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Ultrasonography in Image-Guided Radiotherapy: Current Status and Future Challenges

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Image-Guided High-Precision Radiotherapy

Abstract

Ultrasound is a radiation-free, portable, cost-effective imaging modality, which currently is the only real-time volumetric image guidance option clinically available for radiotherapy applications. Its advantages, among which good soft tissue contrast and high resolution, and its challenges, as for example limited field of view and operator dependence, are discussed together with the future vision and the most innovative and advanced applications.

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References

  1. Antico M, Sasazawa F, Wu L, et al. Ultrasound guidance in minimally invasive robotic procedures. Med Image Anal. 2019;54:149–67.

    PubMed  Google Scholar 

  2. Fontanarosa D, van der Meer S, Bamber J, Harris E, O’Shea T, Verhaegen F. Review of ultrasound image guidance in external beam radiotherapy: I. treatment planning and inter-fraction motion management. Phys Med Biol. 2015;60(3):R77–114.

    PubMed  Google Scholar 

  3. O’Shea T, Bamber J, Fontanarosa D, van der Meer S, Verhaegen F, Harris E. Review of ultrasound image guidance in external beam radiotherapy part II: intra-fraction motion management and novel applications. Phys Med Biol. 2016;61(8):R90–137.

    PubMed  Google Scholar 

  4. Schewe JE, Balter JM, Lam KL, ten Haken RK. Measurement of patient setup errors using port films and a computer-aided graphical alignment tool. Med Dosim. 1996;21(2):97–104.

    CAS  PubMed  Google Scholar 

  5. Bel A, Vos PH, Rodrigus PT, et al. High-precision prostate cancer irradiation by clinical application of an offline patient setup verification procedure, using portal imaging. Int J Radiat Oncol Biol Phys. 1996;35(2):321–32.

    CAS  PubMed  Google Scholar 

  6. Srinivasan K, Mohammadi M, Shepherd J. Applications of linac-mounted kilovoltage cone-beam computed tomography in modern radiation therapy: a review. Pol J Rad. 2014;79:181–93.

    Google Scholar 

  7. Shirato H, Shimizu S, Kitamura K, et al. Four-dimensional treatment planning and fluoroscopic real-time tumor tracking radiotherapy for moving tumor. Int J Radiat Oncol Biol Phys. 2000;48(2):435–42.

    CAS  PubMed  Google Scholar 

  8. Willoughby TR, Kupelian PA, Pouliot J, et al. Target localization and real-time tracking using the calypso 4D localization system in patients with localized prostate cancer. Int J Radiat Oncol Biol Phys. 2006;65(2):528–34.

    PubMed  Google Scholar 

  9. Covington EL, Fiveash JB, Wu X, et al. Optical surface guidance for submillimeter monitoring of patient position during frameless stereotactic radiotherapy. J Appl Clin Med Phys/Am Coll Med Phys. 2019;20(6):91–8.

    Google Scholar 

  10. Enke C, Ayyangar K, Saw CB, Zhen W, Thompson RB, Raman NV. Inter-observer variation in prostate localization utilizing BAT. Int J Radiat Oncol Biol Phys. 2002;54(2):269.

    Google Scholar 

  11. Langen KM, Pouliot J, Anezinos C, et al. Evaluation of ultrasound-based prostate localization for image-guided radiotherapy. Int J Radiat Oncol Biol Phys. 2003;57(3):635–44.

    CAS  PubMed  Google Scholar 

  12. Artignan X, Smitsmans MHP, Lebesque JV, Jaffray DA, van Her M, Bartelink H. Online ultrasound image guidance for radiotherapy of prostate cancer: impact of image acquisition on prostate displacement. Int J Radiat Oncol Biol Phys. 2004;59(2):595–601.

    PubMed  Google Scholar 

  13. van der Meer S, Bloemen-van Gurp E, Hermans J, et al. Critical assessment of intramodality 3D ultrasound imaging for prostate IGRT compared to fiducial markers. Med Phys. 2013;40(7):071707.

    PubMed  Google Scholar 

  14. Martyn M, O’Shea TP, Harris E, Bamber J, Gilroy S, Foley MJ. A Monte Carlo study of the effect of an ultrasound transducer on surface dose during intrafraction motion imaging for external beam radiation therapy. Med Phys. 2017;44(10):5020–33.

