The compressed breast during mammography and breast tomosynthesis:<i>in vivo</i>shape characterization and modeling

Rodríguez‐Ruiz, Alejandro; Agasthya, Greeshma; Sechopoulos, Ioannis

doi:10.1088/1361-6560/aa7cd0

Cited by 27 publications

(36 citation statements)

References 42 publications

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“…Binary shapes of compressed breasts were obtained using a previously developed patient-based 3D model of breasts undergoing mammography and DBT. 26 A dataset of compressed breast surfaces was first obtained by imaging a group of women with structured light surface imaging, performed in parallel with a DBT exam. Subsequently, the breast surface between the compression paddle and the support table from each patient was characterized with a set of equidistant arcs, which was then reduced to a smaller set of linearly independent parameters via principal component analysis (PCA).…”

Section: Compressed Breast Shapesmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Compressed Breast Shapesmentioning

confidence: 99%

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

et al. 2022

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“…Synthetic breast phantoms of realistic shape and dimension were used in the cone beam MC simulations. Such phantoms were inspired in the study published by Rodríguez-Ruiz et al based in mathematical breast modelling (Rodríguez-Ruiz, Agasthya, and Sechopoulos, 2017) where, given a thickness and a glandularity, the shape and dimension of the compressed breast is characterized and modelled. This shape information was used to simulate the compressed breast in Geant4, as shown in phantom Figure 4-a.…”

Section: Monte Carlo Geometrymentioning

confidence: 99%

A semi-empirical model for scatter field reduction in digital mammography

Marimón¹,

Marsden²,

Nait-Charif³

et al. 2021

Phys. Med. Biol.

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X-ray mammography is the gold standard technique in breast cancer screening programmes. One of the main challenges that mammography is still facing is scattered radiation, which degrades the quality of the image and complicates the diagnosis process. Anti-scatter grids, the main standard physical scattering reduction technique, have some unresolved challenges as they increase the dose delivered to the patient, do not remove all the scattered radiation and increase the cost of the equipment. Alternative scattering reduction methods based on post-processing algorithms, have lately been under investigation. This study is concerned with the use of image post-processing to reduce the scatter contribution in the image, by convolving the primary plus scatter image with kernels obtained from simplified Monte Carlo (MC) simulations. The proposed semi-empirical approach uses up to five thickness-dependant symmetric kernels to accurately estimate the scatter contribution of different areas of the image. Single breast thickness-dependant kernels can over-estimate the scatter signal up to 60%, while kernels adapting to local variations have to be modified for each specific case adding high computational costs. The proposed method reduces the uncertainty to a 4%–10% range for a 35–70 mm breast thickness range, making it a very efficient, case-independent scatter modelling technique. To test the robustness of the method, the scattered corrected image has been successfully compared against full MC simulations for a range of breast thicknesses. In addition, clinical images of the 010A CIRS phantom were acquired with a mammography system with and without the presence of the anti-scatter grid. The grid-less images were post-processed and their quality was compared against the grid images, by evaluating the contrast-to-noise ratio and variance ratio using several test objects, which simulate calcifications and tumour masses. The results obtained show that the method reduces the scatter to similar levels than the anti-scatter grids.

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“…However, as explained earlier, simple geometrical models have several shortcomings. A recent approach to breast shape estimation uses additional optical devices with structured light technology to directly measure the breast surface 11,12,31,32 . Installing an additional device to the DBT system, however, may cause cost and complication issues, and the technology itself is considered still in its early stage.…”

Section: Introductionmentioning

confidence: 99%

Deep‐learning‐based projection‐domain breast thickness estimation for shape‐prior iterative image reconstruction in digital breast tomosynthesis

et al. 2022

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Background: Digital breast tomosynthesis (DBT) is a technique that can overcome the shortcomings of conventional X-ray mammography and can be effective for the early screening of breast cancer. The compression of the breast is essential during the DBT imaging. However, since the periphery of the breast cannot be compressed to a constant value, nonuniformity of thickness and inplane shape variation happen. These cause inconvenience in diagnosis, scatter correction, and breast density estimation. Purpose: In this study, we propose a deep-learning-based methodology for projection-domain breast thickness estimation and demonstrate a shape-prior iterative DBT image reconstruction. Methods: We prepared the Euclidean distance map, the thickness map, and the thickness corrected image of the simulated breast projections for thickness and shape estimation. Each pixel of the Euclidean distance map denotes a distance to the closest skin-line. The thickness map is defined as a conceptual projection of ideal breast support that differentiates the inner and outer regions of the breast phantom. The thickness projection map thus represents the Xray path lengths of a homogeneous breast phantom. We generated the thickness corrected image by dividing the projection image by the thickness map in a pixel-wise manner. We developed a convolutional neural network for thickness estimation and correction. The network utilizes a projection image and a Euclidean distance image together as a dual input. An estimated breast thickness map is then used for constructing the breast shape mask by use of the discrete algebraic reconstruction technique. Results:The proposed network effectively corrected the breast thickness in various simulation situations. Low normalized root-mean-squared error (1.976%) and high structural similarity (99.997%) indicated a good agreement between the network-generated thickness corrected image and the ground truth image. Compared to the existing methods and simple single-input network, the proposed method showed outperformance in breast thickness estimation and accordingly in breast shape recovery for various numerical phantoms without provoking any significant artifact. We have demonstrated that the uniformity of voxel value has improved by the inclusion of a shape prior for the iterative DBT reconstruction. Conclusions:We presented a novel deep-learning-based breast thickness correction and a shape reconstruction method. This approach to estimating the true thickness map and the shape of the breast undergoing compression can benefit various fields such as improvement of diagnostic breast images, scatter correction, material decomposition, and breast density estimation.

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The compressed breast during mammography and breast tomosynthesis:in vivoshape characterization and modeling

Cited by 27 publications

References 42 publications

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

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

A semi-empirical model for scatter field reduction in digital mammography

Deep‐learning‐based projection‐domain breast thickness estimation for shape‐prior iterative image reconstruction in digital breast tomosynthesis

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