Obstetric ultrasound image quality is crucial for accurate diagnosis and monitoring of fetal health. However, producing high-quality standard planes is difficult, influenced by the sonographer's expertise and factors like the maternal BMI or the fetus dynamics. In this work, we propose using diffusion-based counterfactual explainable AI to generate realistic high-quality standard planes from low-quality non-standard ones. Through quantitative and qualitative evaluation, we demonstrate the effectiveness of our method in producing plausible counterfactuals of increased quality. This shows future promise both for enhancing training of clinicians by providing visual feedback, as well as for improving image quality and, consequently, downstream diagnosis and monitoring.

翻译：产科超声图像质量对于胎儿健康的准确诊断和监测至关重要。然而，受操作者专业水平以及母体体重指数或胎儿动态等因素影响，生成高质量标准切面十分困难。在本研究中，我们提出利用基于扩散的反事实可解释人工智能，从低质量非标准切面生成真实的高质量标准切面。通过定量和定性评估，我们证明了该方法在生成质量提升的合理反事实图像方面的有效性。这为未来通过提供视觉反馈来加强临床医生培训，以及提升图像质量从而改善下游诊断和监测展现了前景。