Open access peer-reviewed chapter

Breast Ultrasound Tomography

By Nebojsa Duric and Peter Littrup

Submitted: November 21st 2016Reviewed: May 17th 2017Published: December 20th 2017

DOI: 10.5772/intechopen.69794

Downloaded: 1217


Both mammography and standard ultrasound (US) rely upon subjective criteria within the breast imaging reporting and data system (BI-RADS) to provide more uniform interpretation outcomes, as well as differentiation and risk stratification of associated abnormalities. In addition, the technical performance and professional interpretation of both tests suffer from machine and operator dependence. We have been developing a new technique for breast imaging that is based on ultrasound tomography which quantifies tissue characteristics while also producing 3-D images of breast anatomy. Results are presented from clinical studies that utilize this method. In the first phase of the study, ultrasound tomography (UST) images were compared to multi-modal imaging to determine the appearance of lesions and breast parenchyma. In the second phase, correlative comparisons with MR breast imaging were used to establish basic operational capabilities of the UST system. The third phase of the study focused on lesion characterization. Region of interest (ROI) analysis was used to characterize masses. Our study demonstrated a high degree of correlation of breast tissue structures relative to fat subtracted contrast-enhanced MRI and the ability to scan ~90% of the volume of the breast at a resolution of 0.7 mm in the coronal plane.


  • breast
  • ultrasound
  • 3-D imaging
  • tomography
  • cancer

1. Introduction

Breast cancer is the most common cancer among women, accounting for one-third of cancers diagnosed. Statistically, ~230,000 new cases of invasive breast cancer and ~63,000 in situ breast carcinomas are diagnosed in the US annually; breast cancer is the third leading cause of cancer death among women, causing ~40,000 deaths in the US every year [1]. According to SEER statistics, approximately 61% of women are found to have localized breast cancers at the time of diagnosis; about 31% are found to be regional disease; another 5% are diagnosed with distant metastases while about 3% are unstaged [2]. The 5-year survival rate for women with localized cancer is 98%; for those with regional disease, it drops to 84%; for those diagnosed with distant stage, the survival rate drops dramatically to 23%; while for unstaged cancers the 5-year survival rate is about 58%. Figure 1 illustrates the dependence of survival on cancer stage.

Figure 1.

The dependence of mortality rates on cancer type and stage. From Kerlikowske et al. [3].

There are many reasons why cancers are not detected early but some of the major factors relate to limited participation in breast screening and the performance of screening mammography.

1.1. Limited participation in screening

National cancer screening statistics indicate that only 51% of eligible women undergo annual mammograms [4]. That rate is even lower for African American women and/or those of lower socioeconomic groups. Access, fear of radiation and discomfort are some of the factors that contribute to the low participation rate. Greater participation would lead to detection of breast cancer at an earlier stage leading to longer survival. Increased participation and improved breast cancer detection would have the greatest effect on the statistic of nearly 1 in 3 women who are diagnosed each year with later stage (regional or greater) breast cancer, totaling approximately 60,000 women per year in the USA. The net effect would be an increase in survival time and a corresponding decrease in mortality rates. This is also suggested in a recent meta-analysis, whereby increased participation and sensitivity lead to additional invasive cancer detection and greater mortality reduction [4].

1.2. Limited performance of mammography

For women with dense breast tissue, who are at the highest risk for developing breast cancer [58], the performance of mammography is at its worst [9]. Consequently, many cancers are missed at their earliest stages when they are the most treatable. Improved cancer detection for women with denser breasts would decrease the proportion of breast cancers diagnosed at later stages, which would significantly lower the mortality rate.

