Generating PET-derived maps of myelin content from clinical MRI using curricular discriminator training in generative adversarial networks

Théodore Soulier; Mariem Hamzaoui; Milena Sales Pitombeira; Daniele De Paula Faria; Arya Yazdan-Panah; Matteo Tonietto; Claire Leroy; Michel Bottlaender; Benedetta Bodini; Ninon Burgos; Nicholas Ayache; Olivier Colliot; Bruno Stankoff

doi:10.1117/12.3004975

2 April 2024 Generating PET-derived maps of myelin content from clinical MRI using curricular discriminator training in generative adversarial networks

Théodore Soulier, Mariem Hamzaoui, Milena Sales Pitombeira, Daniele De Paula Faria, Arya Yazdan-Panah, Matteo Tonietto, Claire Leroy, Michel Bottlaender, Benedetta Bodini, Ninon Burgos, Nicholas Ayache, Olivier Colliot, Bruno Stankoff

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Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129260S (2024) https://doi.org/10.1117/12.3004975
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Multiple sclerosis (MS) is a demyenalinating inflammatory neurological disease. In vivo biomarkers of myelin content are of major importance for patient care and clinical trials. Positron Emission Tomography (PET) with Pittsburgh Compound B (PiB) provides a specific myelin marker. However, it is not available in clinical routine. In this paper, we propose a method to generate myelin maps by synthesizing PiB PET from clinical routine MRI sequences (T1-weighted and FLAIR). To that purpose, we introduce a new curriculum learning strategy for training generative adversarial networks (GAN). Specifically, we design a curricular approach for training the discriminator: training starts with only lesion patches and random patches (from anywhere in the white matter) are progressively introduced. We relied on two distinct cohorts of MS patients acquired each on a different scanner and in a different country. One cohort was used for training/validation and the other one for testing. We found that the synthetic PiB PET was strongly correlated to the ground-truth both at the lesion level (r = 0.70, p < 10⁻⁵) and the patient level (r = 0.74, p < 10⁻⁵). Moreover, the correlations were stronger when using the curricular learning strategy compared to starting the discriminator training from random patches. Our results demonstrate the interest of this new curriculum learning strategy for PET image synthesis. Even though further evaluations are needed, our approach has the potential to provide a useful biomarker for clinical routine follow-up of patients with MS.

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Théodore Soulier, Mariem Hamzaoui, Milena Sales Pitombeira, Daniele De Paula Faria, Arya Yazdan-Panah, Matteo Tonietto, Claire Leroy, Michel Bottlaender, Benedetta Bodini, Ninon Burgos, Nicholas Ayache, Olivier Colliot, and Bruno Stankoff "Generating PET-derived maps of myelin content from clinical MRI using curricular discriminator training in generative adversarial networks", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129260S (2 April 2024); https://doi.org/10.1117/12.3004975

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