in MRI-guided Radiotherapy
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.
A Novel Automatic Real-time Motion Tracking Method for Magnetic Resonance Imaging-guided Radiotherapy: Leveraging the Enhanced Tracking-Learning-Detection Framework with Automatic Segmentation
Moreover, the ETLD+ICV yielded a dice global score of more than 82% for all subjects, demonstrating the proposed method's extensibility and precise target volume coverage.
