D2SA: Dual-Stage Distribution and Slice Adaptation
for Efficient Test-Time Adaptation in MRI Reconstruction

Lipei Zhang

Rui Sun

Zhongying Deng

Yanqi Cheng

Carola-Bibiane Schönlieb

Angelica I. Aviles-Rivero

The visualisation of our proposed framework.

Abstract

Variations in Magnetic resonance imaging (MRI) scanners and acquisition protocols cause distribution shifts that degrade reconstruction performance on unseen data. Test-time adaptation (TTA) offers a promising solution to address this discrepancies. However, previous single-shot TTA approaches are inefficient due to repeated training and suboptimal distributional models. Self-supervised learning methods may risk over-smoothing in scarce data scenarios. To address these challenges, we propose a novel Dual-Stage Distribution and Slice Adaptation (D2SA) via MRI implicit neural representation (MR-INR) to improve MRI reconstruction performance and efficiency, which features two stages. In the first stage, an MR-INR branch performs patient-wise distribution adaptation by learning shared representations across slices and modelling patient-specific shifts with mean and variance adjustments. In the second stage, single-slice adaptation refines the output from frozen convolutional layers with a learnable anisotropic diffusion module, preventing over-smoothing and reducing computation. Experiments across five MRI distribution shifts demonstrate that our method can integrate well with various self-supervised learning (SSL) framework, improving performance and accelerating convergence under diverse conditions.

VarNet Results

UNet Results

Paper and Supplementary Material

Lipei Zhang, Rui Sun, Zhongying Deng, Yanqi Cheng, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero.
D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction

[Paper] [Bibtex]

Acknowledgements

RS gratefully acknowledges the support from Future Network of Intelligence Institute, The Chinese University of Hong Kong (Shenzhen). ZD acknowledges the support from Wellcome Trust 221633/Z/20/Z and the funding from the Cambridge Centre for Data-Driven Discovery and Accelerate Program for Scientific Discovery, made possible by a donation from Schmidt Sciences. YC is funded by an AstraZeneca studentship and a Google studentship. CBS acknowledges support from the Philip Leverhulme Prize, the Royal Society Wolfson Fellowship, the EPSRC advanced career fellowship EP/V029428/1, EPSRC grants EP/S026045/1 and EP/T003553/1, EP/N014588/1, EP/T017961/1, the Wellcome Innovator Awards 215733/Z/19/Z and 221633/Z/20/Z, the European Union Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No.777826 NoMADS, the Cantab Capital Institute for the Mathematics of Information and the Alan Turing Institute. AIAR gratefully acknowledges the support from Yau Mathematical Sciences Center, Tsinghua University.