4DCBCT-based motion modeling and 3D fluoroscopic image generation for lung cancer radiotherapy

Salam Dhou; Martina Hurwitz; Pankaj Mishra; Ross Berbeco; John Lewis

doi:10.1117/12.2082291

18 March 2015 4DCBCT-based motion modeling and 3D fluoroscopic image generation for lung cancer radiotherapy

Salam Dhou, Martina Hurwitz, Pankaj Mishra, Ross Berbeco, John Lewis

Proceedings Volume 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling; 941510 (2015) https://doi.org/10.1117/12.2082291
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

A method is developed to build patient-specific motion models based on 4DCBCT images taken at treatment time and use them to generate 3D time-varying images (referred to as 3D fluoroscopic images). Motion models are built by applying Principal Component Analysis (PCA) on the displacement vector fields (DVFs) estimated by performing deformable image registration on each phase of 4DCBCT relative to a reference phase. The resulting PCA coefficients are optimized iteratively by comparing 2D projections captured at treatment time with projections estimated using the motion model. The optimized coefficients are used to generate 3D fluoroscopic images. The method is evaluated using anthropomorphic physical and digital phantoms reproducing real patient trajectories. For physical phantom datasets, the average tumor localization error (TLE) and (95th percentile) in two datasets were 0.95 (2.2) mm. For digital phantoms assuming superior image quality of 4DCT and no anatomic or positioning disparities between 4DCT and treatment time, the average TLE and the image intensity error (IIE) in six datasets were smaller using 4DCT-based motion models. When simulating positioning disparities and tumor baseline shifts at treatment time compared to planning 4DCT, the average TLE (95th percentile) and IIE were 4.2 (5.4) mm and 0.15 using 4DCT-based models, while they were 1.2 (2.2) mm and 0.10 using 4DCBCT-based ones, respectively. 4DCBCT-based models were shown to perform better when there are positioning and tumor baseline shift uncertainties at treatment time. Thus, generating 3D fluoroscopic images based on 4DCBCT-based motion models can capture both inter- and intra- fraction anatomical changes during treatment.

Citation Download Citation

Salam Dhou, Martina Hurwitz, Pankaj Mishra, Ross Berbeco, and John Lewis "4DCBCT-based motion modeling and 3D fluoroscopic image generation for lung cancer radiotherapy", Proc. SPIE 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, 941510 (18 March 2015); https://doi.org/10.1117/12.2082291

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