## 1 Introduction  
Ultrafast compound Doppler imaging (UCDI) offers extraordinary capabilities for flow analysis and the quantification of small vessels, which contributes to the diagnosis of various diseases [1; 2; 3; 4]. The principle of UCDI is to replace the traditional B-mode scan sequence by compound plane wave (CPW) imaging to gather backscattered echoes coming from the full Field of View (FOV), which provides a large amount of spatial and temporal samples within a short period of time. The Doppler power or intensity of blood flow signals are extracted by clutter filtering before the blood flow image is displayed for clinical diagnosis. Although promising results of UCDI have been achieved from brains [5], eyes [6; 7], kidneys [8; 9] and livers [10; 11], UCDI remains a significant challenge for the visualization of small vessels with low flow speeds, because the tissue echoes and the blood scatterer echoes tend to share common characteristics. To that end, several clutter filtering techniques have been applied to facilitate the better separation of blood and tissue signals [10; 12; 13; 14]. One of the most recent approaches for clutter filtering is the spatiotemporal singular value decomposition clutter rejection, which was proposed by [PERSON] et al. in 2015 [10]. The singular value decomposition (SVD) takes advantage of the different characteristics of the spatiotemporal coherence between tissue and blood motion and, hence, substantially increases the Doppler sensitivity from fine vessels. Blood flow and tissue signals (and to some extent, noise) can be separated by low-rank matrix projection, such as singular value thresholding (SVT). To achieve better outputs, high-order singular value thresholding based on principles of the Marcenko-Pastur distribution was proposed by [PERSON] et al. to suppress high-frequency noise coupled with the blood flow signal [15; 16]. Although clutter filtering with both low- and high-order SVTs strongly outperform conventional clutter rejection filters, in practice, it appears to be challenging to differentiate blood flow signals from the noise. This leads to a limitation of these methods that a complicated calibration step is required to determine the high-order cutoff threshold before the measurement or quantification of blood flow imaging, which negatively affects the stability and robustness of these approaches [10; 15]. On the other hand, ultrasonic tissue characterization techniques (UTC), which aim to untangle hidden patterns in pulse-echo data to extract additional information regarding the tissue functions and the pathology, have been a research focus for several decades. Several UTC methods have been proposed [17; 18]. More recently (2016), [PERSON] et al. proposed the H-scan ultrasound imaging, which enables the recognition of an added dimension, as each region of the echoes are categorized into large layer scatterers, thin layer scatterers and Rayleigh scatterers, as determined by the impedance function present in tissue along the direction of the propagating pulse [19; 20]. [PERSON] implemented the H-scan technique with the plane wave imaging instrumentation and investigated the impact of spatial angular compounding on H-scan image quality [21]. Their experimental results showed that H-scan US imaging using coherent compounding plane waves (CPW) is a promising method to visualize the relative size and distribution of acoustic scattering sources. The promising results were achieved in monitoring early tumors using H-scan ultrasound imaging, e.g., breast cancer [22; 23; 24] and thyroid lesions [25]. Based on the classification theory for tissue scattering, this paper attempts to propose an H-scan subtraction Doppler imaging method by combining the H-scan method and UCDI approach to bypass the limitations associated with the existing UCDI approaches and improve the image quality of the fine vessels. First, H-scan is used to distinguish the tissue-scattering characteristics from the FOV.