    CAS  PubMed  Google Scholar 

  15. Fontanarosa D, van der Meer S, Bloemen-van Gurp E, Stroian G, Verhaegen F. Magnitude of speed of sound aberration corrections for ultrasound image-guided radiotherapy for prostate and other anatomical sites. Med Phys. 2012;39(8):5286–92.

    PubMed  Google Scholar 

  16. Fontanarosa D, van der Meer S, Harris E, Verhaegen F. A CT-based correction method for speed of sound aberration for ultrasound-based image-guided radiotherapy. Med Phys. 2011;38(5):2665–73.

    PubMed  Google Scholar 

  17. Schlosser J, Salisbury K, Hristov D. Telerobotic system concept for real-time soft-tissue imaging during radiotherapy beam delivery. Med Phys. 2010;37(12):6357–67.

    PubMed  Google Scholar 

  18. Camps SM, Houben T, Carneiro G, et al. Automatic quality assessment of transperineal ultrasound images of the male pelvic region, using deep learning. Ultrasound Med Biol. 2020;46(2):445–54.

    CAS  PubMed  Google Scholar 

  19. Camps SM, Verhaegen F, Vanneste BGL, de With PHN, Fontanarosa D. Automated patient-specific transperineal ultrasound probe setups for prostate cancer patients undergoing radiotherapy. Med Phys. 2018b;45(7):3185–95.

    PubMed  Google Scholar 

  20. Troccaz J, Laieb N, Vassal P, et al. Patient setup optimization for external conformal radiotherapy. J Image Guid Surg. 1995;1(2):113–20.

    CAS  PubMed  Google Scholar 

  21. Lattanzi J, McNeeley S, Donnelly S, et al. Ultrasound-based stereotactic guidance in prostate cancer--quantification of organ motion and set-up errors in external beam radiation therapy. Comput Aided Surg. 2000;5(4):289–95.

    CAS  PubMed  Google Scholar 

  22. Camps SM, Fontanarosa D, de With PHN, Verhaegen F, Vanneste BGL. The use of ultrasound imaging in the external beam radiotherapy workflow of prostate cancer patients. Biomed Res Int. 2018a;2018:7569590.

    PubMed  PubMed Central  Google Scholar 

  23. Fraser DJ, Wong P, Sultanem K, Verhaegen F. Dosimetric evolution of the breast electron boost target using 3D ultrasound imaging. Radiother Oncol. 2010;96(2):185–91.

    PubMed  Google Scholar 

  24. Bloemen-van Gurp E, van der Meer S, Hendry J, et al. Active breathing control in combination with ultrasound imaging: a feasibility study of image guidance in stereotactic body radiation therapy of liver lesions. Int J Radiat Oncol Biol Phys. 2013;85(4):1096–102.

    PubMed  Google Scholar 

  25. Mason SA, O’Shea TP, White IM, et al. Towards ultrasound-guided adaptive radiotherapy for cervical cancer: evaluation of Elekta’s semiautomated uterine segmentation method on 3D ultrasound images. Med Phys. 2017;44(7):3630–8.

    PubMed  Google Scholar 

  26. Fuss M, Salter BJ, Cavanaugh SX, et al. Daily ultrasound-based image-guided targeting for radiotherapy of upper abdominal malignancies. Int J Radiat Oncol Biol Phys. 2004;59(4):1245–56.

    PubMed  Google Scholar 

  27. Fuss M, Wong A, Fuller CD, Salter BJ, Fuss C, Thomas CR. Image-guided intensity-modulated radiotherapy for pancreatic carcinoma. Gastrointest Cancer Res. 2007;1(1):2–11.

    PubMed  PubMed Central  Google Scholar 

  28. Fuller CD, Thomas CR, Wong A, et al. Image-guided intensity-modulated radiation therapy for gallbladder carcinoma. Radiother Oncol. 2006;81(1):65–72.

    PubMed  Google Scholar 

  29. Fraser D, Fava P, Cury F, Vuong T, Falco T, Verhaegen F. Evaluation of a prototype 3D ultrasound system for multimodality imaging of cervical nodes for adaptive radiation therapy. In: Cleary KR, Miga MI, editors. Medical Imaging 2007: Visualization and Image-Guided Procedures. SPIE Proceedings. Bellingham: SPIE; 2007. p. 65090Y.

