In This Issue
Engineering for Women's Health
April 25, 2022 Volume 52 Issue 1
The articles in this issue describe the latest technologies for detection of breast and other cancers, approaches to reduce the incidence of premature births, and remote monitoring for pregnancy, a development of particular interest as the pandemic discouraged many people from going to a doctor’s office or hospital.

Breast Cancer Screening: Opportunities and Challenges with Fully 3D Tomographic X-Ray Imaging

Monday, March 28, 2022

Author: Srinivasan Vedantham and Andrew Karellas

Dedicated breast computed tomography presents an opportunity to realize fully 3D tomographic x-ray imaging.

Mammography screening aims at early detection of breast cancer in asymptomatic women. If a patient is symptomatic, or recalled following an indeterminate or a positive screening exam for additional imaging, then this imaging exam is referred to as diagnostic workup. Mammography and ultrasound are commonly used for diagnostic workup.

A group of experts convened by the International ­Agency for Research on Cancer concluded in 2015 that there is a net benefit for mammography screening (Lauby-Secretan et al. 2015), and randomized clinical trials have shown that mammography reduces breast cancer mortality. But challenges persist.


Mammography screening has been the subject of many studies over the past 50 years, both to assess its efficacy and to understand its limitations.

One important concern is a false-positive exam. Among women aged 40–50 years who undergo annual screening, it is estimated that at least 50 percent will receive a false-positive screen and approximately 7 percent will receive a false-positive biopsy recommendation (­Hubbard et al. 2011). Another concern is that the performance of mammography is dependent on breast density.

The radiation dose to the breast is reported using mean glandular dose (MGD), which apportions the dose to the “at-risk” fibroglandular breast tissue. In the ­United States, the Mammography Quality Standards Act (MQSA) of 1992 limits the MGD to 3 mGy (milligray) for a standard breast[1] imaged in the craniocaudal view (from the head toward the feet). For larger compressed breast thickness, the MGD can exceed 3 mGy.

Technical advances are being explored and developed to enhance the effectiveness of breast imaging methods and reduce the incidence of false-positive results.

Digital Mammography (DM)

Full-field digital mammography (DM) was approved by the United States Food and Drug Administration (FDA) in 2000. This marked both the start of higher-quality digital image capture without using film and wet chemical processing, and the transition to a ­fully digital environment in breast imaging. It enabled ease of transmitting, storing, and retrieving mammograms. It also allowed for easy image manipulation, image processing, and computer-aided detection and diagnosis.

Vedantham and Karellas Figure 1

FIGURE 1 Illustration of indirect and direct conversion detectors. In indirect conversion detectors (A), x-rays are converted to optical photons, which are then detected by a photodetector. In direct conversion detectors (B), x-rays are converted to electron-hole pairs by a photoconductor.

A key component of the DM system is the x-ray ­detector. There are two major types of DM detectors: indirect conversion and direct conversion (figure 1). In indirect conversion, the incident x-ray ­photons are converted to optical ­photons by a scintillator (typically ­thallium-doped cesium iodide as it is grown in columnar structure to reduce light spread) and these photons are detected by a photodetector, which may be an amorphous silicon flat-panel detector coupled to an active matrix, thin-film transistor array or a complementary metal oxide semiconductor (CMOS) detector. In direct conversion, the incident x-ray photons are converted to electrons by a photoconductor (typically amorphous selenium) and the electrons are read out by an active matrix, thin-film transistor array.

The results from the Digital Mammography Imaging Screening Trial (2001–05) showed that the overall diagnostic accuracy of screen-film mammography and DM were similar but that DM was more accurate in younger women, women with heterogeneously dense or extremely dense breasts, and pre- or perimenopausal women (Pisano et al. 2005).

