Technology of Ultrasound-Guided Therapy



The first published medical use of ultrasound was in 1942 when Dr. Karl Dussik measured transmission attenuation through the head to diagnose brain tumors. Twenty years later, Berlyne was the first to use ultrasound to guide a needle, in this case for renal biopsies. Since then, ultrasound has grown into the multidimensional, multimodality technology of today, used daily for image-guided therapies.


Attenuation Paclitaxel Doxorubicin Curcumin Rapamycin 


The first published medical use of ultrasound was in 1942 when Dr. Karl Dussik measured transmission attenuation through the head to diagnose brain tumors [1]. Twenty years later, Berlyne was the first to use ultrasound to guide a needle, in this case for renal biopsies [2]. Since then, ultrasound has grown into the multidimensional, multimodality technology of today, used daily for image-guided therapies.

Ultrasound has several valuable qualities for image-guided therapy, primarily its high frame rate, portability, and safety. It can be incorporated easily into almost all medical and surgical environments and can be used repeatedly without risk of ionizing radiation. It can be used to assess the dynamic aspects of procedures, such as tissue and instrument motion. The ultrasound signal also carries a great deal of information for visualizing structure, blood flow, stiffness, and other tissue properties from a single device. A wide range of transducers are available, allowing imaging both from inside and outside the body. Depending on the transducer, a user can image across an entire organ or focus on detailed substructure over a small area.

This chapter reviews ultrasound technology for use in image-guided therapy. While it is assumed that most readers are familiar with the subject, some basic principles and uses of ultrasound are included for completeness. Following this, the subset of ultrasound technologies and applications relevant to image-guided therapy via tissue characterization and guidance are presented.

Ultrasound Imaging Modes

Ultrasound imaging is based on focused transmission and reception of sound pulses in the MHz range along multiple adjacent scan lines emanating from the transducer face. Pulses received along each scan line are combined to form images (Fig. 10.1) and to measure tissue properties. Many texts are available covering the physics, design, and operation of ultrasound imaging systems and transducers; a good example is by Thomas Szabo [3].
Fig. 10.1

An example of B-mode ultrasound image of the liver with an enhancing mass

Ultrasound transducers are available in a wide variety of form factors and frequency ranges. Standard types include linear, curved linear, and vector, which produce 2-dimensional images and are primarily used on the surface of the body. Wobbler and matrix transducers produce 3-dimensional images. A “wobbler” is a transducer with an embedded motor, which moves an array through a prescribed arc. A matrix array can steer scan lines both laterally and in elevation to interrogate a volume of tissue. Example volume images are shown in Figs. 10.2. Many transducer variants also exist for use inside the body, including endocavitary, intraoperative, laparoscopic, endoscopic, transesophageal, intracardiac echo (ICE), and intravascular (IVUS).
Fig. 10.2

An example of volume ultrasound of a fetus acquired with a wobbler transducer

B-Mode and Doppler

Most often associated with ultrasound, B-Mode imaging displays the intensity of sonic reflectors throughout the imaging region, which reveals the gross structure of tissue, as shown in Fig. 10.1. Specific B-mode features have been identified, which are characteristic of lesion type and malignancy [4]. Also commonly associated with ultrasound, Doppler imaging is a technique for displaying motion, in particular blood flow, using the phase shift between transmitted and received ultrasound pulses with color indicating direction, as shown in Fig. 10.3.
Fig. 10.3

An example of Doppler image of the carotid artery

B-mode imaging is used routinely for guidance of needles for biopsy or ablation [5, 6]. Metal needles are often visible due to the high impedance difference versus the surrounding tissue, as shown in Fig. 10.4. This large difference along with the linear needle geometry, however, can also lead to a variety of artifacts, which affect visualization [7]. Several methods have been explored to enhance the visibility of needles, including steering scan lines perpendicular to needles [8, 9], adding echogenic coatings to needles [10, 11], and post-processing images to enhance needle visualization [12, 13, 14]. Vibrating needles mechanically has also been investigated; the slight needle motion produces a signal under Doppler imaging [15].
Fig. 10.4