1.3. The breast screening challenge

X-ray mammography detects about 5 cancers per 1000 screens [10]. However, its positive predictive value (PPV) is low and its sensitivity is greatly reduced in women with dense breast tissue [10]. Although digital breast tomosynthesis (DBT) may improve upon some of the limitations of standard mammography, it is unlikely to create a paradigm shift in performance [11] while generating even higher levels of ionizing radiation [12]. MRI can significantly improve on these limitations by virtue of its volumetric, radiation-free imaging capability. Studies have shown that MRI can have a positive impact in the breast management continuum ranging from risk assessment to diagnosis and treatment monitoring [12, 13]. However, MRI can have a high false positive rate, requires contrast injection and the exams can be both long and costly [14]. Furthermore, MR has long been prohibitively expensive for routine use and there is a need for a low-cost equivalent alternative. Yet, for high-risk women, MRI is now viewed as the gold standard for breast cancer detection and screening [1523]. Positron emission tomography is also limited by cost and radiation concerns.

Recent studies have demonstrated the effectiveness of hand held ultrasound imaging in detecting breast cancer, particularly for women with dense breasts (Table 1). These studies have shown that up to 4.5 extra cancers were detected per 1000 screens [2434]. A striking aspect of the added detections is that they are predominantly node negative invasive cancers which would have potentially progressed to a later stage before possible mammographic detection. Moreover, there is little risk of over detection of ductal carcinoma in situ (DCIS). The sensitivity of mammography is greater for DCIS than it is for invasive cancer, with DCIS making up approximately 25% of mammographic screen-detected breast cancers [35].

Author (Year)CenterTypeExamsUS only cancersYield per 1000
Brem et al. (2014)MultiABUS15,318301.96
Berg et al. (2012)MultiHHUS7473324.28
Hooley et al. (2012)SingleHHUS93533.21
Kelly et al. (2010)MultiAWBU6425233.58
Corsetti et al. (2008)MultiHHUS9157374.04
Crystal et al. (2003)SingleHHUS151774.61
Leconte et al. (2003)SingleHHUS4236163.78
Kolb et al. (2002)SingleHHUS13,547372.73
Kaplan (2001)SingleHHUS186263.22
Buchberger et al. (2000)SingleHHUS8103323.95
Gordon et al. (1995)SingleHHUS12,706443.46

Table 1.

Summary of studies used in the analysis.

We have examined the data from these studies to extract the statistics of cancer detection by imaging mode (Table 1). The results are summarized in Figure 2. It is striking to note that ultrasound (US) almost doubles the cancer detection rate in dense breasts. However, despite these successful study outcomes, handheld ultrasound is unlikely to be adopted for screening because it is operator dependent, and its imaging aperture is small, which hinders whole breast imaging. Furthermore, ultrasound’s increased sensitivity to invasive cancer is offset by lowered sensitivity to DCIS by virtue of mammography’s greater ability to detect microcalcifications. Although such a trade-off may be justified by the fact that mortality from invasive cancers is much higher than that from DCIS, a combined screening [mammography plus automated breast ultrasound (ABUS)] would provide a comprehensive screen. It has therefore been proposed that ABUS be used for screening, supplemental to mammography.

Figure 2.

Venn diagram summarizing comparative cancer detection rates for screening mammography and ultrasound.

To that end, automated breast ultrasound (ABUS) has been introduced as a way of overcoming these issues, mainly by reducing operator dependence and increasing the field of view. For example, the GE Invenia ABUS ultrasound system for breast cancer screening, originally developed by U-Systems., recently received screening approval, adjunctive to mammography, from the FDA, because it demonstrated an ability to detect cancers missed by mammography in dense breasts. The SomoInsight screening study [24], indeed showed that ABUS plus mammography outperformed mammography alone, leading to the first FDA approval for ultrasound screening for breast cancer.

The fundamental quandary of breast screening today is the knowledge that (i) mammography misses cancers in dense breasts, (ii) that Automated Breast ultrasound (ABUS) detects cancers that mammography misses and yet (iii) screening continues largely with mammography only. This paradox is amplified even further by the proliferation of state breast density notification laws in the USA which mandate that this information be available to women undergoing breast cancer screening. The primary reason this paradox exists today is that ABUS screening increases call back rates (up to a factor of two in case of the SomoInsight study [23]). The improvement in classification performance, measured by the area under the ROC curve, is modest because the increase in sensitivity is partially offset by an increase in false positives thus slowing its adoption. Technically, with its basic B-mode capability, ABUS has the same issue with false positives as hand held ultrasound. It is therefore unlikely that ABUS will be widely adopted for screening in the foreseeable future without more tissue-specific imaging capability. Improved lesion characterization would help lower the barriers to adoption of screening ultrasound.

