K-Syn: K-space Data Synthesis in Ultra Low-data Regimes

• URL: http://arxiv.org/abs/2509.06997v1
• Date: Thu, 04 Sep 2025 12:25:05 GMT
• Title: K-Syn: K-space Data Synthesis in Ultra Low-data Regimes
• Abstract summary: This letter focuses on feature-level modeling in the frequency domain, enabling stable and rich generation even with ultra low-data regimes.<n>We integrate k-space data across time frames with multiple fusion strategies to steer and further optimize the generative trajectory.<n> Experimental results demonstrate that the proposed method possesses strong generative ability in low-data regimes.
• Score: 0.7817545394809559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Owing to the inherently dynamic and complex characteristics of cardiac magnetic resonance (CMR) imaging, high-quality and diverse k-space data are rarely available in practice, which in turn hampers robust reconstruction of dynamic cardiac MRI. To address this challenge, we perform feature-level learning directly in the frequency domain and employ a temporal-fusion strategy as the generative guidance to synthesize k-space data. Specifically, leveraging the global representation capacity of the Fourier transform, the frequency domain can be considered a natural global feature space. Therefore, unlike traditional methods that use pixel-level convolution for feature learning and modeling in the image domain, this letter focuses on feature-level modeling in the frequency domain, enabling stable and rich generation even with ultra low-data regimes. Moreover, leveraging the advantages of feature-level modeling in the frequency domain, we integrate k-space data across time frames with multiple fusion strategies to steer and further optimize the generative trajectory. Experimental results demonstrate that the proposed method possesses strong generative ability in low-data regimes, indicating practical potential to alleviate data scarcity in dynamic MRI reconstruction.

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