Title: Fast MRI for All: Bridging Access Gaps by Training without Raw Data
Venue: NeurIPS 2025
Accepted: yes
Keywords: Computational Imaging

Fast MRI

Unsupervised Learning

Compressed Sensing

Deep Learning

Access
Abstract: Physics-driven deep learning (PD-DL) approaches have become popular for improved reconstruction of fast magnetic resonance imaging (MRI) scans. Though PD-DL offers higher acceleration rates than existing clinical fast MRI techniques, their use has been limited outside specialized MRI centers. A key challenge is generalization to rare pathologies or different populations, noted in multiple studies, with fine-tuning on target populations suggested for improvement. However, current approaches for PD-DL training require access to raw k-space measurements, which is typically only available at specialized MRI centers that have research agreements for such data access. This is especially an issue for rural and under-resourced areas, where commercial MRI scanners only provide access to a final reconstructed image. To tackle these challenges, we propose Compressibility-inspired Unsupervised Learning via Parallel Imaging Fidelity (CUPID) for high-quality PD-DL training using only routine clinical reconstructed images exported from an MRI scanner. CUPID evaluates output quality with a compressibility-based approach while ensuring that the output stays consistent with the clinical parallel imaging reconstruction through well-designed perturbations. Our results show CUPID achieves similar quality to established PD-DL training that requires k-space data while outperforming compressed sensing (CS) and diffusion-based generative methods. We further demonstrate its effectiveness in a zero-shot training setup for retrospectively and prospectively sub-sampled acquisitions, attesting to its minimal training burden. As an approach that radically deviates from existing strategies, CUPID presents an opportunity to provide broader access to fast MRI for remote and rural populations in an attempt to reduce the obstacles associated with this expensive imaging modality. Code is available at https://github.com/ualcalar17/CUPID.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents CUPID, a novel framework to train accelerated MRI reconstruction models using only clinically available reconstructed images, which circumvents the common need for raw k-space data access. The method's core innovation is an unsupervised loss function that pairs a compressibility term with a parallel imaging fidelity term, stabilized by specially designed input perturbations. By achieving results comparable to raw-data-dependent methods, CUPID aims to bridge the equity gap and enable advanced fast MRI in underserved clinical settings.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: The work addresses a critical, real-world barrier to the adoption of AI in medicine—the lack of raw data access in many clinical environments. The focus on improving equity makes the contribution highly significant. The proposed parallel imaging fidelity loss, which uses perturbation equivariance to enforce data consistency without the data itself, is a clever and highly original contribution to unsupervised, physics-informed learning. The method is rigorously evaluated against a wide range of state-of-the-art techniques across multiple datasets and imaging contrasts. The inclusion of both retrospective and prospective experiments, as well as a specific out-of-distribution test, provides strong support for the claims.\n\nWeaknesses:\n1. Insufficient Intuition on Perturbation Design: While effective, the paper lacks a deep theoretical explanation for the perturbation design, relying more on empirical justification. A more formal analysis would strengthen the work. \n\n2.Performance Limits Unexplored: The method's reliance on an initial, artifact-laden parallel imaging reconstruction is a key dependency. The paper does not investigate how performance degrades as the quality of this initial input worsens, for example, at very high acceleration rates. \n3. Lack of Clinical Pathology Validation: The evaluation focuses on structural metrics (PSNR/SSIM) in largely healthy subjects. For clinical translation, an analysis showing the preservation of subtle pathological details and avoiding diagnostic omissions or \"hallucinations\" would be necessary.\n\nQUESTIONS:\n1.Could you provide more theoretical intuition on the design of the perturbations? What are the essential properties required for them to effectively regularize the network beyond simply being resolvable by parallel imaging?\n\n2.How does CUPID's performance degrade at acceleration rates higher than 4x, where the initial parallel imaging reconstruction serving as input would be substantially poorer?\n\n3.How would the framework perform in a common clinical scenario where separate coil calibration scans are unavailable and sensitivities must be estimated directly from the aliased images?\n\n4.For the practical zero-shot application, what is the approximate training/inference time per slice?\n\nLIMITATIONS:\nThe authors provide a dedicated limitations section in the appendix that discusses practical issues like hardware constraints, the challenge of coil sensitivity estimation, and the impact of unknown vendor filtering. For improvement, the authors could further analyze the method's performance limits when the initial parallel imaging reconstruction is severely degraded and discuss the data security implications of off-site processing, which is suggested as a workaround for local hardware limitations.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces CUPID (Compressibility-inspired Unsupervised Learning via Parallel Imaging Fidelity), a novel method for training physics-driven deep learning (PD-DL) MRI reconstruction networks without raw k-space data. \nCUPID leverages DICOM format images to enable unsupervised or zero-shot training. This approach is specifically designed to address the equity gap in access to state-of-the-art fast MRI, especially in rural and underserved areas where access to raw MRI data is typically unavailable due to vendor restrictions. \nThe method combines a compressibility-based loss term with a novel parallel imaging fidelity loss, aiming to ensure both output quality and physical consistency. Comprehensive experiments demonstrate that CUPID matches the performance of supervised and self-supervised methods requiring raw data and outperforms compressed sensing and generative diffusion methods, both retrospectively and prospectively. The paper argues that CUPID provides an avenue for democratizing advanced MRI acceleration technologies.