Deep learning-based synthetic-CT-free photon dose calculation in MR-guided radiotherapy: A proof-of-concept study

Abstract

Background: In magnetic resonance imaging (MRI)-guided online adaptive radiotherapy, MRI lacks tissue attenuation information necessary for accurate dose calculations. Although deep learning (DL)-based synthetic computed tomography (CT) generation models have been developed to obtain CT density information from MRI, they usually do not meet the requirement of real-time plan adaptation.

Purpose: We propose a DL-based photon dose calculation method directly on 0.35 T MRI to skip synthetic CT generation and show its feasibility for prostate patient cases.

Methods: The 0.35 T planning MRI and deformed planning CT (registered to the planning MRI) of 34 prostate cancer patients treated with a 0.35 T magnetic resonance-linear accelerator (MR-Linac) were collected. The air cavities (ACs) in the abdominopelvic area of the deformed CT images were corrected based on manual AC contouring on the MRI. Monte Carlo (MC) dose simulations under a 0.35 T magnetic field were performed on the corrected CT images. All photon beams were simulated using a uniform field size of $1\text{cm}\times 1\text{cm}$ . 10 800 beams were simulated with $5\times {10}^6$ initial photons for training (20 patients) and 2160 beams with $5\times {10}^7$ photons for validation (4 patients). For testing, 1080 beams shooting through the planning target volume (PTV) in 10 patients and five optimized nine-field intensity-modulated plans were simulated with $5\times {10}^7$ photons. 3D MRI cuboids covering the photon beams were input into a Unet model to predict AC segmentation, and 3D MRI and predicted AC cuboids were input into a long short-term memory (LSTM) model for beam's eye view (BEV) processing to predict dose. The gamma passing rate ${\gamma }_{\mathrm{pr}}$ (2%/2mm, $D>10\%D_{\max }$ ), beam dose profiles of single beams and dose volume histogram (DVH) of intensity-modulated plans were evaluated.

Results: The test results for all photon beams from the proposed models demonstrated a mean ${\gamma }_{\mathrm{pr}}$ above 99.50%. The five treatment plans recalculated by the DL model each achieved ${\gamma }_{\mathrm{pr}}$ values exceeding 99.80%. Additionally, the model's inference time was approximately 12 ms per photon beam.

Conclusions: The proposed method showed that DL-based dose calculation directly on MRI is feasible for prostate cases, which has the potential to simplify the procedure for MRI-only workflows and can be beneficial for real-time plan adaptation.

Keywords: LSTM; MRI; deep learning; dose calculation; online MR‐guided photon therapy.