Patient‐derived heterogeneous breast phantoms for advanced dosimetry in mammography and tomosynthesis

Abstract

Background Understanding the magnitude and variability of the radiation dose absorbed by the breast fibroglandular tissue during mammography and digital breast tomosynthesis (DBT) is of paramount importance to assess risks versus benefits. Although homogeneous breast models have been proposed and used for decades for this purpose, they do not accurately reflect the actual heterogeneous distribution of the fibroglandular tissue in the breast, leading to biases in the estimation of dose from these modalities. Purpose To develop and validate a method to generate patient‐derived, heterogeneous digital breast phantoms for breast dosimetry in mammography and DBT. Methods The proposed phantoms were developed starting from patient‐based models of compressed breasts, generated for multiple thicknesses and representing the two standard views acquired in mammography and DBT, that is, cranio‐caudal (CC) and medio‐lateral‐oblique (MLO). Internally, the breast phantoms were defined as consisting of an adipose/fibroglandular tissue mixture, with a nonspatially uniform relative concentration. The parenchyma distributions were obtained from a previously described model based on patient breast computed tomography data that underwent simulated compression. Following these distributions, phantoms with any glandular fraction (1%–100%) and breast thickness (12–125 mm) can be generated, for both views. The phantoms were validated, in terms of their accuracy for average normalized glandular dose (DgN) estimation across samples of patient breasts, using 88 patient‐specific phantoms involving actual patient distribution of the fibroglandular tissue in the breast, and compared to that obtained using a homogeneous model similar to those currently used for breast dosimetry. Results The average DgN estimated for the proposed phantoms was concordant with that absorbed by the patient‐specific phantoms to within 5% (CC) and 4% (MLO). These DgN estimates were over 30% lower than those estimated with the homogeneous models, which overestimated the average DgN by 43% (CC), and 32% (MLO) compared to the patient‐specific phantoms. Conclusions The developed phantoms can be used for dosimetry simulations to improve the accuracy of dose estimates in mammography and DBT.

Dedicated cone‐beam breast CT: Data acquisition strategies based on projection angle‐dependent normalized glandular dose coefficients

“…Importantly, Model I that used heterogeneous tissue distribution and real breast shape showed substantial variation in angular DgN CT value and is consistent with the simplified analytical model as the COM f was farther from the AOR (R = 3.1 mm for the centroid of COM f distribution). Prior studies 15,16,33,38 reporting on tissue distribution of uncompressed breasts from breast CT show that in general there is more fibroglandular tissue inferior to the mid-plane of the breast, where the breast mid-plane is defined as per Huang et al 38 Our analysis is based on COM f and COM b to be consistent with the theoretical model, whereas prior literature on tissue distribution report on the fraction of fibroglandular volume/area along the craniocaudal direction. Analyzing the cohort of 75 breast volumes in this study, COM f was inferior to the breast mid-plane in 61/75 (81%) breasts and was superior to COM b in 60/75 (80%) breasts.…”

“…Prior studies 15,16,33,38 reporting on tissue distribution of uncompressed breasts from breast CT show that in general there is more fibroglandular tissue inferior to the mid‐plane of the breast, where the breast mid‐plane is defined as per Huang et al 38 Our analysis is based on $$CO{M_f}$$ and $$CO{M_b}$$ to be consistent with the theoretical model, whereas prior literature on tissue distribution report on the fraction of fibroglandular volume/area along the craniocaudal direction. Analyzing the cohort of 75 breast volumes in this study, $$CO{M_f}$$ was inferior to the breast mid‐plane in 61/75 (81%) breasts and was superior to $$CO{M_b}$$ in 60/75 (80%) breasts.…”

“…Similar breast models representative of the compressed breast have been proposed to study the radiation dose in mammography and tomosynthesis. 33 An analytical model in conjunction with center-of -mass (COM) distribution analysis is used to qualitatively explain the projection angle dependent trends in angular DgN CT .…”

“…Thus, three breast models are used to investigate the angular $$Dg{N^{CT}}$$: (I) patient‐specific voxelized breast with heterogeneous tissue distribution and real breast shape, (II) patient‐specific breast shape with homogeneous tissue distribution and matched fibroglandular weight fraction, and (III) homogeneous semi‐ellipsoidal breast with matched breast dimensions and fibroglandular weight fraction. Similar breast models representative of the compressed breast have been proposed to study the radiation dose in mammography and tomosynthesis 33 . An analytical model in conjunction with center‐of‐mass ($$COM$$) distribution analysis is used to qualitatively explain the projection angle dependent trends in angular $$Dg{N^{CT}}$$.…”

End-to-end mammographic breast density quantification with deep learning: preliminary study on simulated mammograms

“…The spectrum model used was based on the work of Hernandez et al [12]. The simulations were performed using the geometry and acquisition settings of the Siemens Mammomat Inspiration (Forchheim, Germany) clinical system, with the tube voltage varying between 26 kV and 32 kV according to the breast thickness [13] and a detector pixel size of 0.085 mm. The simulations were then subsequently repeated, only for the test set, using the geometry and acquisition settings of a different mammographic system (Hologic Selenia Dimensions), with the tube voltage varying between 25 kV and 36 kV according to the breast thickness and a detector pixel size of 0.07 mm.…”