qMRI Diffuser: Quantitative T1 Mapping of the Brain using a Denoising Diffusion Probabilistic Model
This work addresses the need for more accurate and precise quantitative MRI mapping for medical imaging applications, though it appears incremental as it builds on existing deep learning and generative model approaches.
The authors tackled the problem of quantitative T1 mapping in brain MRI by proposing qMRI Diffuser, a method using denoising diffusion probabilistic models, which achieved improved accuracy and precision in parameter estimation compared to existing deep learning methods like ResNet and RIM.
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.