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B-mode ultrasound to elastography synthesis using multiscale learning.

Fei Dai¹, Yifang Li², Yunkai Zhu³

¹Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China.

|February 25, 2024

Summary

This summary is machine-generated.

A new deep learning method, multiscale elastic image synthesis network (MEIS-Net), synthesizes elastic ultrasound images from B-mode data. This approach enhances accessibility for diagnosing conditions like tumors in primary care settings.

Keywords:

B-mode ultrasound Deep learning Elastography Modality synthesis Prostate cancer

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Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Elastography measures tissue hardness for diagnosing lesions like tumors.
High costs and limited availability of elastography devices restrict its use in rural primary care.
There is a need for accessible elastography methods in underserved areas.

Purpose of the Study:

To develop a deep learning approach for synthesizing elastic ultrasound images from B-mode ultrasound data.
To overcome the limitations of traditional elastography in terms of cost and accessibility.
To enable wider clinical application of elastography in primary medical facilities.

Main Methods:

Proposed a multiscale elastic image synthesis network (MEIS-Net) utilizing multiscale learning.
Integrated multi-scale prostate features and employed a fusion module to enhance elastic synthesis.
Synthesized local and global elastic images from B-mode and elastography feature maps, then combined them.

Main Results:

MEIS-Net effectively synthesized elastic images from B-mode ultrasound data from two devices.
Achieved a structural similarity index of 0.74 ± 0.04, outperforming Pix2Pix, CycleGAN, and StarGANv2.
Synthetic elastic images showed comparable classification performance to real images, with only a 3% AUC decrease.

Conclusions:

MEIS-Net offers a viable alternative to traditional elastography for synthesizing elastic images.
The method demonstrates potential for improving diagnostic capabilities in resource-limited settings.
The approach shows clinical effectiveness and broad applicability for lesion detection.