Pseudo-CT generation by conditional inference random forest for MRI-based radiotherapy treatment planning

Axel Largent, Jean Claude Nunes, Hervé Saint-Jalmes, Antoine Simon, Nicolas Perichon, Anais Barateau, Chloé Hervé, Caroline Lafond, Peter B. Greer, Jason A. Dowling, Renaud De Crevoisier, Oscar Acosta

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

11 Citations (Scopus)


Dose calculation from MRI is a topical issue. New treatment systems combining a linear accelerator with a MRI have been recently being developed. MRI has good soft tissue contrast without ionizing radiation exposure. However, unlike CT, MRI does not provide electron density information necessary for dose calculation. We propose in this paper a machine learning method to simulate a CT from a target MRI and co-registered CT-MRI training set. Ten prostate MR and CT images have been considered. Firstly, a reference image was randomly selected in the training set. A common space has been built thanks to affine registrations between the training set and the reference image. Multiscale image descriptors such as spatial information, gradients and texture features were extracted from MRI patches at different levels of a Gaussian pyramid and used as voxel-wise characteristics in the learning scheme. A Conditional Inference Random Forest (CIRF) modelled the relation between MRI descriptors and CT patches. For validation, test images were spatially normalized and the same descriptors were computed to generate a new pCT. Leave-one out experiments were performed. We obtained a MAE = 45.79 (pCT vs CT). Dose volume histograms inside PTV and organs at risk are in close agreement. The D98% was 0.45 % (inside PTV) and the 3D gamma pass rate (1mm, 1%) was 99,2%. Our method has better results than direct bulk assignment. And the results suggest that the method may be used for dose calculations in an MR based planning system.

Original languageEnglish
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
EditorsKonstantinos Diamantaras, Stefanos Kollias, Constantine Kotropoulos, Gerasimos Potamianos
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9780992862671
ISBN (Print)9781538607510
Publication statusPublished - 23 Oct 2017
Externally publishedYes
Event25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece
Duration: 28 Aug 20172 Sept 2017

Publication series

Name25th European Signal Processing Conference, EUSIPCO 2017


Conference25th European Signal Processing Conference, EUSIPCO 2017


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