Fast in vivo volume dose reconstruction via Reference Dose Perturbation

17th International Conference on the Use of Computers in Radiation Therapy

物理学;计算机科学

Lu, Weiguo^1 ; Chen, Mingli^1 ; Mo, Xiaohu^1 ; Parnell, Donald^1 ; Olivera, Gustavo^1 ; Galmarini, Daniel^1

21st Century Oncology, United States^1

关键词: Clinical data;    Density images;    Dose reconstruction;    Gamma indices;    Ground truth;    Position errors;    Reference dose;    Setup errors;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/489/1/012016/pdf
DOI  :  10.1088/1742-6596/489/1/012016

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Purpose: Accurate on-line reconstruction of in-vivo volume dose that accounts for both machine and patient discrepancy is not clinically available. We present a simple reference-dose-perturbation algorithm that reconstructs in-vivo volume dose fast and accurately. Methods: We modelled the volume dose as a function of the fluence map and density image. Machine (output variation, jaw/leaf position errors, etc.) and patient (setup error, weight loss, etc.) discrepancies between the plan and delivery were modelled as perturbation of the fluence map and density image, respectively. Delivered dose is modelled as perturbation of the reference dose due to change of the fluence map and density image. We used both simulated and clinical data to validate the algorithm. The planned dose was used as the reference. The reconstruction was perturbed from the reference and accounted for output-variations and the registered daily image. The reconstruction was compared with the ground truth via isodose lines and the Gamma Index. Results: For various plans and geometries, the volume doses were reconstructed in few seconds. The reconstruction generally matched well with the ground truth. For the 3%/3mm criteria, the Gamma pass rates were 98% for simulations and 95% for clinical data. The differences mainly appeared on the surface of the phantom/patient. Conclusions: A novel reference-dose- perturbation dose reconstruction model is presented. The model accounts for machine and patient discrepancy from planning. The algorithm is simple, fast, yet accurate, which makes online in-vivo 3D dose reconstruction clinically feasible.