Arterial Diameter Trend Estimation Using Deep Learning on Ultrasound Spectral Doppler.

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Signal Processing

Background:

• Arterial diameter estimation is crucial for vascular health assessment.
• Gaps in ultrasound data can hinder accurate analysis.
• Spectral Doppler signals offer a potential alternative for continuous monitoring.

Purpose of the Study:

• To develop and evaluate a deep learning approach for estimating arterial diameter using spectral Doppler data.
• To introduce the Arterial ReScaling (AReS) algorithm for filling gaps in arterial diameter measurements.
• To compare the performance of Convolutional Recurrent Neural Network with Attention (CRNN-A) and Audio Spectrogram Transformer (AST) models.

Main Methods:

• Utilized short-time Fourier transform and deep learning models (CRNN-A, AST) on spectrograms for arterial diameter trend estimation.
• Trained models to predict scaled trends in 1, 2, or 4-second increments.
• Implemented the AReS algorithm to reconstruct missing arterial diameter data using model predictions.

Main Results:

• CRNN-A model trained on 1s segments showed high accuracy (R² = 0.8083) in predicting scaled trends.
• AST model trained on 2s segments demonstrated the best performance in filling 1s gaps with minimal Mean Absolute Error (MAE: 0.0341 mm) when using AReS.
• Training on longer time segments can yield superior real-world performance.

Conclusions:

• Deep learning models can effectively estimate arterial diameter trends from spectral Doppler data.
• The AReS algorithm successfully reconstructs missing arterial diameter measurements, ensuring data continuity.
• This approach offers a viable solution for continuous vascular monitoring and diagnosis when ultrasound imaging is interrupted.