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

PURPOSE

Magnetic resonance (MR)-guided radiation therapy enables online adaptation to address intra- and interfractional changes. To address the need of high-fidelity synthetic computed tomography (synCT) required for dose calculation, we developed a conditional generative adversarial network for synCT generation from low-field MR imaging in the brain.

METHODS AND MATERIALS

Simulation MR-CT pairs from 12 patients with glioma 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 index measure (SSIM). Dose was calculated on synCT applying original treatment plan. Dosimetric performance was evaluated by dose-volume histogram metric comparison and local 3-dimensional gamma analysis. To demonstrate utilization in treatment adaptation, longitudinal synCTs were generated for qualitative evaluation, and 1 offline adaptation case underwent 2 comparative plan evaluations. Secondary validation was conducted with 9 patients on a different MR-linac using a high-resolution brain coil.

RESULTS

Our model generated high-quality synCTs with MAE, PSNR, and SSIM of 70.9 ± 10.4 HU, 28.4 ± 1.5 dB, and 0.87 ± 0.02 within the field of view, respectively. Underrepresented postsurgical anomalies challenged model performance. Nevertheless, excellent dosimetric agreement was observed with the mean difference between real and synCT dose-volume histogram metrics of -0.07 ± 0.29 Gy for target D and within [-0.14, 0.02] Gy for organs at risk. Significant differences were only observed in the right lens D 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%/1 mm, 2%/2 mm, and 3%/3 mm, respectively. Secondary validation yielded no significant differences in synCT performance for whole-brain MAE, PSNR, and SSIM with comparable dosimetric results.

CONCLUSIONS

Our conditional generative adversarial network model generated high-fidelity brain synCTs from low-field MR imaging with excellent dosimetric performance. Secondary validation suggests great promise of implementing synCTs to facilitate robust dose calculation for online adaptive brain MR-guided radiation therapy.