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Adaptive weighted log subtraction based on neural networks for markerless tumor tracking using dual‐energy fluoroscopy
Author(s) -
Haytmyradov Maksat,
Mostafavi Hassan,
Cassetta Roberto,
Patel Rakesh,
Surucu Murat,
Zhu Liangjia,
Roeske John C.
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13941
Subject(s) - imaging phantom , artificial intelligence , computer science , fluoroscopy , computer vision , convolutional neural network , pixel , subtraction , weighting , mathematics , physics , optics , acoustics , arithmetic , nuclear physics
Purpose To present a novel method, based on convolutional neural networks (CNN), to automate weighted log subtraction (WLS) for dual‐energy (DE) fluoroscopy to be used in conjunction with markerless tumor tracking (MTT). Methods A CNN was developed to automate WLS (aWLS) of DE fluoroscopy to enhance soft tissue visibility. Briefly, this algorithm consists of two phases: training a CNN architecture to predict pixel‐wise weighting factors followed by application of WLS subtraction to reduce anatomical noise. To train the CNN, a custom phantom was built consisting of aluminum (Al) and acrylic (PMMA) step wedges. Per‐pixel ground truth (GT) weighting factors were calculated by minimizing the contrast of Al in the step wedge phantom to train the CNN. The pretrained model was then utilized to predict pixel‐wise weighting factors for use in WLS. For comparison, the weighting factor was manually determined in each projection (mWLS). A thorax phantom with five simulated spherical targets (5–25 mm) embedded in a lung cavity, was utilized to assess aWLS performance. The phantom was imaged with fast‐kV dual‐energy (120 and 60 kVp) fluoroscopy using the on‐board imager of a commercial linear accelerator. DE images were processed offline to produce soft tissue images using both WLS methods. MTT was compared using soft tissue images produced with both mWLS and aWLS techniques. Results Qualitative evaluation demonstrated that both methods achieved soft tissue images with similar quality. The use of aWLS increased the number of tracked frames by 1–5% compared to mWLS, with the largest increase observed for the smallest simulated tumors. The tracking errors for both methods produced agreement to within 0.1 mm. Conclusions A novel method to perform automated WLS for DE fluoroscopy was developed. Having similar soft tissue quality as well as bone suppression capability as mWLS, this method allows for real‐time processing of DE images for MTT.