Respiratory-induced liver motion prediction using ECG as surrogate signal.

PURPOSE: This study investigates the use of electrocardiogram (ECG) as a surrogate signal to model the liver's respiratory-induced motion. METHODS: A learning-based model was trained to predict respiratory-induced liver motion by relying exclusively on ECG data, without requiring additional imaging. A correspondence model based on an encoder-decoder architecture was defined to map internal liver motion from ECG signals. Experimental validation was conducted through a human subject study involving eight participants performing various breathing patterns. RESULTS: The mean absolute error during normal breathing was 2.83 mm, while the overall error considering all breathing patterns was 4.02 mm with correlation coefficients above 0.90. More than 90% of predictions fell within reported acceptable error margins for needle insertion procedures. The model's performance reduces when the liver motion increases during deep breathing patterns. CONCLUSION: ECG can serve as a reliable noninvasive surrogate for predicting liver motion. This approach offers the advantage of being readily available in clinical settings and can provide accurate guidance for interventional procedures.