OC-029: MR-based synthetic CT with conditional Generative Adversarial Network for prostate RT planning

MHF Savenije, M Maspero, AM Dinkla, PR Seevinck, CAT van den Berg

Research output: Contribution to journalArticleAcademicpeer-review


Purpose or Objective
To enable MR-only planning and accurate MR-based dose calculations, so-called synthetic CT (sCT) images need to be generated. Recently, conditional Generative Adversarial Networks (cGANs) have been proposed as a general-purpose solution to image-to-image translation problems. By interpreting the generation of sCT images as an image-to-image problem, this work aims at assessing whether a cGAN can generate sCT suitable for treatment planning on prostate cancer patients.

Material and Methods
A study was conducted on 59 patients who underwent prostate IMRT for which CT (Brilliance CT Big Bore, Philips) as well as MR (3T Ingenia Omega HP, Philips) scans were acquired for simulation purposes on the same day and in RT position. To generate the sCT images, dual gradient-echo, RF spoiled, 3D T1w MR images were acquired with 1x1x2.5 mm3 resolution, TR/TE2/TE1=3.9/2.5/1.2ms. Dixon reconstruction was performed producing water (W), fat (F) and in-phase (IP) images. A cGAN called “pix2pix” (P. Isola et al, 2016, arXiv) was used in this experiment. Before training, CT images were rigidly registered to MR images and MR images were normalised (Figure 1a). Within pix2pix, the images were scaled to 8-bit grayscale values and resampled to 256x256-pixel slices (Figure 1b). Training of pix2pix was performed on 32 patients in 2D transverse slices using 200 epochs. To guarantee consistent air pockets on the CT and MR images during training, internal air pockets as detected on MR images were copied to CT (Figure 1c). The trained cGAN was applied to the remaining 27 patients (test set) producing sCT images. Image evaluation was performed on the sCT using mean absolute error (MAE) as compared to CT. Dose recalculation of clinical 5-beam 10 MV IMRT plans with a prescribed dose of 35x2.0 Gy to the target was performed for 15/27 patients on the CT and sCT images in Monaco (v 5.11.02, Elekta AB). Dose distributions were subsequently analysed through voxel-based dose differences and gamma analysis.

In total, 3495 slices were used for training, requiring about 11 hrs on a GPU Tesla P100 (NVIDIA). Applying the trained cGAN to a single patient volume (Figure 2) required about 5.6s. Image Evaluation: On average, an MAE of 65±10HU (±1σ, range: 50-96HU) was obtained in the intersection of the body contours between CT and sCT. When air pockets were also copied to the CT in the test set, the MAE reduced to 60±6HU (range: 48-71HU). Dose Evaluation: On average (Table 1), a dose difference below 1.1% was obtained using a 50% dose threshold of the prescribed dose. A mean gamma pass rate of 96±4% was obtained.

Results suggest that accurate MR-based dose calculation using a 2D cGAN for sCT generation is feasible for prostate cancer patients. An additional advantage of using this network is the short time needed to generate the sCT, which would be beneficial for MR-guided RT application. Future investigations will evaluate dose to clinically relevant volumes as well as the use of a 3D network.
Original languageEnglish
Pages (from-to)S150-S151
Number of pages2
JournalRadiotherapy & Oncology
Publication statusPublished - Apr 2018
EventESTRO 37 - Barcelona, Spain
Duration: 20 Apr 201824 Apr 2018
Conference number: 37


  • synthetic-CT
  • MR-only Radiotherapy
  • MRI


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