Adaptive weighted log subtraction based on neural networks for markerless tumor tracking using dual‐energy fluoroscopy

• Published in: Medical physics (Lancaster) Vol. 47; no. 2; pp. 672 - 680
• Main Authors: Haytmyradov, Maksat, Mostafavi, Hassan, Cassetta, Roberto, Patel, Rakesh, Surucu, Murat, Zhu, Liangjia, Roeske, John C.
• Format: Journal Article
• Language: English
• Published: United States 01-02-2020
• Subjects: Calibration; convolutional neural networks; dual‐energy imaging; fast‐kV switching; Fluoroscopy; Image Processing, Computer-Assisted - methods; markerless tumor tracking; Neoplasms - diagnostic imaging; Neural Networks, Computer; Phantoms, Imaging; Subtraction Technique; convolutional neural networks; fast-kV switching; dual-energy imaging; markerless tumor tracking
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.13941

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.