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Active Inference for Closed-loop transmit beamsteering in Fetal Doppler Ultrasound

Beatrice Federici, Ruud JG van Sloun, Massimo Mischi

TL;DR

The proposed cognitive ultrasound system leverages a sequential Monte Carlo method to infer the fetal heart position from the power Doppler signal, and employs a greedy information-seeking criterion to select the steering angle that minimizes the positional uncertainty for future timesteps.

Abstract

Doppler ultrasound is widely used to monitor fetal heart rate during labor and pregnancy. Unfortunately, it is highly sensitive to fetal and maternal movements, which can cause the displacement of the fetal heart with respect to the ultrasound beam, in turn reducing the Doppler signal-to-noise ratio and leading to erratic, noisy, or missing heart rate readings. To tackle this issue, we augment the conventional Doppler ultrasound system with a rational agent that autonomously steers the ultrasound beam to track the position of the fetal heart. The proposed cognitive ultrasound system leverages a sequential Monte Carlo method to infer the fetal heart position from the power Doppler signal, and employs a greedy information-seeking criterion to select the steering angle that minimizes the positional uncertainty for future timesteps. The fetal heart rate is then calculated using the Doppler signal at the estimated fetal heart position. Our results show that the system can accurately track the fetal heart position across challenging signal-to-noise ratio scenarios, mainly thanks to its dynamic transmit beam steering capability. Additionally, we find that optimizing the transmit beamsteering to minimize positional uncertainty also optimizes downstream heart rate estimation performance. In conclusion, this work showcases the power of closed-loop cognitive ultrasound in boosting the capabilities of traditional systems.

Active Inference for Closed-loop transmit beamsteering in Fetal Doppler Ultrasound

TL;DR

The proposed cognitive ultrasound system leverages a sequential Monte Carlo method to infer the fetal heart position from the power Doppler signal, and employs a greedy information-seeking criterion to select the steering angle that minimizes the positional uncertainty for future timesteps.

Abstract

Doppler ultrasound is widely used to monitor fetal heart rate during labor and pregnancy. Unfortunately, it is highly sensitive to fetal and maternal movements, which can cause the displacement of the fetal heart with respect to the ultrasound beam, in turn reducing the Doppler signal-to-noise ratio and leading to erratic, noisy, or missing heart rate readings. To tackle this issue, we augment the conventional Doppler ultrasound system with a rational agent that autonomously steers the ultrasound beam to track the position of the fetal heart. The proposed cognitive ultrasound system leverages a sequential Monte Carlo method to infer the fetal heart position from the power Doppler signal, and employs a greedy information-seeking criterion to select the steering angle that minimizes the positional uncertainty for future timesteps. The fetal heart rate is then calculated using the Doppler signal at the estimated fetal heart position. Our results show that the system can accurately track the fetal heart position across challenging signal-to-noise ratio scenarios, mainly thanks to its dynamic transmit beam steering capability. Additionally, we find that optimizing the transmit beamsteering to minimize positional uncertainty also optimizes downstream heart rate estimation performance. In conclusion, this work showcases the power of closed-loop cognitive ultrasound in boosting the capabilities of traditional systems.
Paper Structure (11 sections, 5 equations, 4 figures)

This paper contains 11 sections, 5 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Methods
  3. Generative Model
  4. Perception
  5. Action
  6. Heart rate estimation
  7. Experiments
  8. Validation Setup
  9. Results
  10. Discussion
  11. Conclusion

Figures (4)

  • Figure 1: Closed-loop fetal Doppler ultrasound.
  • Figure 2: Experimental setup, including a submerged Philips s5-1 phased array probe manipulated by a translation stage and a suspended chicken heart attached to an axial motion generator.
  • Figure 3: In-silico offline experiment comparing the performance of the proposed closed-loop system with adaptive focused beam and an open-system with a focused beam fixed at 0 rad at an SNR of 10 dB. (a) Selected steering angle compared to the Doppler target extent. (b) Angular position tracking over time. (c) Heart rate (HR) estimate over time compared to ground truth (GT). Note how the HR estimation fails when the Doppler target moves out of the fixed transmit beam.
  • Figure 4: Tracking mean absolute error (MAE) and heart rate (HR) accuracy ($\mathrm{|GT - HR|}$$\leq$ 5 bpm) as a function of the power Doppler signal-to-noise ratio (SNR), for the fixed and adaptive beam steering policies.