Physics-Guided Flow Matching for CT Image Reconstruction

Davide Evangelista

cs.AIAug 28, 2026

Physics-Guided Flow Matching for CT Image Reconstruction

Authors: Davide Evangelista

Organizations: Department of Computer Science and Engineering University of Bologna Bologna, 40126, Italy

Abstract

Deep generative models have recently emerged as powerful priors for solving ill-posed inverse problems in CT, with diffusion-based approaches achieving state-of-the-art reconstruction performance. However, diffusion models typically rely on stochastic sampling procedures, long inference trajectories, and carefully tuned noise schedules, which can limit computational efficiency and numerical stability, especially at high spatial resolutions. In this work, we investigate Flow Matching as an alternative generative prior for CT reconstruction. We train a high-resolution Rectified Flow Matching model on 256x256 chest images from the Mayo Clinic Low-Dose CT dataset. To mitigate overfitting and limited anatomical variability, we employ a two-stage training strategy consisting of an initial phase with strong, anatomically informed data augmentation, followed by a fine-tuning phase with reduced or no augmentation to refine structural fidelity. The resulting model is capable of generating high-quality and anatomically coherent CT-like images, serving as a strong learned prior. We then evaluate multiple reconstruction methods specifically designed for Flow Matching models, including Plug-and-Play Flow, FlowDPS, Flower, and Flow-Priors (ICTM), and compare them against state-of-the-art diffusion-based reconstruction algorithms such as DDRM, DPS, and DiffPIR. Experimental results across several CT inverse problem settings show that Flow Matching-based approaches consistently outperform diffusion-based methods in terms of PSNR, SSIM, and perceptual quality, while requiring fewer sampling steps. Finally, we publicly release the trained Flow Matching model and accompanying code to facilitate reproducibility and future research. Overall, this work demonstrates that Flow Matching provides a stable, efficient, and effective alternative to diffusion models for high-resolution CT image reconstruction.

Figures & tables

Figure 1: Graphical overview of the proposed Flow Matching model and its use as a generative prior for CT reconstruction.

Panel of Figure 2 — Figure 2: Samples generated by MedicalFM starting from random noise $\boldsymbol{x}_{0}\sim\mathcal{N}(\boldsymbol{0},\boldsymbol{I})$ : (a) after training with strong augmentation; (b) after fine-tuning with reduced augmentation.

Figure 3: Kernel Inception Distance (KID) for unconditional CT image generation at increasing numbers of generation steps. We report mean and standard deviation across runs for Flow Matching after the first training stage (strong augmentation), after the second fine-tuning stage, and for a diffusion-based baseline evaluated under the same number of steps.

	Method	$\nu_{t}$	PSNR	SSIM	LPIPS
Angles = $60$	DPS	$0.01$	$28.51$	$0.8128$	$0.3530$
	DDRM	–	$35.30$	$0.8934$	$0.2768$
	FlowDPS	$5.0$	$\mathbf{36.19}$	$\mathbf{0.9162}$	$\mathbf{0.1759}$
	PnP-Flow	$0.4$	$34.18$	$0.8819$	$0.2590$
	FLOWER	$50.0$	$34.47$	$0.8382$	$0.2724$
	ICTM	$0.9$	$34.51$	$0.8598$	$0.2878$

Table 1: Reconstruction results (PSNR

\uparrow

, SSIM

\uparrow

, LPIPS

\downarrow

) for different numbers of projection angles.

Panel of Figure 4 — Figure 4: Qualitative comparison of sparse-view CT reconstruction results. Rows correspond to different angular undersampling scenarios ( $60$ , $90$ , and $120$ projection angles). Columns display the Ground Truth, diffusion-based baselines (DPS, DDRM), and the evaluated Flow Matching methods (FlowDPS, PnP-Flow, FLOWER, ICTM). The red insets highlight fine anatomical details, demonstrating that Flow Matching priors (particularly FlowDPS and PnP-Flow) consistently recover sharper structures and minimize artifacts compared to diffusion models, especially in the challenging $60$ -angle setting.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

		C081-45			C081-79
	Method	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS
Angles = $60$	DPS	$28.36$	$0.7956$	$0.3399$	$28.13$	$0.7724$	$0.3413$
	DDRM	$35.63$	$0.8943$	$0.2660$	$34.57$	$0.8571$	$0.2765$
	FlowDPS	$\mathbf{36.50}$	$\mathbf{0.9182}$	$\mathbf{0.1766}$	$\mathbf{35.28}$	$\mathbf{0.9043}$	$\mathbf{0.2037}$
	PnP-Flow	$34.17$	$0.8838$	$0.2450$	$33.48$	$0.8739$	$0.2607$
	FLOWER	$34.74$	$0.8382$	$0.2724$	$34.39$	$0.8558$	$0.2415$

Appendix

Table 2: Reconstruction results (PSNR

\uparrow

, SSIM

\uparrow

, LPIPS

\downarrow

) for samples C081-45 and C081-79 across different projection angles.

Panel of Figure 5 — Figure 5: Visual comparison for Sample C081-45 across three sparse-view configurations. The red box indicates the zoomed region shown in the inset.

Panel of Figure 6 — Figure 6: Visual comparison for Sample C081-79 across three sparse-view configurations. The red box indicates the zoomed region shown in the inset.

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