But DM suffers from a major limitation: it represents the x-ray projection of the three-dimensional (3D) breast on a two-dimensional (2D) detector, yielding a 2D image. This causes tissue superposition in the ­images, which may mask lesions, resulting in missed cancers (false-negative exams), or mimic the presence of abnormalities, resulting in false-positive exams. Tissue superposition also contributes to increase in the background structure or “clutter” that makes it more challenging to detect lesions. In addition, it is well established that the sensitivity (ability to detect cancer) of ­mammography screening decreases with increasing breast density. The need to develop an imaging technique that reduces ­tissue superposition was soon recognized.

Digital Breast Tomosynthesis (DBT)

In 1997 a seminal article described digital breast tomosynthesis (DBT) using a DM system (Niklason et al. 1997). In this limited-angle tomographic approach, a series of low-dose projections of the compressed breast are acquired with the x-ray tube moving along an arc and with a stationary detector.


The initial DBT method used step-and-shoot to acquire the series of projections: the x-ray tube moved to a predetermined position, acquired the projection, and then moved to each subsequent position to acquire the projection data. Early approaches then used a shift-and-add method for image reconstruction to provide pseudo-3D representation of the breast through the ­generation of a series of focal plane images (slices), typically with a spacing of 1 mm, from the bottom to the top of the compressed breast. While the shift-and-add algorithm is simple to implement, it suffers from substantial artifacts, so other image reconstruction algorithms for improving the image quality were investigated (e.g., ­Suryanarayanan et al. 2000, 2001).

Since the FDA approved DBT for breast cancer screening in 2011, there are now at least five commercial DBT systems. Most are modifications of DM systems to incorporate x-ray tube movement, reduce vibration during movement, and collimate the x-ray beam so that it is limited to the detector. The technological implementation differs among these DBT systems in terms of the angular scan range of x-ray source movement, step-and-shoot or continuous acquisition (i.e., the x-ray tube is in continuous motion while the x-ray source is briefly pulsed for each projection), detector technology, whether the detector is stationary or angulated to partially track the motion of the x-ray tube, image acquisition geometry, image acquisition technique factors, target-filter combination, and image reconstruction methods (Sechopoulos 2013; Vedantham et al. 2015).

Vedantham and Karellas  Fig 2.gif
FIGURE 2 Illustration of digital breast tomosynthesis acquisition. (A) The angular scan-range of the x-ray tube is small and the imaging detector tilts to partially track the x-ray source. (B) The angular scan-range is large and the imaging detector is stationary.

Figure 2 shows an illustration of two DBT systems. In figure 2A, the angular range of x-ray tube movement is small and the detector tilts to approximately track the motion of the x-ray tube. In figure 2B, the system has a wide angular range for x-ray tube movement and the detector is stationary.

In some systems, adjacent detector pixels are grouped (binned) during acquisition, enabling faster readout and shorter scan times. DBT’s quasi-3D imaging approach partially addresses the tissue superposition problem of single projection DM, but there is still blur in the depth direction (top to bottom of the compressed breast). Increasing the angular scan range of x-ray source movement usually reduces this blur.

In the United States, DBT is interpreted either in combination with DM, which entails an additional acquisition, or with a synthesized mammogram, which is a computer-generated mammogram using the DBT data. The DBT combined with an acquired DM is often referred to as “combo” mode.

The MGD from DBT is approximately 0.5 mGy higher than DM. When the combo mode is used, for a standard breast imaged in the craniocaudal view the MGD (2.5 mGy) roughly doubles compared to DM (1.2 mGy), although it remains less than the 3 mGy limit. For larger compressed breast thickness, the MGD can exceed 3 mGy.

Clinical Trials and Studies

Prospective clinical trials and observational studies using DBT have shown a reduction in false-positive exams. In the Oslo Tomosynthesis Screening Trial (2010–12), the false-positive recall rate declined from 10.3 percent with DM to 8.5 percent with the combo mode (Skaane et al. 2013). And a large multicenter observational study (2010–12) showed that the recall rate was reduced from 10.7 percent with DM to 9.1 percent with the combo mode (Friedewald et al. 2014).

