Deep Learning-Based Synthetic Computed Tomography for Low-Field Brain Magnetic Resonance-Guided Radiation Therapy

Abstract

PurposeMR-guided Radiation Therapy (MRgRT) enables online adaptation to address intra- and inter-fractional changes. To address the need of high-fidelity synthetic CT (synCT) required for dose calculation, we developed a conditional generative adversarial network (cGAN) for synCT generation from low-field MRI in the brain. Methods and MaterialsSimulation MR-CT pairs from twelve glioma patients imaged with a head and neck surface coil and treated on a 0.35T MR-linac were prospectively included to train the model consisting of a 9-block residual network generator and a PatchGAN discriminator. Four-fold cross validation was implemented. SynCT was quantitatively evaluated against real CT using mean absolute error (MAE), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). Dose was calculated on synCT applying original treatment plan. Dosimetric performance was evaluated by dose-volume histogram (DVH) metric comparison and local three-dimensional gamma analysis. To demonstrate utilization in treatment adaptation, longitudinal synCTs were generated for qualitative evaluation, and one offline adaptation case underwent two comparative plan evaluations. Secondary validation was conducted with 9 patients on a different MR-linac using a high-resolution brain coil. ResultsOur model generated high-quality synCTs with MAE, PSNR and SSIM of 70.9±10.4 HU, 28.4±1.5 d.B. and 0.87±0.02 within the field-of-view, respectively. Underrepresented post-surgical anomalies challenged model performance. Nevertheless, excellent dosimetric agreement was observed with the mean difference between real and synCT DVH metrics of -0.07±0.29 Gy for target D95 and within [-0.14, 0.02] Gy for organs at risk. Significant differences were only observed in the right lens D0.01cc with negligible overall difference (<0.13 Gy). Mean gamma analysis pass rates were 92.2%±3.0%, 99.2%±0.7% and 99.9%±0.1% at 1%/1mm, 2%/2mm and 3%/3mm, respectively. Secondary validation yielded no significant differences in synCT performance for whole brain MAE, PSNR, and SSIM with comparable dosimetric results. ConclusionsOur cGAN model generated high-fidelity brain synCTs from low-field MRI with excellent dosimetric performance. Secondary validation suggests great promise of implementing synCTs to facilitate robust dose calculation for online adaptive brain MRgRT.