Ultrasound Image Generation and Modality Conversion Based on Deep Learning

Shujun Xia, MD; Jianqiao Zhou, MD

doi:10.37015/AUDT.2023.230011

ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY >

2023 , Vol. 7 >Issue 2: 136 - 139

DOI: https://doi.org/10.37015/AUDT.2023.230011

Review Articles

Ultrasound Image Generation and Modality Conversion Based on Deep Learning

Shujun Xia, MD ,
Jianqiao Zhou, MD

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^a Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine

Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Rui Jin Er Road, Shanghai, China, e-mail: zhousu30@126.com

Received date: 2023-03-27

Revised date: 2023-04-05

Accepted date: 2023-04-21

Online published: 2023-04-27

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Abstract

Artificial intelligent (AI) based on deep learning has been used in medical imaging analysis for years. Improvements have been made in the diagnosis of various diseases with the help of deep learning. Multimodal medical imaging combines two or more imaging modalities, providing comprehensive diagnostic information of the diseases. However, some modality problems always exist in clinical practice. Recently, AI-based deep learning technologies have realized the modality conversion. Investigations on modality conversion have gradually been reported in order to acquire multimodal information. MRI images could be generated from CT images while ultrasound elastography could be generated from B mode ultrasonography. Continuous researches and development of new technologies around deep learning are still under investigation and provide huge clinical potentials in the future. The purpose of this review is to summarize an overview of the current applications and prospects of deep learning-based modality conversion of medical imaging.

Key words： Ultrasound image generation; Modality conversion; Deep learning

Cite this article

Shujun Xia, MD , Jianqiao Zhou, MD . Ultrasound Image Generation and Modality Conversion Based on Deep Learning[J]. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY, 2023 , 7(2) : 136 -139 . DOI: 10.37015/AUDT.2023.230011

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