Feasibility of multiparametric PET/MRI as a one-stop-shop for radiotherapy planning of head and neck cancer patients.
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Abstract
Multiparametric PET/MRI for one-stop-shop radiotherapy (RT) planning has great potential but is technically challenging. We studied the feasibility of performing multiparametric PET/MRI of patients with head and neck cancer (HNC) in RT treatment position. As a step towards planning RT based solely on PET/MRI, a deep learning approach was employed to generate synthetic CT (sCT) from MRI. This was subsequently evaluated for dose calculation and PET attenuation correction (AC).
Eleven patients, including three pilot patients referred for RT of HNC, underwent PET/MRI in treatment position after a routine FDG-PET/CT planning scan. The PET/MRI scan protocol included multiparametric imaging. A convolutional neural network (CNN) was trained in a leave-one-out process to predict sCT from the Dixon MRI. The clinical CT-based dose plans were recalculated on sCT, and the plans were compared in terms of relative differences in mean, maximum, near-maximum, and near-minimum absorbed doses for different volumes of interest. Comparisons between PET with sCT-based AC and PET with CT-based AC were assessed based on the relative differences in SUVmean and SUVmax from the PET-positive volumes.
All 11 patients underwent PET/MRI in RT treatment position. Apart from the three pilots, full multiparametric imaging was completed in 45 min for 7/8 patients. One patient terminated the examination after 30 min. With the exception of one patient with an inserted tracheostomy tube, all dosimetric parameters of the sCT-based dose plans were within ±1% of the CT-based dose plans. For PET, the mean difference was 0.4±1.2% for SUVmean and -0.5±1.0% for SUVmax.
Performing multiparametric PET/MRI of patients with HNC in RT treatment position was clinically feasible. The sCT generation resulted in AC of PET and dose calculations sufficiently accurate for clinical use. These results are an important step towards using multiparametric PET/MRI as a one-stop-shop for personalized RT planning.
Copyright © 2020. Published by Elsevier Inc.