Zhu Zheng1,2, Huabei Jiang1,2,3. 1. School of Electronic Science and Engineering (National Exemplary School of Microelectronics), University of Electronic Science and Technology of China, Chengdu 611731, China. 2. Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China. 3. Department of Medical Engineering, University of South Florida, Tampa, FL, USA.
Abstract
BACKGROUND: Tissue mechanical parameters such as elasticity are of great significance for the assessment of biological histopathological and physiological characteristics. Here, we propose a new approach called thermoacoustic elastography (TAE) for imaging tissue elastic modulus. METHODS: Central to TAE is an image reconstruction algorithm that allows the recovery of both microwave energy loss and elastic modulus distributions. The algorithm is first evaluated using simulated data under various practical scenarios, including a varied range of microwave energy loss and elastic modulus between the heterogeneity and background region, different noise levels, and multiple targets. The feasibility of the proposed TAE was then validated by imaging the elastic modulus distribution of agar phantoms with various elastic modulus and microwave energy loss. RESULTS: The results from both the simulated and phantom experiments show that the recovered elastic modulus by TAE agree well with the exact values, having an average error of less than 12.74%. CONCLUSIONS: The findings from this study suggest that TAE provides a new addition to the family of elasticity imaging and may have broad application prospects, such as cirrhosis and atherosclerosis detection.
BACKGROUND: Tissue mechanical parameters such as elasticity are of great significance for the assessment of biological histopathological and physiological characteristics. Here, we propose a new approach called thermoacoustic elastography (TAE) for imaging tissue elastic modulus. METHODS: Central to TAE is an image reconstruction algorithm that allows the recovery of both microwave energy loss and elastic modulus distributions. The algorithm is first evaluated using simulated data under various practical scenarios, including a varied range of microwave energy loss and elastic modulus between the heterogeneity and background region, different noise levels, and multiple targets. The feasibility of the proposed TAE was then validated by imaging the elastic modulus distribution of agar phantoms with various elastic modulus and microwave energy loss. RESULTS: The results from both the simulated and phantom experiments show that the recovered elastic modulus by TAE agree well with the exact values, having an average error of less than 12.74%. CONCLUSIONS: The findings from this study suggest that TAE provides a new addition to the family of elasticity imaging and may have broad application prospects, such as cirrhosis and atherosclerosis detection.
Entities:
Keywords:
Bulk elastic modulus; medical imaging; thermoacoustic imaging
Authors: S Majumdar; J Lin; T Link; J Millard; P Augat; X Ouyang; D Newitt; R Gould; M Kothari; H Genant Journal: Med Phys Date: 1999-07 Impact factor: 4.071