WFM: 3D Wavelet Flow Matching for Ultrafast Multi-Modal MRI Synthesis

Yalcin Tur, Mihajlo Stojkovic, Ulas Bagci

Keywords: Medical image translation, modality synthesis

TL;DR: WFM enables real-time 3D MRI synthesis by replacing slow iterative diffusion with a single-step flow matching process initialized from an informed anatomical prior.

Abstract: Diffusion models have achieved remarkable quality in multi-modal MRI synthesis, but their computational cost (hundreds of sampling steps and separate models per modality) limits clinical deployment. We observe that this inefficiency stems from an unnecessary starting point: diffusion begins from pure noise, discarding the structural information already present in available MRI sequences. We propose WFM (Wavelet Flow Matching), which instead learns a direct flow from an \textit{informed prior}, the mean of conditioning modalities in wavelet space, to the target distribution. Because the source and target share underlying anatomy and differ primarily in contrast, this formulation enables accurate synthesis in just 1-2 integration steps. A single 82M-parameter model with class conditioning synthesizes all four BraTS modalities (T1, T1c, T2, FLAIR), replacing four separate diffusion models totaling 326M parameters. On BraTS 2024, WFM achieves 26.8 dB PSNR and 0.94 SSIM, within 1-2 dB of diffusion baselines, while running 250-1000x faster (0.16-0.64s vs. 160s per volume). This speed-quality trade-off makes real-time MRI synthesis practical for clinical workflows. Code is available at https://github.com/yalcintur/WFM.

Primary Subject Area: Image Synthesis

Secondary Subject Area: Application: Radiology

Registration Requirement: Yes

Reproducibility: https://github.com/yalcintur/WFM

Visa & Travel: No

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

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Submission Number: 356