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.
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.
Authors: Jennifer Dhont; Dirk Verellen; Kenneth Poels; Koen Tournel; Manuela Burghelea; Thierry Gevaert; Christine Collen; Benedikt Engels; Robbe Van Den Begin; Nico Buls; Gert Van Gompel; Toon Van Cauteren; Guy Storme; Mark De Ridder Journal: Radiother Oncol Date: 2015-09-03 Impact factor: 6.280
Authors: Rakesh Patel; Joshua Panfil; Maria Campana; Alec M Block; Matthew M Harkenrider; Murat Surucu; John C Roeske Journal: Med Phys Date: 2015-01 Impact factor: 4.071
Authors: Maksat Haytmyradov; Hassan Mostafavi; Adam Wang; Liangjia Zhu; Murat Surucu; Rakesh Patel; Arun Ganguly; Michelle Richmond; Roberto Cassetta; Matthew M Harkenrider; John C Roeske Journal: Med Phys Date: 2019-06-01 Impact factor: 4.071
Authors: John C Roeske; Hassan Mostafavi; Maksat Haytmyradov; Adam Wang; Daniel Morf; Luca Cortesi; Murat Surucu; Rakesh Patel; Roberto Cassetta; Liangjia Zhu; Mathias Lehmann; Matthew M Harkenrider Journal: Adv Radiat Oncol Date: 2020-03-02