Title: D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction
Venue: NeurIPS 2025
Accepted: yes
Keywords: Magnetic Resonance Imaging

Test-time Adaptation

Implicit Neural Representation

Self-supervised Learning
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes D2SA, a two-stage test-time adaptation (TTA) framework for MRI reconstruction. The goal is to improve model performance when faced with distribution shifts between training and testing data. The method first employs a patient-wise adaptation stage using an MRI-specific Implicit Neural Representation (MR-INR) to capture global mean and variance shifts for a given patient. This is followed by a second stage that performs single-slice refinement. This refinement stage freezes most of the base network and the learned patient-level latent code for efficiency while fine-tuning the MR-INR and a novel, learnable Anisotropic Diffusion (AD) module to preserve structural details. The authors evaluate D2SA across five simulated domain shift scenarios, showing consistent performance improvements over several TTA baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. The paper addresses the highly relevant and practical problem of domain shifts in medical imaging, which is a significant barrier to the real-world deployment of deep learning models.\n2. The experimental setup is a clear strength. The authors have commendably evaluated their method across five different, well-motivated domain shift scenarios and against a suite of relevant TTA baselines.\n3. The paper is well-structured and generally easy to read. The figures, particularly Figure 1 and Figure 2, effectively communicate the core concepts and the architecture of the proposed framework.\n\nWeaknesses\n1. The primary weakness is that the D2SA framework, while elaborate, appears to be a complex combination of several known techniques. Implicit Neural Representations (INRs), test-time feature modulation, self-supervised losses, and anisotropic diffusion are all existing concepts. While their combination is novel, the paper lacks a core conceptual breakthrough. The contribution feels more like a sophisticated engineering effort than a fundamental advance. The complexity of this multi-stage, multi-component system is very high, which may act as a barrier to adoption.\n2. Due to the method's complexity, it is difficult to ascertain the primary drivers of the performance improvement. While the ablation studies are helpful, they don't fully disentangle the contributions. For example, the visual improvements shown in Figure 5 are attributed to the full \"Our\" method (FINE+MR-INR+SST+AD). It is unclear how much of this improvement is simply due to the powerful SST+AD refinement stage, versus the full two-stage pipeline. This makes it hard to judge the true value of the patient-wise adaptation stage.\n\nQUESTIONS:\n1. The core of the paper is the two-stage design. Could you provide an ablation study that compares a simpler baseline (e.g., FINE) directly with that same baseline enhanced only by Stage 2 (i.e., FINE+SST+AD)? This would help clarify whether the complex Stage 1 patient-wise adaptation is truly necessary or if most of the benefit comes from the powerful slice-refinement stage alone.\n2. The proposed system involves numerous components and corresponding hyperparameters (e.g., loss weights λ_self, λ_INR, λ_reg; latent code variance σ, etc.). The paper does not discuss the sensitivity of the results to these choices. How robust is D2SA to variations in these hyperparameters? A brief analysis would be crucial for assessing the method's practical usability and reproducibility.\nComputational Overhead vs. Gain: While adaptation time is reported, what is the increase in parameter count and computational complexity (e.g., FLOPs) of the D2SA framework compared to a baseline like FINE? A more direct comparison would help readers assess if the marginal performance gains in some scenarios justify the added model and computational complexity\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a dual-stage Test-Time Adaptation (TTA) method (D2SA) to address domain shifts in MRI reconstruction.\n\nStage 1 (distribution-level adaptation) uses MRI implicit neural representations (MR-INR) to adapt to patient-specific distribution shifts.\n\nStage 2 (slice-level adaptation) employs anisotropic diffusion modules to refine reconstructions at the slice level, preserving structural fidelity.\n\nIt demonstrates consistent improvements across various MRI distribution shifts (anatomy, modality, dataset, sampling, and acceleration).\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n**Good experimental validation**\n\nThe paper presents extensive quantitative evaluations under multiple distribution shifts, demonstrating performance improvements. Visual results provided in the appendix are convincing and clearly show the effectiveness of the proposed method in comparison to existing baselines like DIP-TTT and ZS-SSL.\n\n**Effective combination of existing techniques**\nIntegrates Implicit Neural Representations (INR) and anisotropic diffusion, both proven techniques, to enhance reconstruction performance and efficiency. It achieves notable efficiency improvements (reduced adaptation time compared to ZS-SSL and DIP-TTT), making the method practical for real-world scenarios.\n\n\nWeakness \n\nx. The authors acknowledge inspirations from existing works (e.g., DIP-TTT, Functa, and SSDU), but the paper could explicitly discuss the novelty more clearly, especially distinguishing from recent diffusion-based approaches. \n\nx. The authors introduce an additional layer of complexity by using the INR framework to handle patient-level distribution adaptation.