1.4. Potential role of UST

Ultrasound tomography (UST) is an emerging technique that has the potential for tissue-specific imaging and characterization, by virtue of its transmission imaging capability [3661]. Improved specificity would lower call back rates and lower the barriers to adoption. An adjunctive use of UST would have the potential to improve specificity relative to current ABUS and provide a comprehensive screen that would uncover invasive cancers otherwise missed by mammography. Detection of such early stage invasive cancers would provide women with curative treatment, the opportunity for which might be otherwise lost.

Conventional reflection ultrasound exploits differences in acoustic impedance between tissue types to provide anatomical images of breast tumors [62, 63]. However, reflection is just one aspect of a multi-faceted set of acoustic signatures associated with the biomechanical properties of tissue. UST is a technique that moves beyond B-mode imaging by virtue of its transmission capabilities. The latter provides additional characterization by measuring tissue parameters such as sound speed and attenuation (ATT) [6468]. These parameters can be used to characterize lesions in a quantitative manner, a capability not available in current whole breast ultrasound systems. By merging reflection images with images of the bio-acoustic parameters of sound speed and attenuation, UST offers the possibility of exploiting differences in anatomical and physical properties of tissue to accurately differentiate cancer from normal tissue or benign disease. UST parameters are also quantitative, which allows new consideration of second and third-order statistical image analyses, or radiomics. Ultrasound has previously not been suitable for the burgeoning applications of radiomics due to its lack of true quantitative parameters such as sound speed (m/s) and attenuation (dB/cm/MHz). Initial assessments of UST performance was carried out, as described below.

In an initial attempt to assess the potential of UST in breast imaging, studies were carried out at the Karmanos Cancer Institute, Detroit, MI, USA. Informed consent was obtained from all patients, prospectively recruited in an IRB-approved protocol following HIPAA guidelines. Patients were scanned at the Alexander J Walt Comprehensive Breast Center. Standard multi-modality imaging was available for all patients. The Walt Breast Center houses SoftVue, a UST system manufactured by Delphinus Medical Technologies, Inc (Novi, MI). SoftVue embodies a number of attributes that differentiate it from conventional imaging modalities:

  • Water-based pulse coupling: SoftVue utilizes a water filled imaging chamber that is kept at body temperature. Its primary purpose is to couple the sound energy between the transducer and the breast tissue.

  • Closed geometry probe: A circular ring transducer surrounds the breast while both are immersed in water. There is no compression of the breast since the transducer is offset from the breast with water acting as the pulse coupling agent. The closed transducer geometry allows collection of signals that pass through the entire width of the breast, a requirement for transmission imaging and the reconstruction of sound speed and attenuation images. These parameters provide quantitative information in absolute units that are tied to externals standards (km/s and dB/cm, respectively).

  • Operator independence: Unlike mammography and other ABUS systems, multiple positionings are not required for larger breasts. Once the patient is positioned on the table, the operator simply presses the button and the exam is performed automatically without further intervention from the operator.

  • Scan time: SoftVue scan time is 1–2 min per breast (depending on breast size). This scan duration minimizes intra-slice and inter-slice motion artifacts.

  • Image reconstruction time. In this study, reconstruction time for a bilateral breast exam was ~30 min for the average patient and current hardware/software processing ability.

SoftVue was used to scan the recruited patients for this study. Coronal image series were produced by tomographic algorithms for reflection, sound speed and attenuation. All images were reviewed by a board-certified radiologist who has more than 20 years of experience in breast imaging and US-technology development. Symptomatic study participants were scanned with a SoftVue UST system. Pathological correlation was based on biopsy results and standard imaging (e.g. US definitive cyst).