In terms of cancer detection, both the Oslo trial and the observational study reported improvement using the combo mode compared to DM: 9.4 vs. 7.1 and 5.4 vs. 4.2, respectively, per 1000 screens. Importantly, most of the additional cancers detected by DBT were invasive cancers, which are precisely the cancers that need to be detected early to improve outcomes. The Malmö Breast Tomosynthesis Screening Trial (2010–17) also reported a reduction in interval cancers with DBT (Johnson et al. 2021).


DBT has shown several benefits and is being rapidly adopted in clinical practice. As of January 1, 2022, 81 percent of MQSA-certified facilities have at least one DBT system and 44 percent of accredited units are DBT systems.[2]

DBT may not confercancer detection benefit for extremely dense breasts, which are reported in approximately 10 percent of women.

However, there is a need to further understand the benefits and limitations of DBT. While it has shown improved cancer detection rate, the mortality reduction benefit of DBT over DM is yet to be demonstrated, although a simulation study (Lowry et al. 2020) using DBT instead of DM screening indicated a modest reduction in breast cancer deaths (0 to 0.21 per 1000 women). The ongoing Tomosynthesis Mammographic Imaging Screening Trial[3] aims to determine ­whether DBT leads to earlier detection of hard-to-treat or aggressive cancers. Results from this study could enable better estimation of the mortality reduction with DBT screening.

The performance of DBT is dependent on breast density, which relates to the amount of fibroglandular tissue. Radiologists during interpretation assign breast density in four categories: almost entirely fat, scattered fibroglandular densities, heterogeneously dense, and extremely dense.[4]

It is well established (Boyd et al. 2007) that breast density is an independent risk factor for breast cancer: likelihood increases from the lowest to the highest density category. But while DBT showed an increase in ­cancer detection rate for breasts in the first three categories (4.2 to 6.1 per 1000 screens), for extremely dense breasts the cancer detection rate was ­essentially the same with DM and DBT (3.8 vs. 3.9 per 1000, P = 0.88). This suggests that DBT may not confer ­cancer detection benefit for extremely dense breasts, which are reported in approximately 10 percent of women.

Additional studies are needed to better understand the limitations of DBT in extremely dense breasts. ­Contrast-enhanced DM (Lewin et al. 2003) and contrast-enhanced DBT (Chen et al. 2007), wherein iodinated contrast media is intravenously administered, could be of benefit for imaging lesions in extremely dense breasts (due to neoangiogenesis-associated contrast uptake), but this is challenging to implement in routine screening.

Dedicated Breast Computed Tomography (BCT)

Dedicated breast computed tomography, commonly referred to as breast CT (BCT), is an emerging ­modality. It is conceptually similar to whole-body CT, but the x-ray beam from the source is limited to the breast, preventing direct irradiation of other organs.

In current implementations, the patient lies prone on a table and the breast is pendant through an ­aperture in the table; the center of the aperture aligns with the axis of rotation. The gantry with the x-ray source and detectors revolves around the axis of rotation and acquires several hundred low-dose x-ray projections. The ­projection dataset is reconstructed to provide fully 3D tomographic images. The cross-sectional images from BCT correspond to the coronal plane.

Early Breast CT

The idea of dedicated BCT was envisioned in the 1970s and called CT/mammography. The first-­generation systems were single-slice scanners and used 127 detector elements containing xenon gas. The pixel size of the reconstructed images was 1.56 mm × 1.56 mm and the slice thickness was 10 mm. The images had only 256 gray levels (dynamic range of −127 to 128 ­Hounsfield Units).

Between 1976 and 1979, 1625 patients underwent BCT with intravenous administration of iodinated contrast media at the University of Kansas Medical Center (Chang et al. 1980). The contrast-enhanced BCT exam detected 94 percent of the 78 cancers in the cohort compared to 77 percent by mammography. But clinical adoption was stymied by the limitations of the early-stage technology, including the need for contrast administration due to the limited dynamic range, inability to visualize microcalcifications because of the limited spatial resolution, and concerns about the radiation dose (about 6 times that of mammography).

