Rationale and Objectives
Although contrast-enhanced ultrasound imaging techniques such as harmonic imaging (HI) have evolved to reduce tissue signals using the nonlinear properties of the contrast agent, levels of background suppression have been mixed. Subharmonic imaging (SHI) offers near complete tissue suppression by centering the receive bandwidth at half the transmitting frequency. The aims of this study were to demonstrate the feasibility of three-dimensional (3D) SHI and to compare it to 3D HI.
Materials and Methods
Three-dimensional HI and SHI were implemented on a Logiq 9 ultrasound scanner with a 4D10L probe. Four-cycle SHI was implemented to transmit at 5.8 MHz and receive at 2.9 MHz, while two-cycle HI was implemented to transmit at 5 MHz and receive at 10 MHz. The ultrasound contrast agent Definity was imaged within a flow phantom and the lower pole of two canine kidneys in both HI and SHI modes. Contrast-to-tissue ratios and rendered images were compared offline.
Results
SHI resulted in significant improvement in contrast-to-tissue ratios relative to HI both in vitro (12.11 ± 0.52 vs 2.67 ± 0.77, P < .001) and in vivo (5.74 ± 1.92 vs 2.40 ± 0.48, P = .04). Rendered 3D subharmonic images provided better tissue suppression and a greater overall view of vessels in a flow phantom and canine renal vasculature.
Conclusions
The successful implementation of SHI in 3D allows imaging of vascular networks over a heterogeneous sample volume and should improve future diagnostic accuracy. Additionally, 3D SHI provides improved contrast-to-tissue ratios relative to 3D HI.
Despite its advantages in cost, portability, and real-time nature, ultrasound suffers from low blood-to-tissue contrast relative to other imaging modalities. Segmentation of vascular formations from the surrounding tissue is clinically useful in a variety of radiologic applications. For example, malignant lesions often show a more tortuous, sporadic vascular network relative to benign lesions . This more chaotic growth pattern can often be quantified by the number of bifurcations , microvascular density , or fractal dimensionality as a means for differentiation of diseased from healthy tissue. Additionally, temporal parameters within the vasculature such as perfusion or filling time of intravenous contrast can also be used for the identification and classification of diseased tissue .
Although the feasibility of experimental, nanoscale agents has been demonstrated , clinically approved ultrasound contrast agents are small gas bubbles stabilized by an outer shell (generally composed of protein or phospholipids), with diameters ranging from 1 to 8 μm that constrain their distribution within the body to the blood supply . The differences in the compressibility and acoustical impedance of the gas cores within these microbubbles relative to the surrounding fluid (roughly 20,000× and 250×, respectively, for most gases) result in up to 30 dB of vascular enhancement . Numerous studies of the use of contrast-enhanced ultrasound for lesion characterization have been reported, although results have been mixed , in part because of an inability to distinguish signals from the contrast agent, perfused within the microvasculature, and signals from the surrounding tissue.
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Materials and methods
Modification of a Commercial Scanner
Get Radiology Tree app to read full this article<
Validation of Subharmonic Generation In Vitro
Get Radiology Tree app to read full this article<
In Vitro HI and SHI
Get Radiology Tree app to read full this article<
In Vivo HI and SHI
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Image Analysis
Get Radiology Tree app to read full this article<
CTR=2(γV−γT)2σ2V+σ2T, CTR
=
2
(
γ
V
−
γ
T
)
2
σ
V
2
+
σ
T
2
,
where γ V and γ T represent the mean backscatter signal strength in the vessel and tissue, respectively, and σ2V σ
V
2 and σ2T σ
T
2 represent the variance in the respective ROIs . In vitro CTRs were calculated from the ROIs as shown in Figure 1 . The vessel ROI was selected as a square region of 45 to 55 pixels (approximately 20 mm 2 ) within the vessel lumen. Four equidistant, equal-sized tissue ROIs along the same horizontal and vertical axes as the vessel ROI were then selected and averaged together. This process was performed for each plane within the volume (45–50 slices/volume), for each volume over the 5-second period (9 to 11 volumes depending of volume acquisition rate). Final in vitro CTR was calculated as the average of three repetitions of 5-second acquisitions.
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Results
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Table 1
In Vitro and In Vivo CTRs
3D Harmonic Imaging 3D Subharmonic Imaging_P_ In vitro CTR 2.67 ± 0.77 12.11 ± 1.52 .0007 In vivo CTR 2.40 ± 0.48 5.74 ± 1.92 .04
CTR, contrast-to-tissue ratio; 3D, three-dimensional.
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Discussion
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Conclusions
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
References
1. Weidner N., Folkman J., Pozza F., et. al.: Tumor angiogenesis: a new significant and independent prognostic indicator in earlystage breast cancer. J Natl Cancer Inst 1992; 84: pp. 1875-1887.