Tomographic algorithms were used to generate images stacks of reflectivity, sound speed and attenuation for each patient. Lesions were identified based on correlation with standard imaging so that the tumor sound speed (SS) and attenuation (ATT) could be assessed. An example each type of image is shown in Figure 3.

Figure 3.

From left to right, reflection, sound speed and attenuation image slices depicting breast parenchyma and a fibroadenoma at 7 o’clock.

In the first phase of the study, correlative comparisons with multi-modal imaging were carried out to assess lesion properties relative to mammography, US and MR. In the second phase, MR breast imaging was used to establish basic operational capabilities of the UST system including the identification and characterization of parenchymal patterns, determination of the spatial resolution of UST and an estimate the breast volume that can imaged with UST. The third phase of the study focused on lesion characterization. Region of interest (ROI) analyses were performed on all identified lesions using all three UST image types. Combinations of the ROI generated quantitative values were used to characterize all masses, particularly in relation to relative differences with surrounding peritumoral regions.


2. Multi-modal comparisons

Since the patients were recruited at KCI on the basis of having a suspicious finding, standard imaging such as mammography, US and sometimes MRI were available, as well as the radiology and pathology reports. These images and the associated reports were used to retroactively locate the lesions in the UST image stacks for visual comparison. Figures 47 show examples of UST images in relation to the other modalities. When MRI was available, the images were projected into the coronal plane for easier comparison with the UST whose native format is coronal.

Figure 4.

A 9 mm IDC at 3 o’clock. CC and MLO mammographic views of the affected breast are shown on the left with the lesion identified by arrows. The coronal UST views are shown in the form of reflection, sound speed and attenuation images. The corresponding ultrasound and MR images are also shown.

Figure 5.

Multimodality images compared to UST reflection, sound speed and attenuation. An IDC is shown at 12 o’clock.

Figure 6.

Multimodality images vs UST reflection, sound speed and attenuation showing an IDC and intramammary lymph node.

Figure 7.

Illustrating the chest wall access achievable by UST relative to mammography.

Figure 4 shows a 9mm IDC at 3 o’clock. CC and MLO mammographic views of the affected breast are shown on the left with the lesion identified by arrows. The UST views corresponding to the coronal planes that contain the lesions are across the top with reflection, sound speed and attenuation images laid out from left to right. The corresponding ultrasound and MR images are shown along the bottom. Inspection of the images shows good correspondence in shape and location of the lesion. The greatest similarity is between the UST images and MRI. The IDC is seen to be hypoechoic in reflection and has high sound speed and attenuation contrast. An IDC in a heterogeneously dense breast is shown in Figure 5 This IDC was initially missed by mammography. A large IDC and an intramammary lymph node are shown in Figure 6. Note the concordance between the UST images and mammography. Figure 7 illustrates the chest wall access achievable by UST relative to mammography. Although UST does not access the entire axilla it does visualize the cancer that has invaded the chest wall.

3. MR concordance

UST and MR imaging was performed within weeks of each other. UST imaging was carried out with the SoftVue system (Delphinus Medical Technologies) and the MR exams with a Philips Achieva 3T system. The resulting image sequences were qualitatively and quantitatively to assess imaging performance of UST. As discussed above, UST images correlate best with MR images. Further inspection shows that of the three UST image types, the sound speed image correlates best with MR. Figure 8 shows a coronal view comparison between UST speed of sound and MR contrast-enhanced fat subtracted images of representative breast parenchyma.

Figure 8.

Top: Coronal UST sound speed images for six different patients. Bottom: Corresponding fat subtracted contrast-enhanced MR images.

The parenchymal patterns are very similar with the only major difference relating to the shape of the breast. This difference can be explained by the fact that the SoftVue system utilizes water so that buoyancy foreshortens the breast while with MR, gravity lengthens the breast in the AP dimension (i.e. prone).

