Compensation for Aberrations When Focusing Ultrasound Through the Skull Based on CT and MRI Data

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Abstract

The study compares the capabilities of using 3D acoustic models of the human head, constructed using magnetic resonance imaging (MRI) and computed tomography (CT) data, to simulate ultrasound beam focusing when passing through skull bones and to compensate for aberrations caused by them. A CT and MRI dataset from one patient was considered. The MRI data were used to reconstruct segments of the human head (skin, skull, and brain) that were homogeneous in their internal structure. The most realistic CT model took into account the internal inhomogeneities of the skull bones and soft tissues. Field simulations and compensation for aberrations were performed using the Rayleigh integral and pseudospectral method for solving the wave equation in an inhomogeneous medium, implemented in the k-Wave software package. The transducer was considered to be a fully populated 256-element phased array with a frequency of 1 MHz and radius of curvature and an aperture of 200 mm. It was shown that when aberrations were compensated using an inhomogeneous CT model and a homogeneous MRI model, the pressure amplitude at the focus and focusing efficiency were different by less than 10%. Thus, a homogeneous MRI model can be used for preoperative assessment of the feasibility of transcranial ultrasound therapy. During therapy, it is preferable to take into account the internal structure of the skull bones based on CT data.

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About the authors

D. D. Chupova

Lomonosov Moscow State University

Author for correspondence.
Email: daria.chupova@yandex.ru
Russian Federation, Moscow

