Goal-conditioned reinforcement learning for ultrasound navigation guidance
arxiv(2024)
摘要
Transesophageal echocardiography (TEE) plays a pivotal role in cardiology for
diagnostic and interventional procedures. However, using it effectively
requires extensive training due to the intricate nature of image acquisition
and interpretation. To enhance the efficiency of novice sonographers and reduce
variability in scan acquisitions, we propose a novel ultrasound (US) navigation
assistance method based on contrastive learning as goal-conditioned
reinforcement learning (GCRL). We augment the previous framework using a novel
contrastive patient batching method (CPB) and a data-augmented contrastive
loss, both of which we demonstrate are essential to ensure generalization to
anatomical variations across patients. The proposed framework enables
navigation to both standard diagnostic as well as intricate interventional
views with a single model. Our method was developed with a large dataset of 789
patients and obtained an average error of 6.56 mm in position and 9.36 degrees
in angle on a testing dataset of 140 patients, which is competitive or superior
to models trained on individual views. Furthermore, we quantitatively validate
our method's ability to navigate to interventional views such as the Left
Atrial Appendage (LAA) view used in LAA closure. Our approach holds promise in
providing valuable guidance during transesophageal ultrasound examinations,
contributing to the advancement of skill acquisition for cardiac ultrasound
practitioners.
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