Deep MR to CT Synthesis using Unpaired Data
This addresses the need for accurate MR-only radiotherapy planning by reducing reliance on perfectly aligned paired images, though it is incremental as it builds on existing GAN methods.
The paper tackled the problem of MR-to-CT synthesis for radiotherapy planning by proposing a GAN trained with unpaired data to avoid errors from misaligned paired images, resulting in synthesized CT images that closely approximate reference CTs and outperform a paired-data GAN model.
MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis. Current deep learning methods for MR-to-CT synthesis depend on pairwise aligned MR and CT training images of the same patient. However, misalignment between paired images could lead to errors in synthesized CT images. To overcome this, we propose to train a generative adversarial network (GAN) with unpaired MR and CT images. A GAN consisting of two synthesis convolutional neural networks (CNNs) and two discriminator CNNs was trained with cycle consistency to transform 2D brain MR image slices into 2D brain CT image slices and vice versa. Brain MR and CT images of 24 patients were analyzed. A quantitative evaluation showed that the model was able to synthesize CT images that closely approximate reference CT images, and was able to outperform a GAN model trained with paired MR and CT images.