In 1996 a noncontrast study with surgical breast specimens using a conventional whole-body CT scanner showed that, compared to specimen radiography, CT performed equally or better for imaging soft tissue lesions, but visualization of microcalcifications was worse (Raptopoulos et al. 1996). In 2001 a landmark article provided estimates of MGD and used a conventional whole-body CT scanner to show that it was possible to get good-quality images at an MGD comparable to that of mammography (Boone et al. 2001).

Motivations for Breast CT

A recent article (Duffy et al. 2021) highlighted the importance of continuous and periodic screening: ­women who participated in two rounds of screening before a breast cancer diagnosis had the largest reduction in breast cancer death, and missing either of these two screens increased risk (Duffy et al. 2021). But in 2019, among women 50–74 years of age, 76.4 percent had a mammogram within the last 2 years.[5]

An earlier systematic review and meta-analysis showed compression-induced pain or discomfort as a major reason for women to discontinue screening (Whelehan et al. 2013). If the elimination of breast compression increases participation rate, this could improve earlier detection. A compression-free technique that increases participation rate is of societal importance.

There is no computed tomography technique for breast cancer screening. Dedicated BCT could address this need.

Unlike DM and DBT, BCT does not require ­physical compression of the breast (and the associated discomfort). BCT also alleviates tissue superposition much ­better than DBT. Considering that the partial reduction in tissue superposition enabled by DBT has shown clinical benefits in terms of reduced false-positive exams and increased cancer detection rate, it is imperative to understand whether nearly complete elimination of ­tissue superposition further improves clinical performance.

The benefit of fully 3D tomographic imaging can be inferred from the routine use of conventional whole-body CT for imaging almost all organs and anatomical regions, as exemplified in low-dose CT for lung cancer screening (NLST Research Team 2011). However, there is no CT technique for breast cancer screening. Dedicated BCT could address this need.

BCT Technology

Early-generation systems used off-the-shelf technology for detectors and x-ray sources, until components better suited for BCT became available (technological aspects and engineering approaches used in several clinical prototype BCT systems and in bench-top systems emulating BCT are reviewed in Sarno et al. 2015). The ­early-generation systems helped in identifying challenges and solutions to them.

At the time of this writing, only one BCT system has received FDA approval. In 2015 the agency approved a cone-beam dedicated BCT system (CBCT 1000, ­Koning Corp.) for diagnostic use (BCT technology needs additional development for adaptation to screening). ­Prototype systems have been or are being developed by ZumaTek Inc., Malcova LLC, and Izotropic Corporation. In the European Union two systems have received CE mark approvals: Koning’s CBCT 1000 and nu:view, a product of Advanced Breast-CT GmbH (figure 3).

Vedantahm and Karellas Fig 3.gif
FIGURE 3 Two clinical dedicated breast computed tomography (BCT) systems: (A) cone-beam (courtesy: Koning Corporation) and (B) photon-counting (courtesy: Advanced Breast-CT GmbH).

Broadly, the method used for BCT data acquisition can be classified as cone-beam or multidetector (figure 4). Multidetector BCT, also called helical BCT, is similar to conventional whole-body CT.

Vedantham and Karellas Fig 4.gif
FIGURE 4 Illustrations of (A) cone-beam breast computed tomography (BCT) and (B) multidetector BCT. Dots denote the x-ray source trajectory. In cone-beam BCT, the entire breast volume is imaged in each projection using a large-area imaging detector, while the x-ray source and detector travels along a circular trajectory. In multidetector BCT, the dimension of the detector along the length from chest wall to nipple is limited and projection data are acquired while the x-ray source and detector travel along a helical trajectory.

Table 1 summarizes the technical specifications of these two commercial systems. Cone-beam BCT systems (including prototypes) use amorphous silicon indirect conversion flat-panel detectors or CMOS detectors coupled to a thallium-doped cesium iodide scintillator and are capable of scan times of 10 seconds or less.

Vedantham and Karellas Table 1.gif

Helical BCT uses a photon-counting cadmium ­telluride detector, which suppresses electronic noise during image acquisition. The scan time of 7–12 seconds depends on the chest wall–to-nipple dimension of the breast. Currently both cone-beam and helical BCT systems use filtered back-projection algorithms for image reconstruction. For further information on the technical aspects of cone-beam and helical BCT, ­readers are referred to a topical review (Zhu et al. 2022).