    Google Scholar 

  30. McBain CA, Green MM, Stratford J, et al. Ultrasound imaging to assess inter- and intra-fraction motion during bladder radiotherapy and its potential as a verification tool. Clin Oncol (R Coll Radiol). 2009;21(5):385–93.

    CAS  PubMed  Google Scholar 

  31. Gill R. The physics and technology of diagnostic ultrasound: a practitioner’s guide. High Frequency Publishing; Sydney, Australia. 2012.

    Google Scholar 

  32. Kataria T, Gupta D, Goyal S, et al. Simple diagrammatic method to delineate male urethra in prostate cancer radiotherapy: an MRI-based approach. Br J Radiol. 2016;89(1068):20160348.

    PubMed  PubMed Central  Google Scholar 

  33. Merritt CR. Doppler color flow imaging. J Clin Ultrasound. 1987;15(9):591–7.

    CAS  PubMed  Google Scholar 

  34. Bamber J, Cosgrove D, Dietrich CF, et al. EFSUMB guidelines and recommendations on the clinical use of ultrasound elastography. Part 1: basic principles and technology. Ultraschall in der Medizin (Stuttgart, Germany : 1980). 2013;34(2):169–84.

    CAS  PubMed  Google Scholar 

  35. Zhang P, Osterman KS, Liu T, et al. How does performance of ultrasound tissue typing affect design of prostate IMRT dose-painting protocols? Int J Radiat Oncol Biol Phys. 2007;67(2):362–8.

    PubMed  PubMed Central  Google Scholar 

  36. Wein W, Röper B, Navab N. Integrating diagnostic B-mode ultrasonography into CT-based radiation treatment planning. IEEE Trans Med Imaging. 2007;26(6):866–79.

    PubMed  Google Scholar 

  37. Fargier-Voiron M, Presles B, Pommier P, et al. Impact of probe pressure variability on prostate localization for ultrasound-based image-guided radiotherapy. Radiother Oncol. 2014;111(1):132–7.

    PubMed  Google Scholar 

  38. Li M, Hegemann N-S, Manapov F, et al. Prefraction displacement and intrafraction drift of the prostate due to perineal ultrasound probe pressure. Strahlenther Onkol. 2017b;193(6):459–65.

    PubMed  Google Scholar 

  39. Schlosser J, Hristov D. Radiolucent 4D ultrasound imaging: system design and application to radiotherapy guidance. IEEE Trans Med Imaging. 2016;35(10):2292–300.

    PubMed  Google Scholar 

  40. Bazalova-Carter M, Schlosser J, Chen J, Hristov D. Monte Carlo modeling of ultrasound probes for image-guided radiotherapy. Med Phys. 2015;42(10):5745–56.

    PubMed  PubMed Central  Google Scholar 

  41. Yan D, Vicini F, Wong J, Martinez A. Adaptive radiation therapy. Phys Med Biol. 1997;42(1):123–32.

    CAS  PubMed  Google Scholar 

  42. Birkner M, Yan D, Alber M, Liang J, Nüsslin F. Adapting inverse planning to patient and organ geometrical variation: algorithm and implementation. Med Phys. 2003;30(10):2822–31.

    CAS  PubMed  Google Scholar 

  43. Lim K, Stewart J, Kelly V, et al. Dosimetrically triggered adaptive intensity modulated radiation therapy for cervical cancer. Int J Radiat Oncol Biol Phys. 2014;90(1):147–54.

    PubMed  Google Scholar 

  44. Li T, Thongphiew D, Zhu X, et al. Adaptive prostate IGRT combining online re-optimization and re-positioning: a feasibility study. Phys Med Biol. 2011;56(5):1243–58.

    PubMed  Google Scholar 

  45. Gill S, Pham D, Dang K, et al. Plan of the day selection for online image-guided adaptive post-prostatectomy radiotherapy. Radiother Oncol. 2013;107(2):165–70.

    PubMed  Google Scholar 

  46. Sharfo AWM, Breedveld S, Voet PWJ, et al. Validation of fully automated VMAT plan generation for library-based plan-of-the-day cervical cancer radiotherapy. PLoS One. 2016;11(12):e0169202.

    PubMed  PubMed Central  Google Scholar 

  47. Camps S, van der Meer S, Verhaegen F, Fontanarosa D. Various approaches for pseudo-CT scan creation based on ultrasound to ultrasound deformable image registration between different treatment time points for radiotherapy treatment plan adaptation in prostate cancer patients. Biomed Phys Eng Exp. 2016;2(3):035018.

