PSEUDO-CT GENERATION FOR MRI-ONLY RADIOTHERAPY: COMPARATIVE STUDY BETWEEN A GENERATIVE ADVERSARIAL NETWORK, A U-NET NETWORK, A PATCH-BASED, AND AN ATLAS BASED METHODS

Axel Largent (1) , Jean-Claude Nunes (1) , Hervé Saint-Jalmes (1) , John Baxter (1) , Peter Greer (2) , Jason Dowling (3) , Renaud de Crevoisier (1, 4) , Oscar Acosta (1, 4)

1 LTSI - Laboratoire Traitement du Signal et de l'Image
2 UoN - University of Newcastle [Callaghan, Australia]
3 CSIRO ICT Centre - CSIRO Information and Commuciation Technologies
4 CRLCC - CRLCC Eugène Marquis

As new radiotherapy treatment systems using MRI (rather than traditional CT) are being developed, the accurate calculation of dose maps from MR imaging has become an increasing concern. MRI provides good soft-tissue but, unlike CT, lacks the electron density information necessary for dose calculation. In this paper, we proposed a generative adversarial network (GAN) using a perceptual loss to generate pseudo-CTs for prostate MRI dose calculation. This network was evaluated and compared to a U-Net network, a patch-based (PBM) and an atlas-based methods (ARM). Influence of the perceptual loss was assessed by comparing this network to a GAN using a L2 loss. GANs and U-Nets are rather similar with slightly better results for GANs. The proposed GAN outperformed the PBM by 9% and the ARM by 13% in term of MAE in whole pelvis. This method could be used for online dose calculation in MRI-only radiotherapy.

Mots clés: Pseudo-CT; Magnetic Resonance Imaging; Radiotherapy Treatment Planning

Domaines: Ingénierie biomédicale

Axel Largent, Jean-Claude Nunes, Hervé Saint-Jalmes, John Baxter, Peter Greer, et al.. PSEUDO-CT GENERATION FOR MRI-ONLY RADIOTHERAPY: COMPARATIVE STUDY BETWEEN A GENERATIVE ADVERSARIAL NETWORK, A U-NET NETWORK, A PATCH-BASED, AND AN ATLAS BASED METHODS. 16th IEEE International Symposium on Biomedical Imaging (ISBI), Apr 2019, Venice, Italy. ⟨hal-02302408⟩