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- This paper introduces a fundamentally new and practical solution to the lack of raw k-space data for fast MRI training/fine-tuning.\n- The authors provide thorough experiments, which demonstrate strong performance, on par with supervised/self-supervised methods requiring raw data, across several datasets and scenarios.\n- The approach combines a compressibility-inspired loss and a parallel imaging fidelity term based on perturbation equivariance, addressing a key failure mode of previous unsupervised training.\n- CUPID supports subject-specific (zero-shot) fine-tuning and is compatible with DICOM, making it truly practical.\n- The paper is well-written and organized.\n\nWeaknesses:\n- The paper does not appear to report the training cost or computational efficiency of CUPID compared to prior supervised or self-supervised PD-DL methods. As practical deployment in resource-limited settings is a key motivation, it would be helpful to provide quantitative details on training time, GPU/memory requirements, and any trade-offs with zero-shot adaptation. This information is important for evaluating real-world feasibility.\n- Figure 2 is a central illustration of the CUPID framework, but the figure and accompanying caption are difficult to read due to small font size and insufficient explanation of each component.\n\nQUESTIONS:\n1. In several qualitative figures, CUPID’s reconstructions appear noticeably smoother than the original images and competing methods (e.g., Figs. 3–5). Is this an inherent effect of the compressibility loss (possibly over-regularizing high-frequency details), or a consequence of DICOM-specific preprocessing? Have the authors quantitatively evaluated the potential loss of diagnostic high-frequency features (such as small lesions or vessel boundaries) and discussed the implications for clinical utility? Greater discussion and, if possible, radiologist assessment or frequency-domain analysis would strengthen the work.\n2. Question about loss normalization and the trade-off parameter \\lambda: \nSince the compressibility loss and parallel imaging fidelity loss do not appear to be normalized, the choice of \\lambda may be sensitive to dataset or architecture changes. Did you consider normalizing or automatically balancing these loss terms? Would such normalization make CUPID more robust and easier to use across different settings?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an unsupervised MRI reconstruction technique that leverages only undersampled, clinically reconstructed images for training that are readily available in routine scans. Thus, training is possible without modifying the scanning protocols, typically only possible in hospitals involved in academic research. Experiments on undersampled knee and brain MRI images show promising results compared to other untrained methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- This work has potential for practical impact. Obtaining raw, fully-sampled kspace data from scanners at large scale is challenging and requires close collaboration with hospitals. Thus, reconstruction techniques that can leverage more easily accessible data from the scanners are highly valuable.\n- The proposed methodology is sensible and the experiments demonstrate clear improvements over untrained baselines in the investigated settings.\n- The paper is mostly well-written and easy to follow. \n\nWeaknesses:\n- The experiments are limited to a single undersampling ratio and scanning pattern. It would be interesting to see how CUPID performs in more diverse scenarios, such as random sampling instead of equidistant (or other popular scanning patterns), and higher/lower acceleration ratios (say R=2 and 8). \n- It would be crucial to have the reconstructions evaluated by radiologists and compared to traditional reconstructions in clinical practice. Promising evaluation from experts would provide strong support for this work.\n- Minor: typo on line 144, font size in Fig. 2 is too small.\n\nQUESTIONS:\n- How does CUPID compare to baselines under different scan patterns and acceleration ratios? Moreover, comparison of reconstruction times/compute effort required would be useful.\n- Are the reconstructions clinically valuable besides the promising distortion metrics?\n\nLIMITATIONS:\nLimitations are discussed in the Appendix.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces CUPID (Compressibility-inspired Unsupervised Learning via Parallel-Imaging Fidelity), a training strategy that lets physics-driven deep-learning (PD-DL) MRI reconstructions be fine-tuned or even trained from scratch using only routine scanner reconstructions (DICOMs) instead of raw k-space. CUPID (i) enforces compressibility with a re-weighted ℓ1 loss and (ii) adds an equivariant “parallel-imaging fidelity” loss based on carefully crafted, non-aliasing perturbations, so the network cannot collapse to a trivial sparse solution. Because it needs only DICOMs, CUPID could facilitate fast-MRI in settings where vendors will not release raw data. One thing i really like is that this paper did prospective undersampled reconstruction study, thats really important for clinical application - though i didn't fully buy the results, also the ood experiments are informative.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper is kinda dense, but contains a wide range of experiments, reading it through, it might not be the most clear one, but i can get all the core ideas, i would say its a well written paper.\n2. As I mentioned before, I love the experiments and figures the authors presented for prospective undersampled reconstruction, instead of only evaluating on fast MRI, also the OOD experiments.\n\nWeaknessness:\n1. I didn't buy the idea of this rural region, you claimed that finetuning will be needed for those scanners, but i didn't see a valid proof of why this is the case, maybe one way to do is to collect some data from a different scanner and just show this point?\n2. I appreciate the prospective study, but to be honest, i didn't see an advantages of CUPID compared to other approaches, and there is only 1 figure showing that, i would appreciate more examples.\n3. Most the experiments are done with R=4, maybe try other undersampling factors?\n\nMinor comments\n1. Fig. 2 caption could define λ and K explicitly.\n2. Typo L288 “improved using a CUPID-trained” → “an”.\n3, Listing GPU hours in Appendix A would be helpful.\n\nQUESTIONS:\n1. I'm curious on the perturbation patterns, can it be also learned?\n\nLIMITATIONS:\nthe most important one is the improvements are subtle to me compared to other appraoches.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-12"}