    Google Scholar 

  48. van der Meer S, Camps SM, van Elmpt WJC, et al. Simulation of pseudo-CT images based on deformable image registration of ultrasound images: a proof of concept for transabdominal ultrasound imaging of the prostate during radiotherapy. Med Phys. 2016;43(4):1913.

    PubMed  Google Scholar 

  49. Mason SA, White IM, O’Shea, T, et al. Combined ultrasound and cone-beam CT improves target segmentation for image-guided radiation therapy in uterine cervix cancer. Int J Radiat Oncol Biol Phys. 2019;104(3):685–93.

    PubMed  PubMed Central  Google Scholar 

  50. Bertholet J, Knopf A, Eiben B, et al. Real-time intrafraction motion monitoring in external beam radiotherapy. Phys Med Biol. 2019;64(15):15TR01.

    PubMed  PubMed Central  Google Scholar 

  51. Loblaw A. Ultrahypofractionation should be a standard of care option for intermediate-risk prostate cancer. Clin Oncol (R Coll Radiol). 2020;32(3):170–4.

    CAS  PubMed  Google Scholar 

  52. Tree A, Ostler P, van As N. New horizons and hurdles for UK radiotherapy: can prostate stereotactic body radiotherapy show the way? Clin Oncol (R Coll Radiol). 2014;26(1):1–3.

    CAS  PubMed  Google Scholar 

  53. Wang H, Jin C, Fang L, Sun H, Cheng W, Hu S. Health economic evaluation of stereotactic body radiotherapy (SBRT) for hepatocellular carcinoma: a systematic review. Cost Eff Res Alloc. 2020;18:1.

    Google Scholar 

  54. Richter A, Exner F, Weick S, et al. Evaluation of intrafraction prostate motion tracking using the clarity autoscan system for safety margin validation. Z Med Phys. 2020;30(2):135–41.

    PubMed  Google Scholar 

  55. Baker M, Cooper DT, Behrens CF. Evaluation of uterine ultrasound imaging in cervical radiotherapy; a comparison of autoscan and conventional probe. Br J Radiol. 2016;89(1066):20160510.

    PubMed  PubMed Central  Google Scholar 

  56. Mason SA, White IM, Lalondrelle S, Bamber JC, Harris EJ. The stacked-ellipse algorithm: an ultrasound-based 3-D uterine segmentation tool for enabling adaptive radiotherapy for uterine cervix cancer. Ultrasound Med Biol. 2020;46(4):1040–52.

    PubMed  PubMed Central  Google Scholar 

  57. Ballhausen H, Li M, Hegemann NS, Ganswindt U, Belka C. Intra-fraction motion of the prostate is a random walk. Phys Med Biol. 2015;60(2):549–63.

    CAS  PubMed  Google Scholar 

  58. Pommer T, Oh JH, Munck AF, Rosenschöld P, Deasy JO. Simulating intrafraction prostate motion with a random walk model. Adv Radiat Oncol. 2017;2(3):429–36.

    PubMed  PubMed Central  Google Scholar 

  59. Biston M-C, Zaragori T, Delcoudert L, et al. Comparison of electromagnetic transmitter and ultrasound imaging for intrafraction monitoring of prostate radiotherapy. Radiother Oncol. 2019;136:1–8.

    PubMed  Google Scholar 

  60. Han B, Najafi M, Cooper DT, et al. Evaluation of transperineal ultrasound imaging as a potential solution for target tracking during hypofractionated radiotherapy for prostate cancer. Radiat Oncol. 2018;13(1):151.

    PubMed  PubMed Central  Google Scholar 

  61. Richardson AK, Jacobs P. Intrafraction monitoring of prostate motion during radiotherapy using the clarity® autoscan Transperineal ultrasound (TPUS) system. Radiography (London, England : 1995). 2017;23(4):310–3.

    CAS  PubMed  Google Scholar 

  62. Sihono DSK, Ehmann M, Heitmann S, et al. Determination of Intrafraction prostate motion during external beam radiation therapy With a Transperineal 4-dimensional ultrasound real-time tracking system. Int J Radiat Oncol Biol Phys. 2018;101(1):136–43.

    PubMed  Google Scholar 

  63. Pang EPP, Knight K, Park SY, et al. Duration-dependent margins for prostate radiotherapy-a practical motion mitigation strategy. Strahlenther Onkol. 2020;196(7):657–63.