Clinical Studies with BCT

BCT is used primarily for diagnostic evaluation of suspicious lesions. A multireader, multicase study (Cole et al. 2015) of noncontrast BCT for diagnostic evaluation used 18 readers interpreting 235 cases (52 negatives, 104 benign, and 79 cancers), all of which had either biopsy verification or a 1-year negative follow-up. Each reader interpreted both modalities: diagnostic mammography workup along with screening mammograms and noncontrast BCT with screening mammograms. The study found that the sensitivity of BCT was higher than that of diagnostic mammography (88 percent vs. 84 percent, P = 0.008). Similar to whole-body CT, BCT is ­readily amenable to contrast-enhanced imaging with intra­venously administered iodinated contrast media.

Other studies have shown that contrast-enhanced BCT improves discrimination between benign and malignant lesions (Prionas et al. 2010), detects lesions that are not visible in a standard diagnostic workup (­Seifert et al. 2014), improves sensitivity (He et al. 2016), and correlates with immunohistochemical subtypes and proliferative potential (Uhlig et al. 2017). And a BCT-guided biopsy system allows biopsy of a lesion that is seen only on BCT (Wienbeck et al. 2017a).

Monitoring response to neoadjuvant therapy and predicting its treatment outcome are potential opportunities for BCT. A pilot study showed that noncontrast BCT could monitor changes in primary tumor volume (Vedantham et al. 2014). There is a need for large-scale studies using noncontrast and contrast-enhanced BCT to better understand its value.

Needed BCT Improvements

The most important opportunity for BCT is to translate it as a primary screening tool, potentially replacing DBT. This would require the capability to perform noncontrast breast imaging at MGD acceptable for screening, ensure sufficient posterior coverage, and be an indicator of early-stage cancer (particularly ductal carcinoma in situ).

A standard mammography screening exam comprises two views: craniocaudal and mediolateral oblique (approximately 45 degrees from the craniocaudal view). Since BCT replaces both by a single scan, 6 mGy is generally considered an acceptable limit. For an early prototype of diagnostic cone-beam BCT the median MGD was 12.6 mGy, similar to and within the range for diagnostic mammography (Vedantham et al. 2013b). Technical improvements, particularly with automatic exposure control, have reduced the radiation dose. More recent studies have reported MGD of 7.2 mGy with cone-beam BCT (Wienbeck et al. 2017b) and approximately 6.5 mGy with helical BCT for an average breast (Germann et al. 2021).

An approach called sparse-view acquisition may reduce the radiation dose by reducing the number of projections. Technical evaluations of image reconstruction algorithms based on compressed sensing (Tseng et al. 2020) and deep learning (Fu et al. 2020; Xie et al. 2020) have been reported, but clinical studies are lacking and further investigation is needed.

In addition, achieving adequate posterior coverage with BCT is important for clinical acceptance. ­Pectoralis muscle was included in 78 percent (107/137) of exams with an early cone-beam BCT prototype (Vedantham et al. 2012). After redesign of the cone-beam BCT, pectoralis muscle was included in 94 percent (84/89) of exams (Vedantham et al. 2013a). With helical BCT, it was included in 58 percent (341/591) of exams (Berger et al. 2020). Further improvement in posterior coverage is possible with the use of detectors with a small bezel along the chest wall.

Regarding microcalcifications, cone-beam and helical (Shim et al. 2020) BCTs resolve 0.22 mm and 0.196 mm, respectively. This exceeds the MQSA requirement of visualizing 0.32 mm calcification cluster.


Digital breast tomosynthesis has demonstrated benefits over digital mammography in terms of reducing false-positive results and improving lesion detection by reducing tissue superposition. However, it is a quasi-3D imaging technique.

