Deep learning-based models have significantly advanced clinical ultrasound tasks by detecting anatomical structures within vast ultrasound image datasets. However, their remarkable performance inherently requires extensive training of annotated medical datasets. Few-shot learning addresses the challenge of limited labeled data for model training. Currently, few-shot learning in the field of medical image analysis mainly focuses on classification and semantic segmentation, with relatively fewer studies on object detection. In this paper, we propose a novel few-shot anatomical structure detection method in ultrasound images called TRR-CCM, which consists of Circular Channel Mamba (CCM) and Topological Relationship Reasoning (TRR) based on human anatomy knowledge. CCM, as a new Mamba variant, performs contextual modeling of anatomical structures and captures long- and short-term dependencies. TRR learns spatial topological relationships between human anatomical structures to further improve the accuracy of detection and localization. Experimental results on two fetal ultrasound datasets demonstrate that TRR-CCM outperforms 9 state-of-the-art baseline methods.