    PubMed  Google Scholar 

  64. Grimwood A, McNair HA, O’Shea TP, et al. In vivo validation of Elekta’s clarity autoscan for ultrasound-based Intrafraction motion estimation of the prostate during radiation therapy. Int J Radiat Oncol Biol Phys. 2018;102(4):912–21.

    PubMed  PubMed Central  Google Scholar 

  65. Li M, Ballhausen H, Hegemann N-S, et al. Comparison of prostate positioning guided by three-dimensional transperineal ultrasound and cone-beam CT. Strahlenther Onkol. 2017a;193(3):221–8.

    PubMed  Google Scholar 

  66. Fargier-Voiron M, Presles B, Pommier P, et al. Evaluation of a new transperineal ultrasound probe for inter-fraction image-guidance for definitive and post-operative prostate cancer radiotherapy. Phys Med. 2016;32(3):499–505.

    PubMed  Google Scholar 

  67. Molloy JA, Chan G, Markovic A, et al. Quality assurance of U.S.-guided external beam radiotherapy for prostate cancer: report of AAPM task group 154. Med Phys. 2011;38(2):857–71.

    PubMed  Google Scholar 

  68. Richter A, Polat B, Lawrenz I, et al. Initial results for patient setup verification using transperineal ultrasound and cone-beam CT in external beam radiation therapy of prostate cancer. Radiat Oncol. 2016;11(1):147.

    PubMed  PubMed Central  Google Scholar 

  69. De Luca V, Banerjee J, Hallack A, et al. Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound-guided liver radiotherapy margins. Med Phys. 2018;45(11):4986–5003.

    PubMed  Google Scholar 

  70. Rivaz H, Collins DL. Near real-time robust non-rigid registration of volumetric ultrasound images for neurosurgery. Ultrasound Med Biol. 2015;41(2):574–87.

    PubMed  Google Scholar 

  71. Wulff D, Kuhlemann I, Ernst F, Schweikard A, Ipsen S. Robust motion tracking of deformable targets in the liver using binary feature libraries in 4D ultrasound. Curr Direct Biomed Eng. 2019;5(1):601–4.

    Google Scholar 

  72. Boda-Heggemann J, Sihono DSK, Streb L, et al. Ultrasound-based repositioning and real-time monitoring for abdominal SBRT in DIBH. Phys Med. 2019;65:46–52.

    PubMed  Google Scholar 

  73. Betgen A, Alderliesten T, Sonke J-J, van Vliet-Vroegindeweij C, Bartelink H, Remeijer P. Assessment of set-up variability during deep inspiration breath hold radiotherapy for breast cancer patients by 3D-surface imaging. Radiother Oncol. 2013;106(2):225–30.

    PubMed  Google Scholar 

  74. Dhont J, Vandemeulebroucke J, Burghelea M, et al. The long- and short-term variability of breathing-induced tumor motion in lung and liver over the course of a radiotherapy treatment. Radiother Oncol. 2018;126(2):339–46.

    PubMed  Google Scholar 

  75. Vogel L, Sihono DSK, Weiss C, et al. Intra-breath-hold residual motion of image-guided DIBH liver-SBRT: an estimation by ultrasound-based monitoring correlated with diaphragm position in CBCT. Radiother Oncol. 2018;129(3):441–8.

    PubMed  Google Scholar 

  76. Mostafaei F, Tai A, Gore E, et al. Feasibility of real-time lung tumor motion monitoring using intrafractional ultrasound and kV cone-beam projection images. Med Phys. 2018;45(10):4619–26.

    CAS  PubMed  Google Scholar 

  77. Omari EA, Erickson B, Ehlers C, et al. Preliminary results on the feasibility of using ultrasound to monitor intrafractional motion during radiation therapy for pancreatic cancer. Med Phys. 2016;43(9):5252.

    PubMed  Google Scholar 

  78. Heimann R, Hard D. A comparison of three dimensional ultrasound, clips and CT for measuring interfractional breast lumpectomy cavity motion. J Nucl Med Radiat Ther. 2016;07(02)

    Google Scholar 

  79. Hilman S, Smith R, Masson S, et al. Implementation of a daily transperineal ultrasound system as image-guided radiotherapy for prostate cancer. Clin Oncol (R Coll Radiol). 2017;29(1):e49.

