Synthetic Elastography Using B-Mode Ultrasound Through a Deep Fully Convolutional Neural Network
| Autor: | Massimo Mischi, Pedro Henrique de Marqui Moraes, Hessel Wijkstra, Georg Salomon, Christophe K. Mannaerts, R.R. Wildeboer, Maria Cristina Chammas, R.J.G. van Sloun |
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| Přispěvatelé: | Graduate School, APH - Quality of Care, Urology, APH - Personalized Medicine, Center for Care & Cure Technology Eindhoven, Biomedical Diagnostics Lab, Signal Processing Systems, EAISI Health |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Scanner Acoustics and Ultrasonics Computer science Computer Vision and Pattern Recognition (cs.CV) shear-wave elastography (SWE) Computer Science - Computer Vision and Pattern Recognition Thyroid Gland SDG 3 – Goede gezondheid en welzijn Convolutional neural network 030218 nuclear medicine & medical imaging B-mode ultrasound 03 medical and health sciences 0302 clinical medicine Deep Learning SDG 3 - Good Health and Well-being convolutional neural networks FOS: Electrical engineering electronic engineering information engineering medicine Image Processing Computer-Assisted Humans Electrical and Electronic Engineering Acoustic radiation force Instrumentation medicine.diagnostic_test business.industry B mode ultrasound Deep learning Image and Video Processing (eess.IV) Ultrasound Pattern recognition Electrical Engineering and Systems Science - Image and Video Processing Visualization 030220 oncology & carcinogenesis Elasticity Imaging Techniques Elastography Artificial intelligence business |
| Zdroj: | IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 67(12):9046008, 2640-2648. Institute of Electrical and Electronics Engineers Inc. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(12):9046008, 2640-2648. Institute of Electrical and Electronics Engineers |
| ISSN: | 1525-8955 0885-3010 |
| Popis: | Shear-wave elastography (SWE) permits local estimation of tissue elasticity, an important imaging marker in biomedicine. This recently-developed, advanced technique assesses the speed of a laterally-travelling shear wave after an acoustic radiation force "push" to estimate local Young's moduli in an operator-independent fashion. In this work, we show how synthetic SWE (sSWE) images can be generated based on conventional B-mode imaging through deep learning. Using side-by-side-view B-mode/SWE images collected in 50 patients with prostate cancer, we show that sSWE images with a pixel-wise mean absolute error of 4.5+/-0.96 kPa with regard to the original SWE can be generated. Visualization of high-level feature levels through t-Distributed Stochastic Neighbor Embedding reveals substantial overlap between data from two different scanners. Qualitatively, we examined the use of the sSWE methodology for B-mode images obtained with a scanner without SWE functionality. We also examined the use of this type of network in elasticity imaging in the thyroid. Limitations of the technique reside in the fact that networks have to be retrained for different organs, and that the method requires standardization of the imaging settings and procedure. Future research will be aimed at development of sSWE as an elasticity-related tissue typing strategy that is solely based on B-mode ultrasound acquisition, and the examination of its clinical utility. Comment: (c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works |
| Databáze: | OpenAIRE |
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