MRI-only brain radiotherapy: assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach

TL;DR

This study demonstrates a deep learning GAN approach to generate highly accurate synthetic CT images from MRI data for brain radiotherapy, enabling rapid MRI-only treatment planning.

Contribution

We developed a GAN-based method using Mutual Information loss to produce accurate synthetic CTs from MRI, streamlining brain radiotherapy planning.

Findings

Mean absolute error of 47.2 HU in synthetic CTs

Dice similarity coefficient of 80% in bone regions

Synthetic CT generation takes only one second per patient

Abstract

Purpose: This study assessed the dosimetric accuracy of synthetic CT images generated from magnetic resonance imaging (MRI) data for focal brain radiation therapy, using a deep learning approach. Material and Methods: We conducted a study in 77 patients with brain tumors who had undergone both MRI and computed tomography (CT) imaging as part of their simulation for external beam treatment planning. We designed a generative adversarial network (GAN) to generate synthetic CT images from MRI images. We used Mutual Information (MI) as the loss function in the generator to overcome the misalignment between MRI and CT images (unregistered data). The model was trained using all MRI slices with corresponding CT slices from each training subject s MRI/CT pair. Results: The proposed GAN method produced an average mean absolute error (MAE) of 47.2 +- 11.0 HU over 5-fold cross validation. The overall mean Dice similarity coefficient between CT and synthetic CT images was 80% +- 6% in bone for all test data. Though training a GAN model may take several hours, the model only needs to be trained once. Generating a complete synthetic CT volume for each new patient MRI volume using a trained GAN model took only one second. Conclusions: The GAN model we developed produced highly accurate synthetic CT images from conventional, single-sequence MRI images in seconds. Our proposed method has strong potential to perform well in a clinical workflow for MRI-only brain treatment planning.