Dedicated breast computed tomography presents an opportunity to realize fully 3D tomographic x-ray imaging. It can be a platform technology addressing needs such as risk estimation using breast density, breast ­cancer screening using a noncontrast exam, diagnostic evaluation, postdiagnosis extent-of-disease evaluation, monitoring and prediction of response to neoadjuvant therapy, and surgical planning.

Realizing this opportunity requires addressing challenges in a collaborative manner among radiologists, oncologists, engineers, physicists, applied mathematicians, and computational scientists, in particular for translating BCT to screening. Importantly, clinical trials are needed to evaluate the benefits and limitations of BCT for the applications discussed.


The authors thank David Georges (Koning Corporation) and Benjamin Kalender (Advanced Breast-CT GmbH) for sharing information about their BCT systems. This paper includes knowledge gained from work supported by grants R01CA199044, R01CA241709, R01CA128906, and R21CA134128 from the National Cancer Institute (NCI) of the National Institutes of Health (NIH). The contents are solely the responsibility of the authors and do not necessarily reflect the official views of the NCI or NIH.


Berger N, Marcon M, Frauenfelder T, Boss A. 2020. Dedicated spiral breast computed tomography with a single photon-counting detector: Initial results of the first 300 women. Investigative Radiology 55(2):68–72.

Boone JM, Nelson TR, Lindfors KK, Seibert JA. 2001. Dedicated breast CT: Radiation dose and image quality evaluation. Radiology 221(3):657–67.

Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, Jong RA, Hislop G, Chiarelli A, Minkin S, Yaffe MJ. 2007. Mammographic density and the risk and detection of breast cancer. New England Journal of Medicine 356(3):227–36.

Chang CH, Sibala JL, Fritz SL, Dwyer SJ 3rd, Templeton AW, Lin F, Jewell WR. 1980. Computed tomography in detection and diagnosis of breast cancer. Cancer 46(4 Suppl):939–46.

Chen SC, Carton AK, Albert M, Conant EF, Schnall MD, Maidment AD. 2007. Initial clinical experience with contrast-enhanced digital breast tomosynthesis. Academic Radiology 14(2):229–38.

Cole EB, Campbell AS, Vedantham S, Pisano ED, Karellas A. 2015. Clinical performance of dedicated breast computed tomography in comparison to diagnostic digital mammography (SSA01-09). 101st Scientific Assembly and Ann Mtg, Radiological Society of North America, Nov 29–Dec 4, Chicago.

Duffy SW, Tabár L, Yen AM, Dean PB, Smith RA, ­Jonsson H, Törnberg S, Chiu SY, Chen SL, Jen GH, and 6 ­others. 2021. Beneficial effect of consecutive screening ­mammography examinations on mortality from breast cancer: A prospective study. Radiology 299(3):541–47.

Friedewald SM, Rafferty EA, Rose SL, Durand MA, Plecha DM, Greenberg JS, Hayes MK, Copit DS, Carlson KL, Cink TM, and 4 others. 2014. Breast cancer screening using tomosynthesis in combination with digital ­mammography. JAMA 311(24):2499–507.

Fu Z, Tseng HW, Vedantham S, Karellas A, Bilgin A. 2020. A residual dense network assisted sparse view reconstruction for breast computed tomography. Scientific Reports 10(1):21111.

Germann M, Shim S, Angst F, Saltybaeva N, Boss A. 2021. Spiral breast computed tomography (CT): Signal-to-noise and dose optimization using 3D-printed phantoms. European Radiology 31(6):3693–02.

He N, Wu YP, Kong Y, Lv N, Huang ZM, Li S, Wang Y, Geng ZJ, Wu PH, Wei WD. 2016. The utility of breast cone-beam computed tomography, ultrasound, and digital mammography for detecting malignant breast tumors: A prospective study with 212 patients. European Journal of Radiology 85(2):392–403.

Hubbard RA, Kerlikowske K, Flowers CI, Yankaskas BC, Zhu W, Miglioretti DL. 2011. Cumulative probability of false-positive recall or biopsy recommendation after 10 years of screening mammography: A cohort study. Annals of Internal Medicine 155(8):481–92.

