B-mode ultrasound to elastography synthesis using multiscale learning

Ultrasonics. 2024 Feb 13;138:107268. doi: 10.1016/j.ultras.2024.107268. Online ahead of print.ABSTRACTElastography is a promising diagnostic tool that measures the hardness of tissues, and it has been used in clinics for detecting lesion progress, such as benign and malignant tumors. However, due to the high cost of examination and limited availability of elastic ultrasound devices, elastography is not widely used in primary medical facilities in rural areas. To address this issue, a deep learning approach called the multiscale elastic image synthesis network (MEIS-Net) was proposed, which utilized the multiscale learning to synthesize elastic images from ultrasound data instead of traditional ultrasound elastography in virtue of elastic deformation. The method integrates multi-scale features of the prostate in an innovative way and enhances the elastic synthesis effect through a fusion module. The module obtains B-mode ultrasound and elastography feature maps, which are used to generate local and global elastic ultrasound images through their correspondence. Finally, the two-channel images are synthesized into output elastic images. To evaluate the approach, quantitative assessments and diagnostic tests were conducted, comparing the results of MEIS-Net with several deep learning-based methods. The experiments showed that MEIS-Net was effective in synthesizing elastic images from B-mode ultrasound data acquired from two different devices, with a structural similarity index of...
Source: Ultrasonics - Category: Physics Authors: Source Type: research