Title: Towards Prospective Medical Image Reconstruction via Knowledge-Informed Dynamic Optimal Transport
Venue: NeurIPS 2025
Accepted: yes
Keywords: prospective medical image reconstruction

dynamic optimal transport

inverse problem

imaging knowledge-informed
Abstract: Medical image reconstruction from measurement data is a vital but challenging inverse problem. Deep learning approaches have achieved promising results, but often requires paired measurement and high-quality images, which is typically simulated through a forward model, i.e., retrospective reconstruction. However, training on simulated pairs commonly leads to performance degradation on real prospective data due to the retrospective-to-prospective gap caused by incomplete imaging knowledge in simulation. To address this challenge, this paper introduces imaging Knowledge-Informed Dynamic Optimal Transport (KIDOT), a novel dynamic optimal transport framework with optimality in the sense of preserving consistency with imaging physics in transport, that conceptualizes reconstruction as finding a dynamic transport path. KIDOT learns from unpaired data by modeling reconstruction as a continuous evolution path from measurements to images, guided by an imaging knowledge-informed cost function and transport equation. This dynamic and knowledge-aware approach enhances robustness and better leverages unpaired data while respecting acquisition physics. Theoretically, we demonstrate that KIDOT naturally generalizes dynamic optimal transport, ensuring its mathematical rationale and solution existence. Extensive experiments on MRI and CT reconstruction demonstrate KIDOT's superior performance. Code is available at https://github.com/TaoranZheng717/KIDOT.


Peer reviews:
comments: SUMMARY:
This paper proposes a new framework for prospective medical image reconstruction. It frames reconstruction as a dynamic transport process, and its learned path is explicitly constrained by imaging physics. Theoretical analysis is performed to validate the existence and optimality of the solution. Experiments on simulated and real MRI/CT datasets demonstrate the effectiveness of the proposed method.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The formulation of prospective medical image reconstruction based on dynamic transport process is reasonable and intriguing.
2. The proposed framework is supported by sufficient theoretical analysis, which builds a solid basis for following development.
3. The proposed method is compared with existing methods on simulated and real MRI/CT datasets, showing the effectiveness of the proposed method.
4. The authors promise the open access to the real prospective datasets, which can benefit the medical imaging community.
5. The paper organization is clear.

Weaknesses:
My concern is mainly about the transport network implementation. The authors use different implementations for the transport network on different tasks, which might be unfair to other comparing methods to some extent. What if using a fixed implementation of the transport network?

QUESTIONS:
In Table 4, what is the difference between the last two rows, namely (3) and (4). This is a little confusing. Please make it more clear.

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 4
IMPACT: 4
ORIGINALITY: 4
DATE: 2025-06-08