An example image showing a needle with an echogenic coating applied near the tip


Ultrasound contrast agents enhance visualization of tissue boundaries, Doppler blood flow, tissue perfusion, and vasculature, as shown in Fig. 10.5. Several agents are available; all are composed of different types of two components: a microbubble shell and a gas core. Microbubbles are bright reflectors of sound as compared to tissue and produce a characteristic nonlinear response to ultrasound frequencies. Thus, contrast-specific pulse sequences can be used to detect agents with high sensitivity and visualize contrast separate from tissue [16, 17]. Common uses include detection of micro-flow as with malignant lesions [18], lesion detection, and characterization through perfusion, as in Fig. 10.6, [19, 20] and percutaneous ablation therapy for both targeting and follow-up [21]. Contrast can be used immediately after ablation therapy to identify residual tumor.
Fig. 10.5

An example of contrast ultrasound image showing a hypovascular liver lesion

Fig. 10.6

An example contrast perfusion image showing peak contrast intensity as a colorized map. Red indicates high intensity and blue indicates low intensity

Recent investigations of 3-dimensional contrast quantification have shown that the reproducibility and quality of quantification is much greater than in two dimensions [22, 23]. Such techniques may enable more precise determination of a patient’s response to therapy to more rapidly optimize treatment. Another active research area is targeted contrast. In this case, contrast microbubbles are coated with ligands designed to preferentially bind to proteins expressed within the body by specific diseases or disease states [24]. For instance, a VEGF receptor may preferentially bind to angiogenic factors, highlighting areas of possible tumor growth [25]. Such techniques would enable both early and more precise therapy by detecting areas where therapy may be most effective.

Finally, while microbubble contrast agents remain intact under low sonic intensity, they collapse and break apart under only slightly higher intensity. This contrast destruction offers a potential vector for targeted drug delivery [26]. Microbubbles can be filled with drugs, which are nonreactive while contained, and then released preferentially in target tissues under ultrasound visualization and control. Examples of drugs being investigated include rapamycin, curcumin, doxorubicin, and paclitaxel [27, 28].


Elastography is primarily used for detection, characterization, and targeting of lesions for biopsy or therapy. When tissue undergoes stress due to either pressure at the surface of the body, instrument manipulation, or acoustic force, the resulting displacement can be visualized in ultrasound via motion tracking. Stiff regions compress less than soft regions, so an elastography image represents a map of relative tissue stiffness, as shown in Fig. 10.7.
Fig. 10.7

An example of elastography image of a hard thyroid mass obtained via manual compression. Red indicates low compression and blue indicates high compression

Elastography based on manual compression has been investigated for identifying and characterizing suspicious lesions in many organs, including the breast, prostate, and thyroid [29]. Barr et al. report, for instance, that a ratio of the longest dimension of a lesion in elastography versus B-mode greater than one is characteristic of malignancy [30]. Other studies characterize lesions based on the ratio of stiffness inside and outside a lesion, known as the strain ratio [31]. Manual elastography has also been used to visualize lesions both before and after radiofrequency ablation [32, 33].

Acoustic Radiation Force Impulse (ARFI) is a novel method of achieving tissue compression at greater depths and with greater consistency than that achievable through surface compression. This technique uses the sonic pulse, known as a “push pulse” to compress tissue along a scan line. Like manual elastography, ARFI has been investigated for lesion characterization [34, 35] and evaluating lesions before and after radiofrequency ablation [36].

Beyond relative stiffness, quantitative measurement of tissue modulus holds promise for more precise localization and characterization of lesions. Compression elastography, however, cannot quantify modulus because physical boundary conditions cannot be controlled [37]. Recently a novel technique has been developed to achieve this quantification, which measures the speed of shear waves generated by an ARFI push pulse [38]. Shear waves travel perpendicular to the push pulse, and their velocity is proportional to tissue modulus and independent of push pulse intensity. A related technique called supersonic shear imaging uses multiple push pulses at progressively increasing depth to create shear waves, which constructively interfere to form plane shear waves [36]. Using imaging at up to 5,000 frames/s, the velocity of these plane shear waves are measured to produce a map of tissue elasticity.


