Abstract
To introduce a novel, deep-learning method to generate synthetic computed tomography (SCT) scans for proton treatment planning and evaluate its efficacy. 50 Patients with base of skull tumors were divided into 2 nonoverlapping training and study cohorts. Computed tomography and magnetic resonance imaging pairs for patients in the training cohort were used for training our novel 3-dimensional generative adversarial network (cycleGAN) algorithm. Upon completion of the training phase, SCT scans for patients in the study cohort were predicted based on their magnetic resonance images only. The SCT scans obtained were compared against the corresponding original planning computed tomography scans as the ground truth, and mean absolute errors (in Hounsfield units [HU]) and normalized cross-correlations were calculated. Proton plans of 45 Gy in 25 fractions with 2 beams per plan were generated for the patients based on their planning computed tomographies and recalculated on SCT scans. Dose-volume histogram endpoints were compared. A γ-index analysis along 3 cardinal planes intercepting at the isocenter was performed. Proton distal range along each beam was calculated. Image quality metrics show agreement between the generated SCT scans and the ground truth with mean absolute error values ranging from 38.65 to 65.12 HU and an average of 54.55 ± 6.81 HU and a normalized cross-correlation average of 0.96 ± 0.01. The dosimetric evaluation showed no statistically significant differences (p > 0.05) within planning target volumes for dose-volume histogram endpoints and other metrics studied, with the exception of the dose covering 95% of the target volume, with a relative difference of 0.47%. The γ-index analysis showed an average passing rate of 98% with a 10% threshold and 2% and 2-mm criteria. Proton ranges of 48 of 50 beams (96%) in this study were within clinical tolerance adopted by 4 institutions. This study shows our method is capable of generating SCT scans with acceptable image quality, dose distribution agreement, and proton distal range compared with the ground truth. Our results set a promising approach for magnetic resonance imaging-based proton treatment planning.
Highlights
Current dose calculation in photon and proton radiotherapy is based on computed tomography (CT) as the primary imaging modality [1, 2]
We introduced a novel learning-based approach for an magnetic resonance imaging (MRI)-only method of generating synthetic computed tomography (SCT) scans for proton therapy of BoS tumors with a 3D cycleGAN
We investigated the efficacy of our method for image quality, dosimetric performance, and proton distal range
Summary
Current dose calculation in photon and proton radiotherapy is based on computed tomography (CT) as the primary imaging modality [1, 2]. The CT provides density information on patients’ anatomy [3] in terms of Hounsfield units (HU), which, in turn, can MRI-based proton treatment planning be converted to relative electron density and a relative stopping power ratio (rSPR) to water for accurate photon and proton dose calculations. Superior soft tissue contrast obtained from MRI, compared with CT scans, enhances the delineation of the target and other critical structures [4, 5], whereas CT enables precise calculation of dose distribution [6,7,8] and provides reference images that are essential for patient positioning [9]. Image registration introduces systematic spatial uncertainties, which are carried downstream to all phases of treatment planning and delivery [12,13,14,15,16]
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