Advances in deep learning have significantly enhanced medical image analysis, yet the availability of large-scale medical datasets remains constrained by patient privacy concerns. We present EchoFlow, a novel framework designed to generate high-quality, privacy-preserving synthetic echocardiogram images and videos. EchoFlow comprises four key components: an adversarial variational autoencoder for defining an efficient latent representation of cardiac ultrasound images, a latent image flow matching model for generating accurate latent echocardiogram images, a latent re-identification model to ensure privacy by filtering images anatomically, and a latent video flow matching model for animating latent images into realistic echocardiogram videos conditioned on ejection fraction. We rigorously evaluate our synthetic datasets on the clinically relevant task of ejection fraction regression and demonstrate, for the first time, that downstream models trained exclusively on EchoFlow-generated synthetic datasets achieve performance parity with models trained on real datasets. We release our models and synthetic datasets, enabling broader, privacy-compliant research in medical ultrasound imaging at https://huggingface.co/spaces/HReynaud/EchoFlow.

翻译：深度学习的发展显著提升了医学图像分析能力，然而大规模医学数据集的获取仍受限于患者隐私问题。本文提出EchoFlow，一种用于生成高质量、保护隐私的合成超声心动图图像与视频的新型框架。EchoFlow包含四个核心组件：用于定义心脏超声图像高效潜在表征的对抗变分自编码器，用于生成精确潜在超声心动图图像的潜在图像流匹配模型，通过解剖学特征过滤图像以确保隐私的潜在重识别模型，以及基于射血分数条件将潜在图像动态生成为逼真超声心动图视频的潜在视频流匹配模型。我们在射血分数回归这一临床相关任务上对合成数据集进行严格评估，并首次证明：仅使用EchoFlow生成的合成数据集训练的下游模型，其性能达到与真实数据集训练模型相当的水平。我们在https://huggingface.co/spaces/HReynaud/EchoFlow 发布了模型与合成数据集，以推动更广泛、符合隐私规范的医学超声成像研究。