Ultrasound-based navigation involves both moving a transducer to image target anatomy and moving an instrument to a particular target location. Example procedures include biopsy, percutaneous ablation, stereotactic neurosurgery, and radiotherapy. In performing such tasks, spatial tracking systems, which report the position and orientation of devices with respect to a base coordinate system, can help maintain spatial awareness. A common application of this tracking data is to display graphical overlays on ultrasound images indicating a needle’s axis and tip location [39, 40], as shown in Fig. 10.8. These help ensure accurate placement if needles are not well visualized and add depth information to guide needles from out of the image plane. Tracked ultrasound is also useful for adjusting interventional plans based on tissue motion, such as brain shift experienced during neurosurgery [41]. More advanced targeted therapeutic devices, such as extracorporeal radiotherapy, have their own articulation, and tracked ultrasound can be used to spatially localize the therapeutic target to position the devices correctly [42, 43]. Robotic surgical systems employ similar techniques [44].
Fig. 10.8

An example of tracked needle display. The grey line indicates the needle axis. Colors along the line indicate the needle location and depth. Blue indicates portions in front of the image. Red indicates portions behind the image. Green indicates portions in the image plane

Tracking also enables fusion imaging, in which ultrasound is displayed simultaneously with another volume modality, such as CT or MRI, as shown in Fig. 10.9. During live scanning, tracking information is used to reconstruct slices from the volume data, which correspond to the ultrasound image. Thus, the same anatomy is observed from the same angle from two modalities. Seeing the same anatomy in two different modalities enables a physician to correlate structures or use one modality to fill in the information missing in the other. For instance, fusion has been shown to assist with the particularly difficult navigation of laparoscopic [45] and endoscopic transducers [46].
Fig. 10.9

An example fusion image showing the hepatic vasculature in the liver. The image on the left is CT. The image on the right is ultrasound. Both are obtained from the same oblique plane

Various tools have been investigated for aligning the volume data with the tracking system for fusion display [47, 48, 49, 50]. Clinical implementation of fusion imaging has suffered, however, due to the time required to achieve adequate alignment using traditional methods. Recent advancements in automatic image analysis may potentially reduce this time greatly.


Given the variety of technologies available both for imaging and navigation, it is clear that ultrasound offers tremendous value and potential. Whether for detecting, localizing, characterizing, or treating lesions, the variety of imaging modalities, including B-mode, Doppler, contrast, and elastography, and the variety of transducer types provide a powerful toolkit all in a single machine. As research progresses and yet more modalities and transducers become available, ultrasound will no doubt continue to be a mainstay of image-guided therapy well into the future.


