  The complex structure of the heart leads to significant challenges in
echocardiography, especially in acquisition cardiac ultrasound images.
Successful echocardiography requires a thorough understanding of the structures
on the two-dimensional plane and the spatial relationships between planes in
three-dimensional space. In this paper, we innovatively propose a large-scale
self-supervised pre-training method to acquire a cardiac structure-aware world
model. The core innovation lies in constructing a self-supervised task that
requires structural inference by predicting masked structures on a 2D plane and
imagining another plane based on pose transformation in 3D space. To support
large-scale pre-training, we collected over 1.36 million echocardiograms from
ten standard views, along with their 3D spatial poses. In the downstream probe
guidance task, we demonstrate that our pre-trained model consistently reduces
guidance errors across the ten most common standard views on the test set with
0.29 million samples from 74 routine clinical scans, indicating that
structure-aware pre-training benefits the scanning.
