A markerless beam's eye view motion monitoring algorithm based on structure conversion and demons registration in medical image with partial data.

PURPOSE

To propose a markerless beam's eye view (BEV) motion monitoring algorithm, which works with the inferior quality megavolt (MV) images with multi-leaf collimator (MLC) occlusion-compatible.

METHODS

A thorax phantom was used to verify the accuracy of the algorithm. Lung tumor quality assurance (QA) plans were generated for the phantom, and delivered 10 times on the linear accelerator with manually treatment offsets in various directions. The algorithm was used to register 753 electronic portal imaging device (EPID) images with the appropriate digitally reconstructed radiograph (DRR), calculating a registration offset that was compared with the actual offset to determine the monitoring errors. Image similarity measure was used as an independent check. Additionally, patient data of 21 lung tumor treatment plans were gathered. A total of 533 pairs of patient images were acquired for motion monitoring study, to offer quantifiable data of the tumor position change during treatment.

RESULTS

The monitoring algorithm can process various degrees (10%-80%) of image loss, and performs well when dealing with non-rigid registration for partial data images. About 86.8% of the monitoring errors are less than 3 mm in the algorithm verification of the phantom study, and about 80% of the errors are under than 2 mm. Normalized Mutual Information (NMI) of phantom images changes from 1.182 ± 0.026 to 1.202 ± 0.027, with p < 0.005, and the Hausdorff-Distance (HD) changes from 3.506 ± 0.417 mm to 3.466 ± 0.473 mm, with p < 0.005. Translation with a displacement range of -6.0 mm to 6.2 mm is the predominant change of the patient target during treatment. NMI of patient images changes from 1.216 ± 0.031 to 1.225 ± 0.031, with p < 0.005, and HD changes from 3.131 ± 0.876 mm to 3.118 ± 0.038 mm, with p < 0.005. The dice index of target before and after registration is 0.264 ± 0.336, indicating the presence of non-negligible non-rigid deformation.

CONCLUSIONS

The study provides a robust markerless motion monitoring algorithm for multi-modal, partial data and inferior quality image processing.