  1. 1.
    Dussik KT. On the possibility of using ultrasound waves as a diagnostic. Aid Neurol Psychiatr. 1942;174:153–68.CrossRefGoogle Scholar
  2. 2.
    McGahan JP. The history of interventional ultrasound. J Ultrasound Med. 2004;23:727–41.PubMedGoogle Scholar
  3. 3.
    Szabo TL. Diagnostic ultrasound imaging: inside out. Waltham, Massachusetts: Academic Press; 2004.Google Scholar
  4. 4.
    Bezzi M, Silecchia G, De Leo A, Carbone I, Pepino D, Rossi P. Laparoscopic and intraoperative ultrasound. Eur J Radiol. 1998;27 Suppl 2:S207–14.PubMedCrossRefGoogle Scholar
  5. 5.
    Gervais D, Sabharwal T. Interventional radiology procedures in biopsy and drainage. New York: Springer; 2011.CrossRefGoogle Scholar
  6. 6.
    Van Sonnenberg E, McMullen W, Solbiati L. Tumor ablation: principles and practice. New York: Springer; 2005.CrossRefGoogle Scholar
  7. 7.
    Huang J, Triedman JK, Vasilyev NV, Suematsu Y, Cleveland RO, Dupont PE. Imaging artifacts of medical instruments in ultrasound-guided interventions. J Ultrasound Med. 2007;26:1303–22.PubMedGoogle Scholar
  8. 8.
    Baker JA, Soo MS, Mengoni P. Sonographically guided percutaneous interventions of the breast using steerable ultrasound beam. AJR Am J Roentgenol. 1999;172:157–9.PubMedCrossRefGoogle Scholar
  9. 9.
    Cheung S, Rohling R. Enhancement of needle visibility in ultrasound-guided percutaneous procedures. Ultrasound Med Biol. 2004;30:617–24.PubMedCrossRefGoogle Scholar
  10. 10.
    Hopkins RE, Bradley M. In-vitro visualization of biopsy needles with ultrasound: a comparative study of standard and echogenic needles using an ultrasound phantom. Clin Radiol. 2001;56: 499–502.PubMedCrossRefGoogle Scholar
  11. 11.
    Nichols K, Wright LB, Spencer T, Culp WC. Changes in ultrasonographic echogenicity and visibility of needles with changes in angles of insonation. J Vasc Interv Radiol. 2003;14:1553–7.PubMedCrossRefGoogle Scholar
  12. 12.
    Okazawa SH, Ebrahimi R, Chuang J, Rohling R, Salcudean SE. Methods for segmenting curved needles in ultrasound images. Med Image Anal. 2006;10:330–42.PubMedCrossRefGoogle Scholar
  13. 13.
    Ding M, Wei Z, Gardi L, Downey DB, Fenster A. Needle and seed segmentation in intra-operative 3D ultrasound-guided prostate brachytherapy. Ultrasonics. 2006;44:331–6.CrossRefGoogle Scholar
  14. 14.
    Novotny P, Stoll J, Vasilyev NV, del Nido PJ, Dupont PE, Zickler TE, Howe RD. GPU based real-time instrument tracking with three-dimensional ultrasound. Med Image Anal. 2007;11:458–64.PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Enhanced ultrasound guided visualization. Accessed 20 June 2012.
  16. 16.
    Cosgrove D. Ultrasound contrast agents: an overview. Eur J Radiol. 2006;60:324–30.PubMedCrossRefGoogle Scholar
  17. 17.
    Claudon M, Cosgrove D, Albrecht T, Bolondi L, Bosio M, Calliada F, Correas JM, Darge K, Dietrich C, D’Onofrio M, Evans DH, Filice C, Greiner L, Jager K, de Jong N, Leen E, Lencioni R, Lindsell D, Martegani A, Meairs S, Nolsoe C, Piscaglia F, Ricci P, Seidel G, Skjoldbye B, Solbiati L, Thorelius L, Tranquart F, Weskott HP, Whittingham T. Guidelines and good clinical practice recommendations for contrast enhanced ultrasound (CEUS) – update 2008. Ultrasound Med. 2008;29:28–44.Google Scholar
  18. 18.
    Greis C. Ultrasound contrast agents as markers of vascularity and microcirculation. Clin Hemorheol Microcirc. 2009;43:1–9.PubMedGoogle Scholar
  19. 19.
    Seitz K, Strobel D, Bernatij T, Blank W, Freidrich-Rust W, von Herbay A, Dietrich CF, Strunk H, Kratzer W, Schuler A. Contrast-enhanced ultrasound (CEUS) for the characterization of focal liver lesions – prospective comparison in clinical practice: CEUS vs CT. Ultrasound Med. 2009;30:383–9.Google Scholar