    CAS  PubMed  Google Scholar 

  80. Ghose S, Oliver A, Martí R, et al. A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Comput Methods Prog Biomed. 2012b;108(1):262–87.

    Google Scholar 

  81. Knoll C, Alcañiz M, Grau V, Monserrat C, Carmen Juan M. Outlining of the prostate using snakes with shape restrictions based on the wavelet transform (doctoral thesis: dissertation). Pattern Recogn. 1999;32(10):1767–81.

    Google Scholar 

  82. Zaim A, Jankun J. 2007. An energy-based segmentation of prostate from ultrasound images using dot-pattern select cells. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP ‘07. IEEE, pp. I-297-I–300.

    Google Scholar 

  83. Shen D, Zhan Y, Davatzikos C. Segmentation of prostate boundaries from ultrasound images using statistical shape model. IEEE Trans Med Imaging. 2003;22(4):539–51.

    PubMed  Google Scholar 

  84. Richard WD, Keen CG. Automated texture-based segmentation of ultrasound images of the prostate. Comput Med Imaging Graph. 1996;20(3):131–40.

    CAS  PubMed  Google Scholar 

  85. Zaim A. Automatic segmentation of the prostate from ultrasound data using feature-based self organizing map. In: Kalviainen H, Parkkinen J, Kaarna A, editors. Image analysis. Lecture notes in computer science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2005. p. 1259–65.

    Google Scholar 

  86. Akbari H, Yang X, Halig LV, Fei B. 3D segmentation of prostate ultrasound images using wavelet transform. Proc SPIE. 2011;7962:79622K.

    PubMed Central  Google Scholar 

  87. Ghose S, Mitra J, Oliver A, et al. A supervised learning framework for automatic prostate segmentation in trans rectal ultrasound images. In: Blanc-Talon J, Philips W, Popescu D, Scheunders P, Zemčík P, editors. Advanced concepts for intelligent vision systems. Lecture notes in computer science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012a. p. 190–200.

    Google Scholar 

  88. Nouranian S, Ramezani M, Spadinger I, Morris WJ, Salcudean SE, Abolmaesumi P. Learning-based multi-label segmentation of Transrectal ultrasound images for prostate brachytherapy. IEEE Trans Med Imaging. 2016;35(3):921–32.

    PubMed  Google Scholar 

  89. Zaim A, Yi T, Keck R. 2007. Feature-based classification of prostate ultrasound images using multiwavelet and kernel support vector machines. In: 2007 International Joint Conference on Neural Networks. IEEE, pp. 278–281.

    Google Scholar 

  90. Ghavami N, Hu Y, Bonmati E, et al. Automatic slice segmentation of intraoperative transrectal ultrasound images using convolutional neural networks. In: Webster RJ, Fei B, editors. Medical imaging 2018: image-guided procedures, robotic interventions, and modeling. Bellingham: SPIE; 2018. p. 2.

    Google Scholar 

  91. Lei Y, Tian S, He X, et al. Ultrasound prostate segmentation based on multidirectional deeply supervised V-net. Med Phys. 2019;46(7):3194–206.

    PubMed  PubMed Central  Google Scholar 

  92. Zhu N, Najafi M, Han B, Hancock S, Hristov D. Feasibility of image registration for ultrasound-guided prostate radiotherapy based on similarity measurement by a convolutional neural network. Technol Cancer Res Treat. 2019;18:1533033818821964.

    PubMed  PubMed Central  Google Scholar 

  93. De Luca V, Benz T, Kondo S, et al. The 2014 liver ultrasound tracking benchmark. Phys Med Biol. 2015;60(14):5571–99.

    PubMed  PubMed Central  Google Scholar 

  94. Gomariz A, Li W, Ozkan E, Tanner C, Goksel O. 2019. Siamese networks with location prior for landmark tracking in liver ultrasound sequences. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, pp. 1757–1760.

    Google Scholar 

  95. Gerlach S, Kuhlemann I, Jauer P, et al. Robotic ultrasound-guided SBRT of the prostate: feasibility with respect to plan quality. Int J Comput Assist Radiol Surg. 2017;12(1):149–59.

    PubMed  Google Scholar 

  96. Ipsen S, Bruder R, Kuhlemann I, et al. 2018. A visual probe positioning tool for 4D ultrasound-guided radiotherapy Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2018, pp. 883–886.

