Deep learning MRI-only synthetic-CT generation for pelvis, brain and head and neck cancers.

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Abstract

MRI-only planning relies on dosimetrically accurate synthetic-CT (sCT) generation to allow dose calculation. Here we validated the dosimetric accuracy of sCTs generated using a deep learning algorithm for pelvic, brain and head and neck (H&N) cancer sites using variable MRI data from multiple scanners.sCT generation models were trained using a cycle-GAN algorithm, using paired MRI-CT patient data. Input MRI sequences were: T2 for pelvis, T1 with gadolinium (T1Gd) and T2 FLAIR for brain and T1 for H&N. Patient validation sCTs were generated for each site (49 – pelvis, 25 – brain and 30 – H&N). VMAT plans, following local clinical protocols, were calculated on planning CTs and recalculated on sCTs. HU and dosimetric differences were assessed, including DVH differences and gamma index (2%/2mm).Mean absolute error (MAE) HU differences were; 48.8 HU (pelvis), 118 HU (T2 FLAIR brain), 126 HU (T1Gd brain) and 124 HU (H&N). Mean primary PTV D95% dose differences for all sites were <0.2 % (range: -0.9 to 1.0 %). Mean 2%/2mm and 1%/1mm gamma pass rates for all sites were > 99.6 % (min: 95.3 %) and >97.3 % (min: 80.1 %) respectively. For all OARs for all sites, mean dose differences were <0.4 %.Generated sCTs had excellent dosimetric accuracy for all sites and sequences. The cycle-GAN model, available on the research version of a commercial treatment planning system, is a feasible method for sCT generation with high clinical utility due to its ability to use variable input data from multiple scanners and sequences.Copyright © 2023. Published by Elsevier B.V.