Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography

TL;DR

This paper introduces a CNN-based method for real-time ultrasound computed tomography image reconstruction that maintains high accuracy and improves lesion detection, significantly reducing computational costs compared to traditional full-waveform inversion techniques.

Contribution

The study presents a novel supervised learning approach using CNNs with task-informed loss for fast, accurate USCT image reconstruction, integrating lesion detection into the training process.

Findings

CNN achieves comparable RMSE and SSIM to FWI

Method improves lesion detection performance

Reconstruction time is significantly reduced

Abstract

Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction methods incorporate accurate wave physics to produce high spatial resolution quantitative images of speed of sound or other acoustic properties of the breast tissues from USCT measurement data. However, the high computational cost of FWI reconstruction represents a significant burden for its widespread application in a clinical setting. The research reported here investigates the use of a convolutional neural network (CNN) to learn a mapping from USCT waveform data to speed of sound estimates. The CNN was trained using a supervised approach with a task-informed loss function aiming at preserving features of the image that are relevant to the detection of lesions. A large set of anatomically and physiologically realistic numerical breast phantoms (NBPs) and corresponding simulated USCT measurements was employed during training. Once trained, the CNN can perform real-time FWI image reconstruction from USCT waveform data. The performance of the proposed method was assessed and compared against FWI using a hold-out sample of 41 NBPs and corresponding USCT data. Accuracy was measured using relative mean square error (RMSE), structural self-similarity index measure (SSIM), and lesion detection performance (DICE score). This numerical experiment demonstrates that a supervised learning model can achieve accuracy comparable to FWI in terms of RMSE and SSIM, and better performance in terms of task performance, while significantly reducing computational time.