As discussed above, UST images correlate best with MR images. Further inspection shows that of the three UST image types, the sound speed image correlates best with MR, as illustrated in Figure 8. The parenchymal patterns are very similar with the only major difference relating to the shape of the breast. This difference can be explained by the fact that the SoftVue system utilizes water so that the buoyancy force helps shape the breast while with MR, gravity shapes the breast.

4. Breast volume comparisons

MRI was used as the gold standard for defining the extent of the breast tissue. MRI and UST breast volumes were compared using a paired t-test. In the first step, a k-means segmentation algorithm was applied to T1 breast MR images to automatically separate out the non-tissue background. In the second step, the boundary between the breast tissue and the chest wall was drawn manually and the chest wall removed, leaving behind only breast tissue (Figure 9).

Figure 9.

The segmentation process for MR images (top) and UST images (bottom). From left to right, original image, segmentation boundary and the final segmented image.

In the UST images a semi-automated tool was used to draw a boundary around the breast tissue in each coronal slice and everything outside the boundary removed (water signal). Any slices containing chest wall signal were also removed. The resulting stack of slices then represented the pure breast volume scanned by UST.

The two sets of volumes were plotted against each other as shown in Figure 10. The average breast volumes for MRI and UST were compared and the result shown in Table 2. As expected, the UST scanned volume was less than that of MRI and was found to be about 89% of the MRI volume on average. However, a student’s paired t-test indicates that this difference is not significant. Since UST cannot fully access the axilla, it is likely that the UST scanned volume is somewhat lower than that of MRI, even though UST generally reaches the pectoralis muscle at the chest wall.

Figure 10.

Correlation between UST and MR measured breast volumes.

Mean MRI volume (cm3)Mean UST volume (cm3)pValue

Table 2.

Volume comparison.

5. Spatial resolution assessment

The spatial resolution of each modality was estimated using profile cuts of thin features using, the full-width, half-maximum criterion as shown in Figure 11. The results of the spatial resolution analysis are shown in Table 3. The spatial resolution was found to be dependent on the reprojection type for both MRI and with UST outperforming MRI in the coronal plane and MRI outperforming UST in the other projections. (However, MR acquisitions with isotropic voxels would show comparable resolution to UST in the coronal plane). The UST image voxels are not isotropic and data acquisition cannot be readily adjusted like MR, such that UST reconstructed in axial and sagittal planes have resolution that approach the 2.5 mm slice thickness at this time.

Figure 11.

The spatial resolution of each modality was estimated using profile cuts of thin features using, the full-width, half-maximum criterion, as illustrated.

Coronal0.7 ± 0.1 mm1.6 ± 0.3 mm
Axial/sagittal2.5 ± 0.5 mm0.8 ± 0.1 mm

Table 3.

Spatial resolution comparison.


6. Lesion characterization

Ultrasound breast imaging reporting and data system (US-BI-RADS) criteria are predominantly devoted to assessment of tumor shape, margins and interaction with adjacent tissue. However, criteria such as shadowing or enhanced through transmission are not applicable to UST’s circular geometry. In addition, UST, operating at 3 MHz, appears more sensitive to the specular reflectors of benign mass capsules, or the spiculations and/or architectural distortions of many cancers. Therefore, we developed a 5-point scale that combined US-BI-RADS criteria for tumor margins, as well as possibilities for peritumoral tissue interaction (Figure 12).

Figure 12.

Schematic of shape and margin analysis and associated grading scheme.

Masses were characterized by a (i) Margin Boundary score, (ii) reflectivity, (iii) quantitative SS evaluation and (iv) ATT evaluations. A semi-automatic region-of-interest (ROI) tool was used to determine the quantitative properties of each mass. After identifying the mass of interest, a simple elliptical ROI is drawn around the mass. The ROI algorithm then generates 20 radial ellipsoids – 10 inside and 10 outside the mass. Quantitative information was then measured for each of the 20 annuli for subsequent analysis. The region of interest (ROI) analysis was performed on all identified lesions using all three UST image types. Combinations of the ROI generated values were used to characterize all masses in the study.