Johnson K, Lang K, Ikeda DM, Akesson A, Andersson I, Zackrisson S. 2021. Interval breast cancer rates and tumor characteristics in the prospective population-based Malmö Breast Tomosynthesis Screening Trial. Radiology 299(3):559–67.

Lauby-Secretan B, Scoccianti C, Loomis D, Benbrahim-­Tallaa L, Bouvard V, Bianchini F, Straif K. 2015. Breast-cancer screening: Viewpoint of the IARC Working Group. New England Journal of Medicine 372(24):2353–58.

Lewin JM, Isaacs PK, Vance V, Larke FJ. 2003. Dual-energy contrast-enhanced digital subtraction mammography: ­Feasibility. Radiology 229(1):261–68.

Lowry KP, Trentham-Dietz A, Schechter CB, Alagoz O, ­Barlow WE, Burnside ES, Conant EF, Hampton JM, Huang H, Kerlikowske K, and 6 others. 2020. Long-term outcomes and cost-effectiveness of breast cancer screening with digital breast tomosynthesis in the United States. Journal of the National Cancer Institute 112(6):582–89.

NLST [National Lung Screening Trial] Research Team. 2011. Reduced lung-cancer mortality with low-dose computed tomographic screening. New England Journal of Medicine 365(5):395–409.

Niklason LT, Christian BT, Niklason LE, Kopans DB, ­Castleberry DE, Opsahl-Ong BH, Landberg CE, Slanetz PJ, Giardino AA, Moore R, and 12 others. 1997. Digital tomosynthesis in breast imaging. Radiology 205(2):399–406.

Pisano ED, Gatsonis C, Hendrick E, Yaffe M, Baum JK, ­Acharyya S, Conant EF, Fajardo LL, Bassett L, D’Orsi C, and 2 others. 2005. Diagnostic performance of digital ­versus film mammography for breast-cancer screening. New ­England Journal of Medicine 353(17):1773–83.

Prionas ND, Lindfors KK, Ray S, Huang SY, Beckett LA, Monsky WL, Boone JM. 2010. Contrast-enhanced dedicated breast CT: Initial clinical experience. Radiology 256(3):714–23.

Raptopoulos V, Baum JK, Hochman M, Karellas A, Houlihan MJ, D’Orsi CJ. 1996. High resolution CT mammography of surgical biopsy specimens. Journal of Computer Assisted Tomography 20(2):179–84.

Sarno A, Mettivier G, Russo P. 2015. Dedicated breast computed tomography: Basic aspects. Medical Physics 42(6):2786–804.

Sechopoulos I. 2013. A review of breast tomosynthesis – Part I: The image acquisition process. Medical Physics 40(1):014301.

Seifert P, Conover D, Zhang Y, Morgan R, Arieno A, ­Destounis S, Somerville P, Murphy PF. 2014. Evaluation of malignant breast lesions in the diagnostic setting with cone beam breast computed tomography (breast CT): Feasibility study. Breast Journal 20(4):364–74.

Shim S, Saltybaeva N, Berger N, Marcon M, Alkadhi H, Boss A. 2020. Lesion detectability and radiation dose in spiral breast CT with photon-counting detector technology: A phantom study. Investigative Radiology 55(8):515–23.

Skaane P, Bandos AI, Gullien R, Eben EB, Ekseth U, ­Haakenaasen U, Izadi M, Jebsen IN, Jahr G, Krager M, and 3 others. 2013. Comparison of digital ­mammography alone and digital mammography plus tomosynthesis in a ­population-based screening program. Radiology 267(1):47–56.

Suryanarayanan S, Karellas A, Vedantham S, Glick SJ, D’Orsi CJ, Baker SP, Webber RL. 2000. Comparison of ­tomosynthesis methods used with digital mammography. Academic Radiology 7(12):1085–97.

Suryanarayanan S, Karellas A, Vedantham S, Baker SP, Glick SJ, D’Orsi CJ, Webber RL. 2001. Evaluation of linear and nonlinear tomosynthetic reconstruction methods in digital mammography. Academic Radiology 8(3):219–24.