P. B. Rosnitskiy

Lomonosov Moscow State University

Email: daria.chupova@yandex.ru
Russian Federation, Moscow

O. V. Solontsov

Lomonosov Moscow State University

Email: daria.chupova@yandex.ru
Russian Federation, Moscow

L. R. Gavrilov

Lomonosov Moscow State University

Email: daria.chupova@yandex.ru
Russian Federation, Moscow

V. E. Sinitsyn

Lomonosov Moscow State University

Email: daria.chupova@yandex.ru
Russian Federation, Moscow

E. A. Mershina

Lomonosov Moscow State University

Email: daria.chupova@yandex.ru
Russian Federation, Moscow

O. A. Sapozhnikov

Lomonosov Moscow State University

Email: daria.chupova@yandex.ru
Russian Federation, Moscow

V. A. Khokhlova

Lomonosov Moscow State University

Email: daria.chupova@yandex.ru
Russian Federation, Moscow

References

  1. Qiu W., Bouakaz A., Konofagou E., Zheng H. Ultrasound for the brain: A review of physical and engineering principles, and clinical applications // IEEE Trans. Ultrason. Ferroelect. Freq. Contr. 2020. V. 68. № 1. P. 6–20.
  2. O’Reilly A.M. Incisionless Brain Surgery: Overcoming the Skull with Focused Ultrasound // Acoustics Today. V. 19. № 3. P. 30–37.
  3. Elias W.J., Lipsman N., Ondo G. et al. A Randomized trial of focused ultrasound thalamotomy for essential tremor // N. Engl. J. Med. 2016. V. 375. № 8. P. 730–739.
  4. Гаврилов Л.Р. Фокусированный ультразвук высокой интенсивности в медицине. М.: Фазис, 2013.
  5. Hynynen K., Jones R.M. Image-guided ultrasound phased arrays are a disruptive technology for non-invasive therapy // Phys. Med. Biol. 2016. V. 61. P. 206–248.
  6. Schneider U., Pedroni E., Lomax A. The calibration of CT Hounsfield Units for radiotherapy treatment planning // Phys. Med. Biol. 1996. V. 41. P. 111–124.
  7. Mast T.D. Empirical relationships between acoustic parameters in human soft tissues // ARLO. 2000. V. 1. № 2. P. 37–42.
  8. D’Souza M., Chen K., Rosenberg J. et al. Impact of skull density ratio on efficacy and safety of magnetic resonance-guided focused ultrasound treatment of essential tremor // J. of Neurosurgery. 2019. V. 132. № 5. P. 1392–1397.
  9. Aubry J.F., Eames M., Snell J., Miller G.W. Ultrashort echo-time MRI as a substitute to CT for skull aberration correction in transcranial focused ultrasound: in vitro comparison on human calvaria // J. Ther. Ultrasound 3. 2015. (Suppl. 1). P12.
  10. Wiesinger F., Bylund M., Yang J. et al. Zero TE-based pseudo-CT image conversion in the head and its application in PET/MR attenuation correction and MR-guided radiation therapy planning // Magn. Reson. Med. 2018.V. 80. № 4. P. 1440–1451.
  11. Leung S.A., Moore D., Gilbo Y. et al. Comparison between MR and CT imaging used to correct for skull-induced phase aberrations during transcranial focused ultrasound // Scientific Rep. 2022. V. 12. № 1. P. 13407–12320.
  12. Johnson E.M., Vyas U., Ghanouni P., Pauly K.B., Pauly J.M. Improved cortical bone specificity in UTE MR Imaging // Magn Reson Med. 2017. V. 77. № 2. P. 684-695.
  13. Su P., Gou S., Roys S. et al. Transcranial MR Imaging-Guided Focused Ultrasound Interventions Using Deep Learning Synthesized CT // AJNR. 2020. V. 41. № 10. P. 1841–1848.
  14. Koh H., Park T.Y., Chung Y.A., Lee J.H., Kim H. Acoustic simulation for transcranial focused ultrasound using GAN-Based Synthetic CT // IEEE J. Biomed. and Health Inf. 2022. V. 26. № 1. P. 161–171.
  15. Wintermark M., Tustison N.J., Elias W.J. et al. T1-weighted MRI as a substitute to CT for refocusing planning in MR-guided focused ultrasound // Phys Med. Biol. 2014. V. 59. № 13. P. 3599–3614.
  16. Miscouridou M., Pineda-Pardo J.A., Stagg C.J., Treeby B.E., Stanziola A. Classical and learned MR to pseudo-CT mappings for accurate transcranial ultrasound simulation // IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 2022. V. 69. № 10. P. 2896–2905.
  17. Rosnitskiy P.B., Vysokanov B.A., Gavrilov L.R., Sapozhnikov O.A., Khokhlova V.A. Method for designing multielement fully populated random phased arrays for ultrasound surgery applications // IEEE Trans. Ultrason. Ferroelect. Freq. Contr. 2018. V. 65. № 4. P. 630–637.
  18. Rosnitskiy P.B., Yuldashev P.V., Sapozhnikov O.A., Gavrilov L.R., Khokhlova V.A. Simulation of nonlinear trans-skull focusing and formation of shocks in brain using a fully populated ultrasound array with aberration correction // J. Acoust. Soc. Am. 2019. V. 146. № 3. P 1786–1798.
  19. Duck F.A. Physical Properties of Tissue: A Comprehensive Reference Book. Academic Press, London, 1990.
  20. Pinter C., Lasso A., Fichtinger G. Polymorph segmentation representation for medical image computing // Comp. Methods and Progr. in Biomed. 2019. V. 171. P. 19–26.
  21. Fennema-Notestine C., Ozyurt B., Clark C.P. et al. Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location human brain mapping // The Morph. BIRN. 2006. V. 27. № 2. P. 99–113.
  22. Arnold J.B., Liow J.S., Schaper K.A. et al. Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects // NeuroImage. 2001. V. 5. № 13. P. 931–943.
  23. Tsai K.W., Chen J.C., Lai H.C., Chang W.C., Taira T., Chang J.W., Wei C.Y. The Distribution of skull score and skull density ratio in tremor patients for MR-guided focused ultrasound thalamotomy // Front. in neuroscience. 2021. V. 15. 612940.
  24. Otsu N. A threshold selection method from gray-level histograms // IEEE Trans. Syst. Man Cybernetics. 1979. V. 9. P. 62–66.
  25. Ильин С.А., Юлдашев П.В., Хохлова В.А., Гаврилов Л.Р., Росницкий П.Б., Сапожников О.А. Применение аналитического метода для оценки качества акустических полей при электронном перемещении фокуса многоэлементных терапевтических решеток // Акуст. журн. 2015. Т. 61. № 1. С. 57–64.
  26. Чупова Д.Д., Росницкий П.Б., Гаврилов Л.Р., Хохлова В.А. Компенсация искажений фокусированных ультразвуковых пучков при транскраниальном облучении головного мозга на различной глубине // Акуст. журн. 2022. Т. 68. № 1. С. 3–13.
  27. Treeby B.E., Cox B.T. Modeling power law absorption and dispersion in viscoelastic solids using a split-field and the fractional Laplacian // J. Acoust. Soc. Am. 2014. V. 136. № 4. P. 1499–1510.
  28. Treeby B.E., Jaros J., Rohrbach D., Cox B.T. Modelling elastic wave propagation using the k-Wave Matlab toolbox // IEEE International Ultrasonics Symposium. 2014. P. 146–149.
  29. Бобина А.С., Росницкий П.Б., Хохлова Т.Д., Юлдашев П.В., Хохлова В.А. Влияние неоднородностей брюшной стенки на фокусировку ультразвукового пучка при различных положениях излучателя // Изв. Рос. Акад. наук. Сер. физ. 2021. Т. 85. № 6. С. 875–882.
  30. Maimbourg G., Houdouin A., Deffieux T., Tanter M., Aubry J.-F. Steering capabilities of an acoustic lens for transcranial therapy: Numerical and experimental studies // IEEE Trans. Biomed. Eng. 2020. V. 67. P. 27–37.
  31. Wu N., Shen G., Qu X., Wu H., Qiao S., Wang E., Chen Y., Wang H. An efficient and accurate parallel hybrid acoustic signal correction method for transcranial ultrasound // Phys Med Biol. 2020. V. 65. № 21. P. 215019.
  32. Maimbourg G., Guilbert J., Bancel T., Houdouin A., Raybaud G., Tanter M., Aubry J.-F. Computationally effective transcranial ultrasonic focusing: taking advantage of the high correlation length of the human skull // IEEE Trans. Ultrason. Ferroelect. Freq. Contr. 2020. V. 67. № 10. P. 1993–2002.
  33. Jin C., Moore D., Snell J., Paeng D.-G. An open-source phase correction toolkit for transcranial focused ultrasound // BMC Biomed Eng. 2020. V. 2. P. 9.
  34. Ebbini E.S., Cain C.A., A Spherical-Section Ultrasound Phased Array Applicator for Deep Localized Hyperthermia // IEEE. 1991. V. l. № 38. P. 634–643.