    Google Scholar 

  97. Brattain LJ, Telfer BA, Dhyani M, Grajo JR, Samir AE. Machine learning for medical ultrasound: status, methods, and future opportunities. Abdom Radiol (New York). 2018;43(4):786–99.

    Google Scholar 

  98. Schwaab J, Prall M, Sarti C, et al. Ultrasound tracking for intra-fractional motion compensation in radiation therapy. Phys Med. 2014;30(5):578–82.

    CAS  PubMed  Google Scholar 

  99. Kellnberger S, Assmann W, Lehrack S, et al. Ionoacoustic tomography of the proton Bragg peak in combination with ultrasound and optoacoustic imaging. Sci Rep. 2016;6:29305.

    PubMed  PubMed Central  Google Scholar 

  100. Journy N, Indelicato DJ, Withrow DR, et al. Patterns of proton therapy use in pediatric cancer management in 2016: an international survey. Radiother Oncol. 2019;132:155–61.

    PubMed  Google Scholar 

  101. Lagendijk JJW, Raaymakers BW, van Vulpen M. The magnetic resonance imaging-linac system. Semin Radiat Oncol. 2014;24(3):207–9.

    PubMed  Google Scholar 

  102. Ziegenhein P, Kamerling CP, Fast MF, Oelfke U. Real-time energy/mass transfer mapping for online 4D dose reconstruction. Sci Rep. 2018;8(1):3662.

    PubMed  PubMed Central  Google Scholar 

  103. Giger A, Stadelmann M, Preiswerk F, et al. Ultrasound-driven 4D MRI. Phys Med Biol. 2018;63(14):145015.

    PubMed  Google Scholar 

  104. Lee W, Chan H, Chan P, et al. A magnetic resonance compatible E4D ultrasound probe for motion management of radiation therapy. IEEE Netw. 2017;2017

    Google Scholar 

  105. Arteaga-Marrero N, Mainou-Gomez JF, Brekke Rygh C, et al. Radiation treatment monitoring with DCE-US in CWR22 prostate tumor xenografts. Acta Radiologica (Stockholm, Sweden : 1987). 2019;60(6):788–97.

    PubMed  Google Scholar 

  106. Detsky JS, Milot L, Ko Y-J, et al. Perfusion imaging of colorectal liver metastases treated with bevacizumab and stereotactic body radiotherapy. Phys Imaging Radiat Oncol. 2018;5:9–12.

    PubMed  PubMed Central  Google Scholar 

  107. Li H, Liu J, Chen M, Li H, Long L. Therapeutic evaluation of radiotherapy with contrast-enhanced ultrasound in non-Resectable hepatocellular carcinoma patients with portal vein tumor thrombosis. Med Sci Monit. 2018;24:8183–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Shiozawa K, Watanabe M, Ikehara T, et al. Evaluation of contrast-enhanced ultrasonography for hepatocellular carcinoma prior to and following stereotactic body radiation therapy using the CyberKnife® system: a preliminary report. Oncol Lett. 2016;11(1):208–12.

    PubMed  Google Scholar 

  109. Mabuchi S, Sasano T, Kuroda H, Takahashi R, Nakagawa S, Kimura T. Real-time tissue sonoelastography for early response monitoring in cervical cancer patients treated with definitive chemoradiotherapy: preliminary results. J Med Ultrason (2001). 2015;42(3):379–85.

    PubMed  Google Scholar 

  110. Xu Y, Zhu L, Liu B, et al. Strain elastography imaging for early detection and prediction of tumor response to concurrent chemo-radiotherapy in locally advanced cervical cancer: feasibility study. BMC Cancer. 2017;17(1):427.

    PubMed  PubMed Central  Google Scholar 

  111. Rafaelsen SR, Vagn-Hansen C, Sørensen T, Lindebjerg J, Pløen J, Jakobsen A. Ultrasound elastography in patients with rectal cancer treated with chemoradiation. Eur J Radiol. 2013;82(6):913–7.

    CAS  PubMed  Google Scholar 

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Fontanarosa, D. et al. (2022). Ultrasonography in Image-Guided Radiotherapy: Current Status and Future Challenges. In: Troost, E.G.C. (eds) Image-Guided High-Precision Radiotherapy. Springer, Cham. https://doi.org/10.1007/978-3-031-08601-4_9

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  • DOI: https://doi.org/10.1007/978-3-031-08601-4_9

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