Tseng HW, Vedantham S, Karellas A. 2020. Cone-beam breast computed tomography using ultra-fast image reconstruction with constrained, total-variation minimization for suppression of artifacts. Physica Medica 73:117–24.

Uhlig J, Fischer U, von Fintel E, Stahnke V, Perske C, Lotz J, Wienbeck S. 2017. Contrast enhancement on cone-beam breast-CT for discrimination of breast cancer immunohistochemical subtypes. Translational Oncology 10(6):904–10.

Vedantham S, Shi L, Karellas A, O’Connell AM. 2012. Dedicated breast CT: Fibroglandular volume measurements in a diagnostic population. Medical Physics 39(12):7317–28.

Vedantham S, Karellas A, Emmons MM, Moss LJ, Hussain S, Baker SP. 2013a. Dedicated breast CT: Geometric design considerations to maximize posterior breast coverage. ­Physics in Medicine and Biology 58(12):4099–118.

Vedantham S, Shi L, Karellas A, O’Connell AM, Conover DL. 2013b. Personalized estimates of radiation dose from dedicated breast CT in a diagnostic population and comparison with diagnostic mammography. Physics in Medicine and Biology 58(22):7921–36.

Vedantham S, O’Connell AM, Shi L, Karellas A, Huston AJ, Skinner KA. 2014. Dedicated breast CT: Feasibility for monitoring neoadjuvant chemotherapy treatment. Journal of Clinical Imaging Science 4:64.

Vedantham S, Karellas A, Vijayaraghavan GR, Kopans DB. 2015. Digital breast tomosynthesis: State of the art. ­Radiology 277(3):663–84.

Whelehan P, Evans A, Wells M, Macgillivray S. 2013. The effect of mammography pain on repeat participation in breast cancer screening: A systematic review. Breast 22(4):389–94.

Wienbeck S, Lotz J, Fischer U. 2017a. Feasibility of vacuum-assisted breast cone-beam CT-guided biopsy and comparison with prone stereotactic biopsy. American Journal of Roentgenology 208(5):1154–62.

Wienbeck S, Uhlig J, Luftner-Nagel S, Zapf A, Surov A, von Fintel E, Stahnke V, Lotz J, Fischer U. 2017b. The role of cone-beam breast-CT for breast cancer detection relative to breast density. European Radiology 27(12):5185–95.

Xie H, Shan H, Cong W, Liu C, Zhang X, Liu S, Ning R, Wang GE. 2020. Deep efficient end-to-end reconstruction (DEER) network for few-view breast CT image reconstruction. IEEE Access 8:196633–46.

Yaffe MJ, Boone JM, Packard N, Alonzo-Proulx O, Huang SY, Peressotti CL, Al-Mayah A, Brock K. 2009. The myth of the 50-50 breast. Medical Physics 36(12):5437–43.

Zhu Y, O’Connell AM, Ma Y, Liu A, Li H, Zhang Y, Zhang X, Ye Z. 2022. Dedicated breast CT: State of the art – Part I. Historical evolution and technical aspects. ­European ­Radiology 32(3):1579–89.



[1]  In the United States, the standard breast has been modeled (since at least the 1970s) as a 4.3 cm thick compressed breast consisting of 50 percent fibroglandular and 50 percent adipose ­tissue. Subsequent studies have shown that the average compressed breast thickness is approximately 5.9 cm and the average composition, excluding the skin, is 14.3–17.2 percent fibroglandular tissue (Yaffe et al. 2009; Vedantham et al. 2012).

[2] insight s/mqsa-national-statistic

[3] trials/nci-supported/tmist#goals

[4] Resourc es/Breast-Density-bro_ACR_SBI.pdf

[5]  National Cancer Institute Cancer Trends Progress Report, Jul 2021,

About the Author:Srinivasan Vedantham is a professor in the Departments of Medical Imaging and Biomedical Engineering, and Andrew Karellas is a professor in the Department of Medical Imaging, both at the University of Arizona.