Supplementary files

Supplementary Files
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1. JATS XML
2. Fig. 1. Axial section of a human head computed tomography (a) – before segmentation, (b) – before segmentation with a different contrast, (c) – after segmentation in the Slicer program. Segments are marked with numbers: 1 – external environment (water), 2 – skin, 3 – skull, 4 – brain.

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3. Fig. 2. Axial section of magnetic resonance imaging of the human head (a) – before segmentation; (b) – after segmentation in the BrainSuit program; (c) – after revision in the Slicer program. The colors of the segments correspond to those in the CT segmentation.

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4. Fig. 3. The result of combining three-dimensional models of the human head (skull layer), constructed on the basis of CT data (blue) and MRI data (red); (a) – external surfaces, (b) – internal surfaces.

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5. Fig. 4. (a) – Schematic diagram of the 256-element array considered in the work; diameter D = 200 cm, radius of curvature F = 200 cm, operating frequency f = 1 MHz, gap between elements 0.5 mm. Schemes (b) – calculation of the field during brain irradiation and (c) – compensation of aberrations. The arrows show the order of calculations: using the Rayleigh integral and the pseudospectral method of modeling the propagation of acoustic waves in inhomogeneous media. Focusing was performed on the center of the brain.

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6. Fig. 5. (a) – Distribution of density in the cortical plane zy in the inhomogeneous CT model. Distributions of segments using the example of a cortical slice zy (b) – in the homogeneous CT model and (c) – in the MRI model, the numbers indicate the indices of each segment.

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7. Fig. 6. Distributions of the normalized pressure amplitude pA/p0 in the zy plane in the case of irradiation of the brain center without compensation for aberrations for (a) an inhomogeneous CT model; (b) a homogeneous CT model; (c) a homogeneous MRI model. Designation of the model for which the field calculation was performed: NCCT – inhomogeneous CT model, OCT – homogeneous CT model, MRI – MRI model.

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8. Fig. 7. Distributions of the normalized pressure amplitude pA/p0 in the zy plane in the case of irradiation of the brain center after compensation for aberrations for (a) an inhomogeneous CT model; (b) a homogeneous CT model; (c) a homogeneous MRI model. Designations of the models used for aberration compensation and field calculation: iCT – inhomogeneous CT model, oCT – homogeneous CT model, MRI – MRI model.

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9. Fig. 8. Distributions of the normalized pressure amplitude pA/p0 in the zy plane in the case of irradiation of the brain center after compensation for aberrations with phases calculated using (a) an inhomogeneous CT model; (b) a homogeneous CT model; (c) a homogeneous MRI model. Designations of the models used to compensate for aberrations and calculate the field: iCT – inhomogeneous CT model, oCT – homogeneous CT model, MRI – MRI model.

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