Supercomputer Technology for Ultrasound Tomographic Image Reconstruction: Mathematical Methods and Experimental Results
This paper is concerned with layer-by-layer ultrasound tomographic imaging methods for differential diagnosis of breast cancer. The inverse problem of ultrasound tomography is formulated as a coefficient inverse problem for a hyperbolic differential equation. The scalar mathematical model takes into account wave phenomena, such as diffraction, refraction, multiple scattering, and absorption of ultrasound. The algorithms were tested on real data obtained in experiments on a test bench for ultrasound tomography studies. Low-frequency ultrasound in the 100–500 kHz band was used for sounding. An important result of this study is an experimental confirmation of the adequacy of the underlying mathematical model. The ultrasound tomographic imaging methods developed have a spatial resolution of 2 mm, which is acceptable for medical diagnostics. The experiments were carried out using phantoms with parameters close to the acoustical properties of human soft tissues. The image reconstruction algorithms are designed for graphics processors. Architecture of the GPU cluster for ultrasound tomographic imaging is proposed, which can be employed as a computing device in a tomographic complex.
KeywordsUltrasound tomography Coefficient inverse problem Medical imaging GPU cluster
This work was supported by Russian Science Foundation [grant number 17-11-01065]. The research is carried out at Lomonosov Moscow State University. The research is carried out using the equipment of the shared research facilities of HPC computing resources at Lomonosov Moscow State University.
- 1.Sak, M., et al.: Using speed of sound imaging to characterize breast density. Ultrasound Med. Biol. 43(1), 91–103 (2017). https://doi.org/10.1016/j.ultrasmedbio.2016.08.021CrossRefGoogle Scholar
- 4.Burov, V.A., Zotov, D.I., Rumyantseva, O.D.: Reconstruction of the sound velocity and absorption spatial distributions in soft biological tissue phantoms from experimental ultrasound tomography data. Acoust. Phys. 61(2), 231–248 (2015). https://doi.org/10.1134/s1063771015020013CrossRefGoogle Scholar
- 7.Klibanov, M.V., Timonov, A.A.: Carleman Estimates for Coefficient Inverse Problems and Numerical Applications, Walter de Gruyter GmbH (2004). https://doi.org/10.1515/9783110915549
- 20.Hamilton, B., Bilbao, S.: Fourth-order and optimised finite difference schemes for the 2-D wave equation. In: Proceedings of the 16th International Conference on Digital Audio Effects (DAFx-13), pp. 363–395. Springer (2013)Google Scholar
- 21.Bakushinsky, A., Goncharsky, A.: Iterative Methods for Solving ill-Posed Problems. Nauka, Moscow (1989)Google Scholar
- 22.Goncharsky, A.V., Seryozhnikov, S.Y.: The architecture of specialized GPU clusters used for solving the inverse problems of 3D low-frequency ultrasonic tomography. In: Voevodin, V., Sobolev, S. (eds.) RuSCDays2017. Communications in Computer and Information Science, vol. 793, pp. 363–395. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71255-0_29CrossRefGoogle Scholar