Abstract
PurposeThe purpose of this study was to address the dosimetric accuracy of synthetic computed tomography (sCT) images of patients with brain tumor generated using a modified generative adversarial network (GAN) method, for their use in magnetic resonance imaging (MRI)‐only treatment planning for proton therapy.MethodsDose volume histogram (DVH) analysis was performed on CT and sCT images of patients with brain tumor for plans generated for intensity‐modulated proton therapy (IMPT). All plans were robustly optimized using a commercially available treatment planning system (RayStation, from RaySearch Laboratories) and standard robust parameters reported in the literature. The IMPT plan was then used to compute the dose on CT and sCT images for dosimetric comparison, using RayStation analytical (pencil beam) dose algorithm. We used a second, independent Monte Carlo dose calculation engine to recompute the dose on both CT and sCT images to ensure a proper analysis of the dosimetric accuracy of the sCT images.ResultsThe results extracted from RayStation showed excellent agreement for most DVH metrics computed on the CT and sCT for the nominal case, with a mean absolute difference below 0.5% (0.3 Gy) of the prescription dose for the clinical target volume (CTV) and below 2% (1.2 Gy) for the organs at risk (OARs) considered. This demonstrates a high dosimetric accuracy for the generated sCT images, especially in the target volume. The metrics obtained from the Monte Carlo doses mostly agreed with the values extracted from RayStation for the nominal and worst‐case scenarios (mean difference below 3%).ConclusionsThis work demonstrated the feasibility of using sCT generated with a GAN‐based deep learning method for MRI‐only treatment planning of patients with brain tumor in intensity‐modulated proton therapy.
Highlights
Magnetic resonance imaging (MRI) is often used in radiation therapy to accurately contour the clinical target volume (CTV) and organs at risk (OARs) because of its superior soft tissue contrast compared with computed tomography (CT) images
The MRI/CT pairs used for training were non‐aligned, and our aim was to overcome the difficulties related to MRI‐to‐CT registration by using the mutual information as the loss function (See Section 2.B.2)
The mean absolute error (MAE) obtained is slightly smaller than the values reported in recently published studies using deep learning methods, like the convolutional neural networks (CNN) method used by Dinkla et al.,[16] who reported an MAE
Summary
Magnetic resonance imaging (MRI) is often used in radiation therapy to accurately contour the clinical target volume (CTV) and organs at risk (OARs) because of its superior soft tissue contrast compared with computed tomography (CT) images. Magnetic resonance imaging‐CT co‐registration introduces geometrical uncertainties of ~2 mm for the brain[2,3] and 2–3 mm for prostate and gynecological patients.[4] Importantly, these errors are systematic, persist throughout treatment, shift high‐dose regions away from the target,[5] and may lead to a geometric miss that compromises tumor control. This problem has recently led to the concept of MRI‐only–based treatment planning, where pseudo or synthetic CT (sCT) images for dose calculation are generated directly from the MRI scan. Accurately generating Hounsfield unit (HU) maps from MRI images is not straightforward
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