  20. 20.
    Jang HJ, Yu H, Kim TK. Contrast-enhanced ultrasound in the detection and characterization of liver tumors. Cancer Imaging. 2009;9:96–103.PubMedCentralPubMedGoogle Scholar
  21. 21.
    Meloni MF, Livraghi T, Filice C, Lazzaroni S, Calliada F, Perretti L. Radiofrequency ablation of liver tumors: the role of microbubble ultrasound contrast agents. Ultrasound Q. 2006;22:41–7.PubMedGoogle Scholar
  22. 22.
    Leen E, Kumar S, Khan SA, Low G, Ong KO, Tait P, Averkiou M. Contrast-enhanced 3D ultrasound in the radiofrequency ablation of liver tumors. World J Gastroenterol. 2009;15:289–99.PubMedCrossRefGoogle Scholar
  23. 23.
    Feingold S, Gessner R, Guracar I, Dayton P. Quantitative volumetric perfusion mapping of the microvasculature using contrast ultrasound. Invest Radiol. 2010;45:669–74.PubMedCrossRefGoogle Scholar
  24. 24.
    Piedra M, Allroggen A, Lindner J. Molecular imaging with targeted contrast ultrasound. Cerebrovasc Dis. 2009;27 Suppl 2:66–74.PubMedCrossRefGoogle Scholar
  25. 25.
    Anderson CR, Rychak J, Backer M, Backer J, Ley K, Klibanov A. ScVEGF microbubble ultrasound contrast agents: a novel probe for ultrasound molecular imaging of tumor angiogenesis. Invest Radiol. 2010;45:579–85.PubMedCrossRefGoogle Scholar
  26. 26.
    Qin S, Caskey CF, Ferrara KW. Ultrasound contrast microbubbles in imaging and therapy: physical principles and engineering. Phys Med Biol. 2009;54:27–57.CrossRefGoogle Scholar
  27. 27.
    Phillips L, Dhanaliwala AH, Klibanov A, Hossack J, Wamhoff BR. Focused ultrasound-mediated drug delivery from microbubbles reduces drug dose necessary for therapeutic effect on neointima formation—brief report. J Nucl Med. 2012;53:345–8.CrossRefGoogle Scholar
  28. 28.
    Nagaraja AS. Curcumin loaded ultrasound contrast agents for drug delivery to tumor cells. Thesis, Drexel University; 2010.
  29. 29.
    Ginat DT, Destounis SV, Barr RG, Castaneda B, Strang J, Rubens D. US elastography of breast and prostate lesions. Radiographics. 2009;29:2007–16.PubMedCrossRefGoogle Scholar
  30. 30.
    Barr RG, Destounis S, Lackey LB, Svensson WE, Balleyguier C, Smith C. Evaluation of breast lesions using sonographic elasticity imaging a multicenter trial. J Ultrasound Med. 2012;31:281–7.PubMedGoogle Scholar
  31. 31.
    Thomas A, Degenhardt F, Farrokh A, Wojcinski S, Slowinski T, Fischer T. Significant differentiation of focal breast lesions: calculation of strain ratio in breast sonoelastography. Acad Radiol. 2010;17:558–63.PubMedCrossRefGoogle Scholar
  32. 32.
    Kolokythas O, Gauthier T, Fernandez AT, Xie H, Timm BA, Cuevas C, Dighe MK, Mitsumori LM, Bruce MF, Herzka DA, Goswami GK, Andrews RT, Oas KM, Dubinsky TJ, Warren BH. Ultrasound-based elastography: a novel approach to assess radio frequency ablation of liver masses performed with expandable ablation probes. J Ultrasound Med. 2008;27:935–46.PubMedGoogle Scholar
  33. 33.
    Varghese T, Techavipoo U, Liu W, Zagzebski JA, Chen Q, Frank G, Lee FT. Elastographic measurements of the area and volume of thermal lesions resulting from radiofrequency ablation: pathologic correlation. AJR Am J Roentgenol. 2003;181:701–7.PubMedCrossRefGoogle Scholar
  34. 34.
    Nightingale K. Acoustic radiation force impulse (ARFI) imaging: a review. Curr Med Imaging Rev. 2011;7:328–39.PubMedCentralPubMedCrossRefGoogle Scholar
  35. 35.
    Fahey BJ, Nelson RC, Bradway DP, Hsu SJ, Dumont DM, Trahey GE. In vivo visualization of abdominal malignancies with acoustic radiation force elastography. Phys Med Biol. 2008;53:279.PubMedCentralPubMedCrossRefGoogle Scholar
