Feasibility of a Deep Learning approach to estimate Shear Wave Speed using the framework of Reverberant Shear Wave Elastography: A numerical simulation study

Pierol Quispe, Stefano E. Romero, Benjamín Castaneda

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

Reverberant Shear Wave Elastography (RSWE) is an ultrasound elastography technique that offers great advantages, however, current estimators generate underestimations and time-consuming issues. As well, the involvement of Deep Learning into the medical imaging field with new tools to assess complex problems, makes it a great candidate to serve as a new approach for a RSWE estimator. This work addresses the application of a Deep Neural Network (DNN) for the estimation of Shear Wave Speed (SWS) maps from particle velocity using numerically simulated data. The architecture of the proposed network is based on a U-Net, which works with a custom loss function specifically adopted for the reconstruction task. Four DNNs were trained using four different databases: clean, noisy, acquired at variable frequency, and noisy and acquired at variable frequency data. After the training of the DNNs, the predicted SWS maps were evaluated based on different metrics related to segmentation, regression and similarity of images. The model for clean data showed better results with a Mean Absolute Error (MAE) of 0.011, Mean Square Error(MSE) of 0.001, modified Intersection over Union (mIoU) of 98.4%, Peak Signal to Noise Ratio (PSNR) of 32.925 and a Structural Similarity Index Measure (SSIM) of 0.99, for 250 (size of Testing Sets); while the other models delivered SSIM in the range of 0.87 to 0.96. It was concluded that noisy and clean data could be effectively handled by the model, while the other ones still need enhancement. Clinical Relevance - This work is focused on the application of a Deep Learning approach to accurately asses the Shear Wave Speed in numerical simulations of Reverberant Shear Wave Elastography approach. This novel estimator could be useful for future clinical experiments specially with real time applications to determine the status of living tissue such as detection of malignant or benign tumors located in breast cervix prostate or skin and in the diagnosis of other pathologies such us liver fibrosis.

Idioma originalInglés
Título de la publicación alojada44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas3895-3898
Número de páginas4
ISBN (versión digital)9781728127828
DOI
EstadoPublicada - 2022
Evento44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 - Glasgow, Reino Unido
Duración: 11 jul. 202215 jul. 2022

Serie de la publicación

NombreProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volumen2022-July
ISSN (versión impresa)1557-170X

Conferencia

Conferencia44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
País/TerritorioReino Unido
CiudadGlasgow
Período11/07/2215/07/22

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