Deep learning-based CNN method for fiducial marker detection in kilovoltage X-ray images for liver tumor motion monitoring.

چکیده اصلی

BACKGROUND: During radiation therapy, liver tumor motion can reduce dose delivery accuracy and increase irradiation of adjacent healthy tissues. Because liver tumors are difficult to visualize directly on kilovoltage (kV) X-ray images, fiducial markers are commonly implanted as surrogates for tumor position during treatment. Conventional marker-segmentation approaches such as template matching can lose accuracy when markers are obscured by bone, surgical clips, or stents. Although Convolutional Neural Network (CNN)-based approaches have shown strong performance for image analysis, their application has been constrained to more static organs such as the prostate or has used single-center and vendor-specific datasets, limiting assessment of model transportability across different institutions and anatomic sites. PURPOSE: The aim of this study was to develop, integrate, and evaluate a CNN-based method for fiducial marker detection in kV X-ray images to support real-time image-guided radiation therapy (IGRT) during liver radiotherapy using a multi-institutional, multi-platform dataset spanning both large and fast free-breathing motion and slow and small breath-hold motion. METHODS: A compact Convolutional Neural Network (CNN) was trained on 314,625 kilovoltage (kV) X-ray images encompassing 28 patients from the multi-institutional TROG 17.03 LARK clinical trial (NCT02984566). The model was validated using a hold-out set of 31,463 images (10%) from the same cohort of 12 patients and 55 treatment fractions, spanning three centers and three respiratory motion-management techniques. The CNN was tested on 4184 images from 16 patients and 55 fractions across three centers and three motion-management techniques. The ground truth for testing was manually segmented marker positions from every 10 degrees of gantry rotation for each fraction of each test patient. Based on AAPM guidelines (TG147 and TGB135.B), feasibility for clinical implementation was predefined as > 95% of marker positions being within 2 mm of the ground truth position in each dimension, and processing time less than 150 ms per image using a simulated real-time Kilovoltage Intrafraction Monitoring (KIM) framework. Additional evaluation metrics included sensitivity, specificity, and the area under the precision-recall curve (AUC). RESULTS: For the unseen test patient data, the marker position was segmented by the CNN within 2 mm in 95.4% of frames on the X axis and 97.6% of frames on the Y axis, meeting AAPM criteria. Sensitivity reached 97.76%, specificity was 99.94%, and the AUC was 0.9964. No statistically significant difference in localization error was observed between breath-hold and free-breathing treatments, although this comparison was limited by the small number of free-breathing patients. Localization error differed significantly between Varian and Elekta linacs due to imbalance within the dataset. The processing time for each image was 50-60 ms using a NVIDIA GeForce RTX 3070 GPU. CONCLUSION: A CNN-based method for fiducial marker detection in the liver was developed and evaluated on multi-institutional kV X-ray images spanning free-breathing and breath-hold motion, as well as Varian and Elekta linac platforms. The method satisfied the predefined feasibility criteria for positional accuracy and computation time, supporting future translation toward clinical implementation.

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