\nHowever, INR requires careful hyperparameter tuning (latent vector size, network depth, initialization) to avoid instability issues. Hence, a clearer analysis of hyperparameter sensitivity and computational complexity could improve transparency and could help understand the practical implications of this complexity better.\n\nQUESTIONS:\nCan the authors clarify explicitly what distinguishes their method fundamentally from existing slice-wise refinement or TTA methods, beyond merely combining MR-INR and anisotropic diffusion?\n\nHow sensitive is the method to hyperparameter choices such as latent code dimensionality or anisotropic diffusion parameters?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a dual-stage test-time adaptation framework for fMRI reconstruction in the compressed sensing domain. The first stage is to adapt a model trained on one domain to a new patient more generally, and the second refines the model to the individual slices of that patient. Ablation studies and results across five datasets indicate that the each of the authors' stages and modules contributes to performance improvements in terms of SSIM and PSNR.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\\\nI think the authors introduce original modifications to the field of test-time adaptation, I like the idea of separating out the patient adaptation and single-slice refinement. Moreover, the authors' results seem to indicate a significant improvement over previous works, although I would think it strengthens the work even more if the authors actually performed significance tests for their results. Especially compared to DIP-TTT, some of the results come close, and it is unclear whether these results are significant. All-in-all, I think this work can be a significant contribution to the field if the authors address the issues below in their rebuttal.\n\n**Major weaknesses**\\\nI think the major weakness for this paper is that the quality and clarity need to be significantly improved before I can recommend this paper for acceptance. The introduction of the method is quite unclear, and I found it hard to follow exactly how all of the authors' improvements fit together into a larger framework. Specifically, the explanation of the AD module and section 4.2&4.3 are unclear. An example of an unclear paragraph are lines 124-128. Moreover, this extends to Figure 4, which is incredibly hard to read, is badly formatted, and I can therefore not verify their U-Net results. It is also unclear to me why the results are presented differently for the VarNet (table 2) and U-net architecture (Figure 4). Moreover, why are the ablations purely done on the U-Net architecture when both architectures were used in the paper?\n\n**Minor weaknesses**\\\nThe authors cite the following paper: \"Among various designs [15], SIREN remains a strong choice ...\". However, in the paper that the authors refer to, the discussion states the following:\n\"For applications where computational efficiency is less critical and the highest reconstruction quality is\nneeded, Incode would be the preferred choice. However, for real-time applications or cases where a balance\nbetween quality and speed is crucial, Fr and Finer offer strong alternatives with their adaptable frequency\nhandling and more manageable computation times. Siren can be a good option when tasks require periodic\nsignal reconstruction but with less variability in frequency content. One area where INR methods still face\nchallenges is scalability, particularly in handling extremely high-resolution or highly detailed tasks. Future\nwork could explore more efficient frequency encoding mechanisms that reduce the computational overhead\nwithout sacrificing quality. Additionally, optimising activation functions to be more adaptive to task-specific\nrequirements could improve generalisation across diverse datasets and applications. Enhancing the dynamic\nadaptability of models like Finer could allow for more robust performance across tasks that involve varying\nlevels of detail, potentially improving their usability in real-world scenarios.\"\nIt seems like INCODE, Fr, and Finer are potentially better backbone models for this specific task because I am unsure why we would expect more periodic signal reconstruction, but less variability in the frequency content. Moreover, in their paper, (Table 2), the medical imaging results indicate that Fr would be a much better choice. Can the authors comment on this or potentially perform an ablation study to test these effects?\n\n**Spelling/grammar**\\\nL3: \"... this discrepancies ...\" -> these \\\nL15-16: \"... can integrate well with various self-supervised (SSL) framework, ...\" -> frameworks \\\nL126: \"... and Fourier feature ...\" -> features \\\nL138: \"... before last layer ...\" -> the last layer \\\nL166: \"... given an set of feature u, ...\" -> given a set of features u \\\nL181: \"In calculation of AD equation ...\" -> the AD equation\n\nQUESTIONS:\nAs mentioned in the strengths and weaknesses section, I think the following questions are important to answer:\n1. Why are the ablations purely done on the U-Net architecture when both architectures were used in the paper?\n2. Why did the authors choose SIREN when the paper they cite seems to imply that other architectures could have been better choices?\n3. Are the results significant?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}