title: Impact of temporal probability in 4D dose calculation for lung tumors.
abstract: The purpose of this study was to evaluate the dosimetric uncertainty in 4D dose calculation using three temporal probability distributions: uniform distribution, sinusoidal distribution, and patient-specific distribution derived from the patient respiratory trace. Temporal probability, defined as the fraction of time a patient spends in each respiratory amplitude, was evaluated in nine lung cancer patients. Four-dimensional computed tomography (4D CT), along with deformable image registration, was used to compute 4D dose incorporating the patient's respiratory motion. First, the dose of each of 10 phase CTs was computed using the same planning parameters as those used in 3D treatment planning based on the breath-hold CT. Next, deformable image registration was used to deform the dose of each phase CT to the breath-hold CT using the deformation map between the phase CT and the breath-hold CT. Finally, the 4D dose was computed by summing the deformed phase doses using their corresponding temporal probabilities. In this study, 4D dose calculated from the patient-specific temporal probability distribution was used as the ground truth. The dosimetric evaluation matrix included: 1) 3D gamma analysis, 2) mean tumor dose (MTD), 3) mean lung dose (MLD), and 4) lung V20. For seven out of nine patients, both uniform and sinusoidal temporal probability dose distributions were found to have an average gamma passing rate > 95% for both the lung and PTV regions. Compared with 4D dose calculated using the patient respiratory trace, doses using uniform and sinusoidal distribution showed a percentage difference on average of -0.1% ± 0.6% and -0.2% ± 0.4% in MTD, -0.2% ± 1.9% and -0.2% ± 1.3% in MLD, 0.09% ± 2.8% and -0.07% ± 1.8% in lung V20, -0.1% ± 2.0% and 0.08% ± 1.34% in lung V10, 0.47% ± 1.8% and 0.19% ± 1.3% in lung V5, respectively. We concluded that four-dimensional dose computed using either a uniform or sinusoidal temporal probability distribution can approximate four-dimensional dose computed using the patient-specific respiratory trace.
authors.affiliation: University of Iowa. ouided.rouabhi@gmail.com.
authors.forename: Ouided
authors.initials: O
authors.lastname: Rouabhi
authors.forename: Mingyu
authors.initials: M
authors.lastname: Ma
authors.forename: John
authors.initials: J
authors.lastname: Bayouth
authors.forename: Junyi
authors.initials: J
authors.lastname: Xia
mesh_terms: D062485:Breath Holding; D056973:Four-Dimensional Computed Tomography / Q000706:statistics & numerical data; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy* / Q000601:surgery; D009068:Movement; D011336:Probability; D011874:Radiometry; D016634:Radiosurgery / Q000379:methods / Q000706:statistics & numerical data; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods / Q000706:statistics & numerical data; D012119:Respiration