Heart Rate Estimation Based on Optical Flow: Enabling Smooth Angle Changes in Ultrasound Simulation

Abstract

Ultrasound simulators show previously recorded ultrasound videos from different angles to the trainee. During acquisition, breathing, pulse, and other motion artifacts are involved, which often prevent a smooth image transition between different angles during simulation. In this work, a global motion vector is derived using the Lucas–Kanade method for calculating the optical flow in order to create a motion profile in addition to the recording. This profile allows transition synchronization in ultrasound simulators. For the transition in kidney recordings, the Pearson’s r correlation could be increased from 0.252 to 0.495 by autocorrelating motion profiles and synchronizing them based on calculated delays. Approaches based on tracking and structural similarity were also evaluated, yet these have shown inferior qualitative transition results. In ultrasound videos with visibility of vessels, e.g., thyroid gland with carotid artery or echocardiogram, the heart rate can also be estimated via the optical flow. In the abdominal region, the signal contains respiratory information. Since the motion profile can be generated in real time directly at the transducer position, it could be useful for diagnostic purposes.

Henning Schäfer

Researcher in the first cohort

My research interests include Deep Learning, Computer Vision, Radiomics, and Explainable AI.

Hendrik Damm

Researcher in the second cohort

My research interests include Deep Learning, Natural Language Processing, and Information Retrieval.

Christoph M. Friedrich

Co-Speaker

My research interests include Deep Learning, Computer Vision, Radiomics, and Explainable AI.