Ultrasound offers a safe, cost-effective, and widely accessible technology for fetal brain imaging, making it especially suitable for routine clinical use. However, it suffers from view-dependent artifacts, operator variability, and a limited field of view, which make interpretation and quantitative evaluation challenging. Ultrasound compounding aims to overcome these limitations by integrating complementary information from multiple 3D acquisitions into a single, coherent volumetric representation. This work provides four main contributions: (1) We present the first systematic categorization of computational strategies for fetal brain ultrasound compounding, including both classical techniques and modern learning-based frameworks. (2) We implement and compare representative methods across four key categories - multi-scale, transformation-based, variational, and deep learning approaches - emphasizing their core principles and practical advantages. (3) Motivated by the lack of full-view, artifact-free ground truth required for supervised learning, we focus on unsupervised and self-supervised strategies and introduce two new deep learning based approaches: a self-supervised compounding framework and an adaptation of unsupervised deep plug-and-play priors for compounding. (4) We conduct a comprehensive evaluation on ten multi-view fetal brain ultrasound datasets, using both expert radiologist scoring and standard quantitative image-quality metrics. We also release the USFetal Compounding Toolbox, publicly available to support benchmarking and future research. Keywords: Ultrasound compounding, fetal brain, deep learning, self-supervised, unsupervised.

翻译：超声成像为胎儿脑部提供了一种安全、经济且广泛可及的成像技术，尤其适用于常规临床使用。然而，该方法存在视角依赖伪影、操作者依赖性以及视野有限等缺陷，使得图像解读与定量评估面临挑战。超声复合成像旨在通过将多次三维采集的互补信息整合为单一、连贯的体数据表示，以克服这些局限性。本研究作出以下四项主要贡献：（1）我们首次系统性地对胎儿脑部超声复合成像的计算策略进行分类，涵盖经典技术与现代基于学习的框架。（2）我们在四个关键类别——多尺度方法、基于变换的方法、变分方法及深度学习方法中，实现并比较了代表性方案，重点阐释其核心原理与实践优势。（3）针对监督学习所需的全视角、无伪影真实数据缺失的问题，我们聚焦于无监督与自监督策略，并引入两种新型基于深度学习的方法：一种自监督复合框架，以及一种适用于复合任务的无监督深度即插即用先验的适配方案。（4）我们在十个多视角胎儿脑部超声数据集上开展了综合评估，结合放射科专家评分与标准定量图像质量指标进行分析。同时，我们公开发布了USFetal复合成像工具箱，以支持基准测试与未来研究。关键词：超声复合成像，胎儿脑部，深度学习，自监督，无监督。