2. Less J.R., Skalak T.C., Sevick E.M., et. al.: Microvascular architecture in a mammary carcinoma: branching patterns and vessel dimensions. Cancer Res 1991; 51: pp. 265-273.
3. Mavroforakis M.E., Georgiou H.V., Dimitropoulos N., et. al.: Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers. Artif Intell Med 2006; 37: pp. 145-162.
4. Goldberg B.B., Raichlen J.S., Forsberg F.: Ultrasound Contrast Agents: Basic Principles and clinical Applications.2nd ed.2001.Martin DunitzLondon, United Kingdom
5. Lassau N., Chami L., Chebil M., et. al.: Dynamic contrast-enhanced ultrasonography (DCE-US) and anti-angiogenic treatments. Discover Med 2011; 11: pp. 18-24.
6. Chinchure S., Thomas B., Wanju S., et. al.: Mean intensity curve on dynamic contrast-enhanced susceptibility-weighted perfusion MR imaging- a review of a new parameter to differentiate intracranial tumors. J Neuroradiol 2011; 38: pp. 199-206.
7. Hughes M.S., Marsh J.N., Hall C.S., et. al.: Acoustic characterization in whole blood and plasma of site-targeted nanoparticle ultrasound contrast agent for molecular imaging. J Acoust Soc Am 2005; 117: pp. 964-972.
8. Casciaro S., Conversano F., Raqusa A., et. al.: Optimal enhancement configuration of silica nanoparticles for ultrasound imaging and automatic detection at conventional diagnostic frequencies. Invest Radiol 2010; 45: pp. 715-724.
9. Leighton T.J.: The Acoustic Bubble.1994.Academic PressLondon, United Kingdom
10. Chaudhari M.H., Forsberg F., Voodarala A., et. al.: Breast tumor vascularity identified by contrast enhanced ultrasound and pathology: initial results. Ultrasonics 2000; 38: pp. 105-109.
11. Fleischer A.C.: Sonographic depiction of tumor vascularity and flow: from in vivo models to clinical models. J Ultrasound Med 2000; 19: pp. 55-61.
12. Ferrara K.W., Merritt C.R., Burns P., et. al.: Evaluation of tumor angiogenesis with US: imaging, Doppler, and contrast agents. Acad Radiol 2000; 7: pp. 824-839.
13. Hamilton M.F., Blackstock D.T.: Nonlinear Acoustics.1998.Academic PressSan Diego, CA
14. Shankar P.M., Krishna P.D., Newhouse V.L.: Advantages of subharmonic over second harmonic backscatter for contrast-to-tissue echo enhancement. Ultrasound Med Biol 1998; 24: pp. 395-399.
15. Shi W.T., Forsberg F., Hall A.L., et. al.: Subharmonic imaging with microbubble contrast agents: initial results. Ultrason Imaging 1999; 21: pp. 79-94.
16. Forsberg F., Shi W.T., Goldberg B.B.: Subharmonic imaging of contrast agents. Ultrasonics 2000; 38: pp. 93-98.
17. Forsberg F., Liu J.B., Shi W.T., et. al.: In vivo perfusion estimation using subharmonic contrast microbubble signals. J Ultrasound Med 2006; 25: pp. 15-21.
18. Forsberg F., Piccoli C., Merton D.A., et. al.: Breast lesions: imaging with contrast-enhanced subharmonic US—initial experience. Radiology 2007; 244: pp. 718-726.
19. Chomas J., Dayton P., May D., et. al.: Nondestructive subharmonic imaging. IEEE Trans Ultrason Ferroelectr Freq Control 2002; 49: pp. 883-892.
20. Goertz D.E., Frijlink M.E., Tempel D., et. al.: Subharmonic contrast intravascular ultrasound for vasa vasorum imaging. Ultrasound Med Biol 2007; 33: pp. 1859-1872.
21. Faez T., Emmer M., Docter M., et. al.: Characterizing the subharmonic response of phospholipid-coated microbubbles for carotid imaging. Ultrasound Med Biol 2011; 37: pp. 958-970.
22. Needles A., Goertz D.E., Karshafian R., et. al.: High-frequency subharmonic pulsed-wave Doppler and color flow imaging of microbubble contrast agents. Ultrasound Med Biol 2008; 34: pp. 1139-1151.
23. Dave J.K., Forsberg F., Fernandes S., et. al.: Static and dynamic cumulative maximum intensity display mode for subharmonic imaging—a comparative study with mammography and conventional ultrasound techniques. J Ultrasound Med 2010; 29: pp. 1177-1185.
