The development of high-resolution and highly sensitive small blood vessels visualization methods has great clinical significance for the early diagnosis and treatment monitoring of related tissue lesions. Different from traditional focused ultrasound, ultrafast ultrasound Doppler (μDoppler) imaging can detect instantaneous changes of small flows due to the framerate of several thousands. Effective tissue clutter filtering and noise suppression methods are crucial to the quality of μDoppler imaging. The commonly used clutter filtering method is the singular value decomposition ( SVD) method. SVD can separate tissue clutter and blood flow signal quickly by utilizing the difference in spatiotemporal coherence of components. However, it cannot effectively suppress noise. Here we propose a novel clutter filtering method based on generalised scalable robust principal component analysis (GSRPCA), using Schatten p norm and l q norm to strengthen the low-rank constraint and sparse constraint of the RPCA model, and enhance the extraction of blood flow signal. Rat cerebral blood flow imaging results demonstrate that GSRPCA can improve the imaging quality of blood vessels in power Doppler imaging, improving SNR by about 20 dB and improving CNR by about 10 dB compared with SVD. The results of brain functional ultrasound imaging shows that GSRPCA can improve the sensitivity of blood volume changes in small vessels. Relevant methods facilitate the study on clutter filtering methods in ultrafast ultrasound imagin Ke g.