Abstract Full waveform inversion (FWI) is a complex data fitting process based on full wavefield modeling, aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution. However, this process is highly nonlinear and ill-posed, therefore achieving high-resolution imaging of complex biological tissues within a limited number of iterations remains challenging. We propose a multiscale frequency-domain full waveform inversion (FDFWI) framework for ultrasound computed tomography (USCT) imaging of biological tissues, which innovatively incorporates Sobolev space norm regularization for enhancement of prior information. Specifically, we investigate the effect of different types of hyperparameter on the imaging quality, during which the regularization weight is dynamically adapted based on the ratio of the regularization term to the data fidelity term. This strategy reduces reliance on predefined hyperparameters, ensuring robust inversion performance. The inversion results from both numerical and experimental tests (i.e., numerical breast, thigh, and ex vivo pork-belly tissue) demonstrate the effectiveness of our regularized FWI strategy. These findings will contribute to the application of the FWI technique in quantitative imaging based on USCT and make USCT possible to be another high-resolution imaging method after x-ray computed tomography and magnetic resonance imaging.
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 12474461), the Basic and Frontier Exploration Project Independently Deployed by Institute of Acoustics, Chinese Academy of Sciences (Grant No. JCQY202402), and the Goal-Oriented Project Independently Deployed by Institute of Acoustics, Chinese Academy of Sciences (Grant No. MBDX202113).
Corresponding Authors:
Yubing Li, Weijun Lin
E-mail: liyubing@mail.ioa.ac.cn;linwj@mail.ioa.ac.cn
Cite this article:
Panpan Li(李盼盼), Yubing Li(李玉冰), Chang Su(苏畅), Zeyuan Dong(董则元), and Weijun Lin(林伟军) Sobolev space norm regularized full waveform inversion for ultrasound computed tomography 2025 Chin. Phys. B 34 054301
[1] Huthwaite P and Simonetti F 2011 J. Acoust. Soc. Am. 130 1721 [2] Fincke J, Zhang X, Shin B, Ely G and Anthony B W 2022 IEEE Trans. Med. Imaging. 41 502 [3] Zhou C, Xu K and Ta D 2023 J. Acoust. Soc. Am. 154 279 [4] Guasch L, Calderón Agudo O, Tang M X, Nachev P and Warner M 2020 NPJ Digit. Med. 3 28 [5] Li Y B, Wang J, Su C, Lin W J, Wang X M and Luo Y 2023 Chin. Phys. B 32 014303 [6] Na S and Wang L V 2021 Biomed. Opt. Express 12 4056 [7] Pan Y, Qiang Y, Liang W, Huang W, Wang N, Wang X, Zhang Z, Qiu W and Zheng H 2024 Ultrasonics 143 107405 [8] Mittendorff L, Young A and Sim J 2022 J. Med. Radiat. Sci. 69 250 [9] Sanches J M, Laine A F and Suri J S 2012 Ultrasound Imaging: Advances and Applications (New York: Springer) [10] Azhari H 2012 Curr. Pharm. Biotechnol. 13 2104 [11] Iyer A, Sun Z, Lambeth K, Singh M, Cleveland C and Sharma N 2024 IEEE Trans. Rob. 40 4322 [12] Martin K 2010 Introduction to B-mode imaging (in: Hoskins P R, Martin K, Thrush A eds.) Diagnostic Ultrasound: Physics and Equipment (Cambridge University Press) pp. 1-3 [13] Wiskin JW, Borup D T, Iuanow E, Klock J and Lenox M W 2017 IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 64 1161 [14] Duric N, Littrup P, Poulo L, Babkin A, Pevzner R, Holsapple E, Rama O and Glide C 2007 Med. Phys. 34 773 [15] Qu X, Azuma T, Yogi T, Azuma S, Takeuchi H, Tamano S and Takagi S 2016 J. Med. Ultrason. 43 461 [16] Perrot V, Polichetti M, Varray F and Garcia D 2021 Ultrasonics 111 106309 [17] Bao Y and Jia J 2020 IEEE Trans. Instrum. Meas. 69 974 [18] Devaney A 1982 Ultrason. Imag. 4 336 [19] Simonetti F, Huang L, Duric N and Littrup P 2009 Med. Phys. 36 2955 [20] Schuster G T 1996 Geophys. J. Int. 127 427 [21] Wu X, Li Y, Su C, Li P, Wang X and Lin W 2023 Ultrasonics 132 107004 [22] Virieux J and Operto S 2009 Geophysics 64 74 [23] Pratt R, Huang L, Duric N and Littrup P 2007 Proceedings 6510 65104 [24] Ali R, et al. 2024 IEEE Trans. Med. Imaging. 43 2988 [25] Wu X, Li Y, Su C, Li P and Lin W 2025 Ultrasonics 147 107505 [26] Li Y, Shi Q, Li Y, Song X, Liu C, Ta D and Wang W 2021 Chin. Phys. B 30 014302 [27] Régo R C L, et al. 2019 Proceedings of the 16th International Congress of the Brazilian Geophysical Society & Expogef August 19-22, 2019, Rio de Janeiro, Brazil [28] Bunks C, Saleck F M, Zaleski S and Chavent G 1995 Geophysics. 60 1457 [29] Pratt R G 1999 Geophysics 64 888 [30] Pratt R and Shipp R 1999 Geophysics 64 902 [31] Sirgue L and Pratt R G 2004 Geophysics 69 231 [32] Kazei V V, Kalita M and Alkhalifah T 2017 Proceedings of the 79th EAGE Conference and Exhibition 2017, June 12-15, 2017 Paris, France, pp. 1-5 [33] Tikhonov A N and Arsenin V Y 1977 Solutions of ill-posed problems (New York: John Wiley & Sons) [34] Bertete-Aguirre H, Cherkaev E and Oristaglio M 2002 Geophysical Journal International 149 499 [35] Aghamiry H, Gholami A and Operto S 2020 Geophysics 85 116 [36] Aghamiry H S, Gholami A and Operto S 2021 SIAM Journal on Imaging Sciences 14 58 [37] Aghamiry H S, Gholami A and Operto S 2018 Proceedings of the SEG meeting, October 18, 2018, Anaheim, California, USA pp. 1253-1257 [38] Agazade K, Gholami A and Aghamiry H S 2023 84th EAGE Annual Conference & Exhibition, June 2023, Vienna, Autria pp. 1-5 [39] Tarantola A 2005 Inverse Problem Theory and Methods for Model Parameter Estimation (Philadelphia: Society for Industrial and Applied Mathematics) pp. 81-96 [40] Wang Y 2017 Seismic Inversion: Theory and Applications (JohnWiley & Sons, Ltd) [41] Nocedal J and Wright S J 2006 Numerical optimization (New York: Springer) pp. 30-191 [42] Byrd R H, Lu P, Nocedal J and Zhu C 1995 SIAM J. Sci. Comput. 16 1190 [43] Plessix R E 2006 Geophys. J. Int. 167 495 [44] Osnabrugge G, Leedumrongwatthanakun S and Vellekoop I M 2016 J. Comput. Phys. 322 113 [45] Wang Z, Bovik A C, Sheikh H R and Simoncelli E P 2004 IEEE Trans. Image Process 13 600 [46] Lou Y, et al. 2017 J. Biomed. Opt. 22 041015 [47] Treeby B E and Cox B T 2010 J. Biomed. Opt. 15 021314
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.