24. Eisenbrey J.R., Dave J.K., Merton D.A., et. al.: Parametric imaging using subharmonic signals from ultrasound contrast agents in patients with breast lesions. J Ultrasound Med 2011; 30: pp. 85-92.
25. US Food and Drug Administration. Guidance for industry and FDA staff—information for manufacturers seeking marketing clearance of diagnostic ultrasound systems and transducers. Available at: http://www.fda.gov/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm070856.htm . Accessed March 11, 2012.
26. Wilson S.R., Jang H.J., Kim T.K., et. al.: Diagnosis of focal liver masses on ultrasonography. J Ultrasound Med 2007; 26: pp. 775-787.
27. Hill C.R., Bamber J.C., Cosgrove D.O.: Performance criteria for quantitative ultrasonology and image parameterisation. Clin Phys Physiol Meas 1990; 11: pp. 57-72.
28. Dabrowski W., Dummore-Buyze J., Cardinal H.N., et. al.: A real vessel phantom for flow imaging: 3-D Doppler ultrasound of steady flow. Ultrasound Med Biol 2001; 27: pp. 135-141.
29. Harrer J.U., Hornen S., Oertel M.F., et. al.: Comparison of perfusion harmonic imaging and perfusion MR imaging for the assessment of microvascular characteristics in brain tumors. Ultraschall Med 2008; 29: pp. 45-52.
30. Lassau N., Chami L., Benatsou B., et. al.: Dynamic contrast-enhanced ultrasonography (DCE-US) with quantification of tumor perfusion: a new diagnostic tool to evaluate the early effects of antiangiogenic treatment. Eur Radiol 2007; 17: pp. F89-F98.
31. Eyding J., Krogias C., Schöllhammer M., et. al.: Contrast-enhanced ultrasonic parametric perfusion imaging detects dysfunctional tissue at risk in acute MCA stroke. J Cereb Blood Flow Metab 2006; 26: pp. 576-582.
32. Eisenbrey J.R., Joshi N., Dave J.K., et. al.: Assessing algorithms for defining vascular architecture in subharmonic images of breast lesions. Phys Med Biol 2011; 56: pp. 919-930.
33. Ohishi H., Hirai T., Yamada R., et. al.: Three-dimensional power Doppler sonography of tumor vascularity. J Ultrasound Med 1998; 17: pp. 619-622.
34. Kurjak A., Kupesic S., Aparac V., et. al.: Three-dimensional ultrasonographical and power Doppler characterization of ovarian lesions. Ultrasound Obstet Gynecol 2000; 16: pp. 365-371.
35. Hansen C., Hütterbräuker N., Wilkening W., et. al.: Three-dimensional reconstruction of fine vascularity in ultrasound breast imaging using contrast-enhanced spatial compounding. Acad Radiol 2008; 15: pp. 1155-1164.
36. Forsberg F., Rawool N.M., Merton D.A., et. al.: Contrast enhanced vascular three-dimensional ultrasound imaging. Ultrasonics 2002; 40: pp. 117-122.
37. Hoyt K., Sorace A., Saini R.: Quantitative mapping of tumor vascularity using volumetric contrast-enhanced ultrasound. Invest Radiol 2012; 47: pp. 167-174.
38. Halldorsdottir V.G., Dave J.K., Leodore L.M., et. al.: Subharmonic contrast microbubble signals for noninvasive pressure estimation under static and dynamic flow conditions. Ultrason Imaging 2011; 33: pp. 153-164.
39. Katiyar A., Sarkar K., Forsberg F.: Modeling subharmonic response from contrast microbubbles as a function of ambient static pressure. J Acoust Soc Am 2011; 129: pp. 2325-2335.
40. Faez T., Renaud G., Defontaine M., et. al.: Dynamic manipulation of the subharmonic scattering of phospholipid-coated microbubbles. Phys Med Biol 2011; 56: pp. 6459-6473.
41. Dave J.K., Halldorsdottir V.G., Eisenbrey J.R., et. al.: Noninvasive LV pressure estimation using subharmonic emissions from microbubbles. JACC Cardiovasc Imaging 2011; 5: pp. 87-92.
42. Needles A., Couture O., Foster F.S.: A method of differentiating targeted microbubbles in real time using subharmonic micro-ultrasound and interframe filtering. Ultrasound Med Biol 2009; 35: pp. 1564-1573.
43. Sprague M.R., Cherin E., Goertz D.E., et. al.: Nonlinear emission from individual bound microbubbles at high frequencies. Ultrasound Med Biol 2010; 36: pp. 313-324.
44. Eisenbrey J.R., Dave J.K., Halldorsdottir V.G., et. al.: Simultaneous grayscale and subharmonic imaging on a modified commercial scanner. Ultrasonics 2011; 51: pp. 890-897.