Investigating Pulse-Echo Sound Speed Estimation in Breast Ultrasound with Deep Learning

• URL: http://arxiv.org/abs/2302.03064v1
• Date: Mon, 6 Feb 2023 19:02:44 GMT
• Title: Investigating Pulse-Echo Sound Speed Estimation in Breast Ultrasound with Deep Learning
• Authors: Walter A. Simson, Magdalini Paschali, Vasiliki Sideri-Lampretsa, Nassir Navab, Jeremy J. Dahl
• Abstract summary: We propose a deep-learning approach for sound speed estimation from in-phase and quadrature ultrasound signals. We develop a large-scale simulated ultrasound dataset that generates quasi-realistic breast tissue. We evaluate the model on simulated, phantom, and in-vivo breast ultrasound data.
• Score: 44.70495434283752
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians with diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound speed to form B-mode images for diagnosis. However, the various types of breast tissue, such as glandular, fat, and lesions, differ in sound speed. These differences can degrade the image reconstruction process. Alternatively, sound speed can be a powerful tool for identifying disease. To this end, we propose a deep-learning approach for sound speed estimation from in-phase and quadrature ultrasound signals. First, we develop a large-scale simulated ultrasound dataset that generates quasi-realistic breast tissue by modeling breast gland, skin, and lesions with varying echogenicity and sound speed. We developed a fully convolutional neural network architecture trained on a simulated dataset to produce an estimated sound speed map from inputting three complex-value in-phase and quadrature ultrasound images formed from plane-wave transmissions at separate angles. Furthermore, thermal noise augmentation is used during model optimization to enhance generalizability to real ultrasound data. We evaluate the model on simulated, phantom, and in-vivo breast ultrasound data, demonstrating its ability to accurately estimate sound speeds consistent with previously reported values in the literature. Our simulated dataset and model will be publicly available to provide a step towards accurate and generalizable sound speed estimation for pulse-echo ultrasound imaging.

Related papers

• Deep Sylvester Posterior Inference for Adaptive Compressed Sensing in Ultrasound Imaging [16.553626039240903]
  Minimizing the number of required scan-lines can significantly enhance frame rate, field of view, energy efficiency, and data transfer speeds. We introduce an adaptive subsampling method that maximizes intrinsic information gain in-situ.
  arXiv  Detail & Related papers  (2025-01-07T14:37:14Z)
• Ultrasound Lung Aeration Map via Physics-Aware Neural Operators [78.6077820217471]
  Lung ultrasound is a growing modality in clinics for diagnosing acute and chronic lung diseases. It is complicated by complex reverberations from the pleural interface caused by the inability of ultrasound to penetrate air. We propose LUNA, an AI model that directly reconstructs lung aeration maps from RF data.
  arXiv  Detail & Related papers  (2025-01-02T09:24:34Z)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• Real-Time Model-Based Quantitative Ultrasound and Radar [65.268245109828]
  We propose a neural network based on the physical model of wave propagation, which defines the relationship between the received signals and physical properties. Our network can reconstruct multiple physical properties in less than one second for complex and realistic scenarios.
  arXiv  Detail & Related papers  (2024-02-16T09:09:16Z)
• Cardiac ultrasound simulation for autonomous ultrasound navigation [4.036497185262817]
  We propose a method to generate large amounts of ultrasound images from other modalities and from arbitrary positions. We present a novel simulation pipeline which uses segmentations from other modalities, an optimized data representation and GPU-accelerated Monte Carlo path tracing. The proposed approach allows for fast and accurate patient-specific ultrasound image generation, and its usability for training networks for navigation-related tasks is demonstrated.
  arXiv  Detail & Related papers  (2024-02-09T15:14:48Z)
• AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers [75.20925220246689]
  endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature. This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
  arXiv  Detail & Related papers  (2023-09-25T19:34:12Z)
• Deep Learning for Ultrasound Beamforming [120.12255978513912]
  Beamforming, the process of mapping received ultrasound echoes to the spatial image domain, lies at the heart of the ultrasound image formation chain. Modern ultrasound imaging leans heavily on innovations in powerful digital receive channel processing. Deep learning methods can play a compelling role in the digital beamforming pipeline.
  arXiv  Detail & Related papers  (2021-09-23T15:15:21Z)
• A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images [0.0]
  We define a universal deep learning framework for real-time denoising of ultrasound images. We analyse and compare state-of-the-art methods for the smoothing of ultrasound images. Then, we propose a tuned version of the selected state-of-the-art denoising methods.
  arXiv  Detail & Related papers  (2021-01-22T14:18:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.