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces KIDOT, a novel framework for prospective medical image reconstruction. The core idea is to frame reconstruction as a dynamic optimal transport (OT) problem, where the evolution from a degraded measurement to a high-quality image is modeled as a continuous path. Crucially, this transport path is guided by imaging physics, incorporated into both a novel instantaneous cost function and the transport dynamics equation itself. Experiments demonstrate its superiority on simulated and real-world prospective MRI and CT data.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper is well-motivated and tackles a critical and practical problem in prospective medical image reconstruction. The proposed method, Knowledge-Informed Dynamic Optimal Transport (KIDOT), is novel and logically sound. It offers a more principled, physics-grounded approach.\n2. This paper clearly introduces and validates the proposed theory, grounding the method in the theory of dynamic optimal transport. The formulation of the imaging-informed cost function and the physics-guided transport equation is elegant.\n3. Extensive experiments are conducted, and the proposed method demonstrates state-of-the-art performance. The ablation studies are comprehensive and effectively validate the contribution of their designs.\n4. This paper validates the proposed method on both simulated and real, prospectively acquired MRI and CT datasets, which demonstrates the robustness and practical relevance of the method.\n5.  This paper is very well-written. The motivation is clearly articulated, and the complex methodology is broken down into understandable components. \n\nWeaknesses:\n1. The authors do not explain how the number of discrete time steps is determined for the experiments. And what is the impact on the performance when N increases? Is there a trade-off between the number of steps and reconstruction quality?\n2. Although the proposed method achieves the best performance among the compared baselines, most of the compared methods are relatively outdated. It would be beneficial to include comparisons with recent optimal transport-based methods, such as Feedback Schrödinger Bridge Matching.\n3. A discussion comparing the strengths and weaknesses of the proposed method relative to recent optimal transport-based methods would further clarify its advantages and limitations, such as the high relevant FSBM.\n\nQUESTIONS:\n1. The authors performed experiments on real-world CT datasets and described their data collection process. Will these datasets be made publicly available? If not, are there any existing public datasets that can be used to further evaluate the method to improve reproducibility?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces KIDOT, a dynamic optimal transport framework for medical image reconstruction that integrates imaging physics directly into both the cost function and transport dynamics. The method is designed to work with unpaired data and aims to address the retrospective-to-prospective domain gap, with experiments on MRI and CT showing improved perceptual and fidelity metrics.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n1.\tThe supervision assumption is less restrictive than fully supervised approaches, as it does not require paired measurement-image data. This aligns well with real-world clinical scenarios, where perfectly aligned scans are often unavailable or impractical to obtain.\n2.\tTheoretical analysis is provided to support the formulation, including proof of existence of solutions under mild assumptions.\n3.\tThe method was tested in various settings, focusing on both simulated and prospective MRI data, and clinical prospective CT data.\n\n**Weaknesses:**\n\n1.\tKIDOT is claimed to work without paired data, but it still requires thousands of training slices based on the numbers reported in the paper. There is no evaluation of performance in low-data regimes, which are often the reality in clinical settings. This weakens the motivation for unpaired learning, especially since large paired datasets (such as fastMRI, mridata.org and others) already exist and are widely used.\n2.\tWhile KIDOT is proposed as a solution for unpaired and prospective data, the paper does not compare it to diffusion-based reconstruction methods that use strong priors trained on large datasets like fastMRI. Even though these models are trained with paired data, they are often robust to distribution shifts and can perform well on prospective data if the prior matches the anatomy or modality, which is commonly the case in practice.\n3.\tAlthough KIDOT is trained on unpaired data, the paper does not explore how well the model generalizes to more aggressive out-of-distribution (OOD) examples, such as different contrasts, anatomies, or acquisition protocols. Evaluating under such conditions would strengthen the paper’s premise, since the core motivation of KIDOT is to address the retrospective-to-prospective gap, which is itself a form of distribution shift.\n\nQUESTIONS:\n1.\t*Regarding weakness 1:* Can the authors provide proof for KIDOT’s superior performance in low-data regimes?\n2.\t*Regarding weakness 2:* Prospective image for simulated data was generated by flipping 3% of the entries in the training mask, essentially just changing the undersampling pattern slightly. Diffusion-based reconstruction methods, which are typically trained independently of the sampling mask, are known to be inherently robust to such variations. Would KIDOT still provide an advantage over these models under a more meaningful shift, and why are they not included in the comparison?\n3.\t*Regarding weakness 2:* Would a diffusion model trained on a large existing dataset, such as DDS [1] trained on fastMRI brain data, perform competitively on this prospective dataset at test time? If so, this would suggest that we can still collect prospective data in an unpaired fashion and reconstruct it using strong pre-trained priors, without the need for a method like KIDOT.  Exploring this would help clarify whether KIDOT provides a real advantage in clinical settings.\n4.\t*Regarding weakness 3:* Unpaired training is often motivated by the ability to use heterogeneous data sources, so how would KIDOT perform when the source and target domains differ more significantly, for example in contrast type, anatomy, or acquisition settings?\n\n[1] Chung et al., “Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems”, ICLR, 2024.\n\nLIMITATIONS:\nYes, the authors have addressed the limitations in Section 5.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}