Ongoing analyses of the ROI tool have not yet led to full evaluation of second and third-order statistics of textural analyses, as well as their impacts upon decision analysis and predictive values. However, our recent RSNA presentation highlighted the significant impacts of first-order statistics such as standard deviation, within the tumoral ROI and comparisons with the surrounding peritumoral region [69]. Scatterplots and box plots of the optimal methods were used to illustrate the characterization potential. The box plot in Figure 13 shows the differentiation achieved when using the boundary score (Figure 6) combined with the first-order statistic of standard deviation, a more crude measure of heterogeneity, based upon tumoral ROI extracted from ATT images, which had only slightly higher significance than SS [69]. These ROIs were again obtained by simply drawing an elliptical ROI around the mass and determining the standard deviation with in the ROI. The box plot was based on taking the average values for 107 benign lesions and 31 cancers [69].

Figure 13.

Separation of cancer from benign when using boundary score and hetrogeneity score.

Upon further investigation, it was found that the SS of the peritumoral mass region (defined by an annular area just outside the mass boundary ROI) further separated the benign masses from cancer. A scatter plot based on all of these parameters is shown in Figure 14. The scatter plot shows separately the cancers, fibroadenomas and cancers. The cancers are tightly grouped in the top left corner of the plot indicating high boundary scores, high heterogeneity and lower peritumoral sound speed. By these measures, there was not much separation between cysts and fibroadenomas but significant separation between them and cancer. ROC analysis of the data represented in the scatter plot indicates a PPV of 91% when the sensitivity is 97%. However, this is a subset of data relative to an expanded ongoing study that includes more quantitative margin analyses. The ultimate goal is to generate textural analyses that will be less operator dependent and serve as appropriate diagnostic aids for a detected mass by simply requiring the radiologist to draw an ellipsoidal ROI. This method can also serve as a teaching tool for identifying grossly apparent textural differences within the tumor and surrounding peritumoral region. Figure 15 shows the basic differences in sound speed texture noted for many cysts, fibroadenomas and cancer.

Figure 14.

Scatter plot showing the distribution of cancers (squares), Fibroadenomas (diamonds), cysts (triangles) and other benign (circles).

Figure 15.

Cyst, fibroadenoma, cancer: Waveform SS images showing well circumscribed margins and smooth internal textures for both the 1.5 cm cyst in dense white breast tissue (left) and the 0.7 cm fibroadenoma (middle) in darker fat. The 1.8 cm cancer (right) has irregular margins, heterogeneous content and subtle peritumoral spiculations.

7. Conclusions

In this study we reviewed the status of breast cancer screening and the potential role that ultrasound tomography (UST) could play in breast imaging. Several results from recent ongoing UST studies were used in this review. The main conclusions from those studies are:

  1. UST sound speed demonstrated a high degree of correlation of breast tissue structures relative to fat subtracted contrast-enhanced MRI. This correlation of structures was most evident in the coronal plane comparisons.

  2. UST can scan ~90% of the volume of the breast compared to MRI. With proper positioning UST can image the pectoralis muscle and a portion of the axillary tissue.

  3. UST demonstrated a spatial resolution of 0.7mm in the coronal plane, similar to MRI.

  4. Initial clinical results suggest an ability to characterize lesions using margin boundary scores in combination with sound speed and attenuation parameters. These parameters leverage all three imaging modes of UST (reflection, sound speed and attenuation).

UST is a promising new modality that has the potential to complement existing breast imaging methods to aid in lesion detection and characterization. Future larger scale studies will assess UST’s role in diagnostic and screening settings.


The authors thank Dr. Mark Sak for providing images relating to the MRI comparison study and Mr. Mark Krycia for his help in the statistical analysis of the lesion characterization data. The work presented in this paper was supported by NIH grant 5R44CA165320-05.

© 2017 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Nebojsa Duric and Peter Littrup (December 20th 2017). Breast Ultrasound Tomography, Breast Imaging, Cherie M. Kuzmiak, IntechOpen, DOI: 10.5772/intechopen.69794. Available from:

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