SVD-SN2N: Self-Inspired Noise2Noise Learning for Denoising Log-Compressed SVD-Filtered Ultrasound Imaging

Singular value decomposition (SVD) is the reference clutter-rejection strategy for medical ultrafast ultrasound imaging, yet SVD-filtered images retain a residual noise floor that obscures microvascular signals at depth. Supervised denoising cannot address this gap because clean references do not exist, and most physics-based alternatives require radio-frequency or in-phase/quadrature data that clinical scanners do not expose. We introduce SVD-SN2N, a self-supervised Noise2Noise framework that operates entirely on the post-SVD image and requires neither clean targets nor channel-level access. Our central contribution is to show that the residual fluctuations of SVD-filtered images are dominated by multiplicative speckle, which violates the zero-mean additive assumption of Noise2Noise theory, and that a single log-compression step converts it into an additive perturbation whose variance no longer depends on the signal, restoring the conditions the framework requires. The log-compressed image is split into two statistically independent half-size copies by diagonal 2×2 resampling, rescaled by Fourier zero-padding interpolation, and used to train a U-Net under a self-constrained twin-prediction loss. We assess the underlying assumptions directly by measuring the residual-noise distribution, its spatial correlation and the signal similarity within diagonal pixel pairs, and we validate the framework on four heterogeneous datasets spanning preclinical and clinical, contrast-enhanced and contrast-free, and 2-D to 3-D regimes. Relative to conventional SVD, SVD-SN2N raises image-derived SNR by 4.2–10.6 dB and narrows the apparent vessel full-width at half-maximum by 34%–59%, providing a post-SVD denoising front-end compatible with the image-only data available on clinical scanners.