qMRI Diffuser

Quantitative T1 Mapping of the Brain Using a Denoising Diffusion Probabilistic Model

Conference Paper (2025)
Author(s)

Shishuai Wang (Erasmus MC)

Hua Ma (Erasmus MC)

Juan Antonio Hernández-Tamames (Erasmus MC, TU Delft - ImPhys/Vos group)

Stefan Klein (Erasmus MC)

Dirk H  J Poot (Erasmus MC)

Research Group
ImPhys/Vos group
DOI related publication
https://doi.org/10.1007/978-3-031-72744-3_13
More Info
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Publication Year
2025
Language
English
Research Group
ImPhys/Vos group
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
129-138
ISBN (print)
978-3-031-72743-6
ISBN (electronic)
978-3-031-72744-3
Reuse Rights

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Abstract

Quantitative MRI (qMRI) offers significant advantages over weighted images by providing objective parameters related to tissue properties. Deep learning-based methods have demonstrated effectiveness in estimating quantitative maps from series of weighted images. In this study, we present qMRI Diffuser, a novel approach to qMRI utilising deep generative models. Specifically, we implemented denoising diffusion probabilistic models (DDPM) for T1 quantification in the brain, framing the estimation of quantitative maps as a conditional generation task. The proposed method is compared with the residual neural network (ResNet) and the recurrent inference machine (RIM) on both phantom and in vivo data. The results indicate that our method achieves improved accuracy and precision in parameter estimation, along with superior visual performance. Moreover, our method inherently incorporates stochasticity, enabling straightforward quantification of uncertainty. Hence, the proposed method holds significant promise for quantitative MR mapping.

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