  36. 36.
    Fahey BJ, Nelson RC, Hsu SJ, Bradway DP, Dumont DM, Trahey GE. In vivo guidance and assessment of liver radio-frequency ablation with acoustic radiation force elastography. Ultrasound Med Biol. 2008;34:1590–603.PubMedCentralPubMedCrossRefGoogle Scholar
  37. 37.
    Thitaikumar A, Ophir J. Effect of lesion boundary conditions on axial strain elastograms: a parametric study. Ultrasound Med Biol. 2007;33:1463–7.PubMedCentralPubMedCrossRefGoogle Scholar
  38. 38.
    Bercoff J, Tanter M, Fink M. Supersonic shear imaging: a new technique for soft tissue elasticity mapping. IEEE Trans Ultrason Ferroelectr Freq Control. 2004;51:396–409.PubMedCrossRefGoogle Scholar
  39. 39.
    Hakime A, Deschamps F, De Carvalho EGM, Barah A, Auperin A, De Baere T. Electromagnetic-tracked biopsy under ultrasound guidance: preliminary results. Cardiovasc Intervent Radiol. 2012;35:898–905.PubMedCrossRefGoogle Scholar
  40. 40.
    Stippel D, Bohm S, Beckurts T, Brochhagen H, Holscher A. Experimental evaluation of accuracy of radiofrequency ablation using conventional ultrasound or a third-dimension navigation tool. Langenbecks Arch Surg. 2002;387:303–8.PubMedCrossRefGoogle Scholar
  41. 41.
    Lunn K, Paulsen K, Roberts D, Kennedy F, Hartov A, West J. Displacement estimation with co-registered ultrasound for image-guided neurosurgery: a quantitative in-vivo porcine study. IEEE Trans Med Imaging. 2003;22:1358–68.PubMedCrossRefGoogle Scholar
  42. 42.
    Langen KM, Pouliot J, Anezinos C, Aubin M, Gottschalk AR, Hsu IC, Lowther D, Liu YM, Shinohara K, Verhey LJ, Weinberg V, Roach M. Evaluation of ultrasound-based prostate localization for image-guided radiotherapy. Int J Radiat Oncol Biol Phys. 2003;57:635–44.PubMedCrossRefGoogle Scholar
  43. 43.
    Bouchet L, Meeks S, Goodchild G, Bova F, Buatti J, Friedman W. Calibration of three-dimensional ultrasound images for image-guided radiation therapy. Phys Med Biol. 2001;46:559.PubMedCrossRefGoogle Scholar
  44. 44.
    Taylor RH, Stoianovici D. Medical robotics in computer-integrated surgery. IEEE Trans Robot Automat. 2003;19:765–81.CrossRefGoogle Scholar
  45. 45.
    Ellsmere J, Stoll J, Wells W, Kikinis R, Vosburgh K, Kane R, Brooks D, Rattner D. A new visualization technique for laparoscopic ultrasonography. Surgery. 2004;136:84–92.PubMedCrossRefGoogle Scholar
  46. 46.
    Estepar R, Stylopoulos N, Ellis R, Samset E, Westin CF, Thompson C, Vosburgh K. Towards scarless surgery: an endoscopic ultrasound navigation system for transgastric access procedures. Comput Aided Surg. 2007;12:311–24.PubMedCrossRefGoogle Scholar
  47. 47.
    Arbel T, Morandi X, Comeau RM, Collins DL. Automatic non-linear MRI-ultrasound registration for the correction of intra-operative brain deformations. Comput Aided Surg. 2004;9:123–36.PubMedGoogle Scholar
  48. 48.
    Penney GP, Blackalla JM, Hamadyb MS, Sabharwalb T, Adamb A, Hawkes DJ. Registration of freehand 3D ultrasound and magnetic resonance liver images. Med Image Anal. 2004;8:81–91.PubMedCrossRefGoogle Scholar
  49. 49.
    Lange T, Eulenstein S, Hünerbein M, Schlag P. Vessel-based non-rigid registration of MR/CT and 3D ultrasound for navigation in liver surgery. Comput Aided Surg. 2003;8:228–40.PubMedCrossRefGoogle Scholar
  50. 50.
    Wein W, Brunke S, Khamene A, Callstrom MR, Navab N. Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention. Med Image Anal. 2008;12:577–85.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Division of UltrasoundSiemens HealthcareMountain ViewUSA

Personalised recommendations