Homogeneous vs. patient specific breast models for Monte Carlo evaluation of mean glandular dose in mammography

Sarno, Antonio; Mettivier, Giovanni; Lillo, Francesca Di; Bliznakova, Kristina; Sechopoulos, Ioannis; Russo, Paolo

doi:10.1016/j.ejmp.2018.04.392

Cited by 29 publications

(34 citation statements)

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“…A common model for breast dosimetry includes an internal breast volume composed of a homogeneous mixture of glandular and adipose tissue, surrounded by an envelope of skin tissue. New 3D breast imaging techniques (dedicated breast computed tomography, BCT) [3] provided a detailed 3D description of the breast anatomy in patients, so permitting more realistic breast models for dosimetry in 2D and 3D X-ray breast imaging [4][5][6][7][8][9]. In particular, the assumption of a breast skin thickness of 4-5 mm for the breast model -as commonly adopted in quality assurance protocols [10][11][12][13] -was questioned recently, on the basis of measurements obtained by clinical BCT scans [4,14]: they indicated a skin thickness, on average, of 1.45 mm, with no evidence of subcutaneous fat layer [4].…”

Section: Introductionmentioning

confidence: 99%

“…As a further investigative aspect, in quality assurance protocols [11][12][13] breast models for MGD estimates do not consider the heterogeneous distribution of the breast glandular tissue within the organ volume: indeed, they assume that the inner portion of the breast is made of a homogeneous mixture of glandular and adipose tissue. This model simplification may produce a bias in MGD estimates, when compared to estimates based on patient specific structured models derived from clinical BCT scans [5,6,8,9]. In particular, this assumption neglects the large variability in the glandular tissue texture and it is representative of a hypothetical "average" breast [1,8,9].…”

Section: Introductionmentioning

confidence: 99%

“…This model simplification may produce a bias in MGD estimates, when compared to estimates based on patient specific structured models derived from clinical BCT scans [5,6,8,9]. In particular, this assumption neglects the large variability in the glandular tissue texture and it is representative of a hypothetical "average" breast [1,8,9].…”

Section: Introductionmentioning

confidence: 99%

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Normalized glandular dose coefficients in mammography, digital breast tomosynthesis and dedicated breast CT

et al. 2018

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To provide mean glandular dose (MGD) estimates via Monte Carlo (MC) simulations as a function of the breast models and scan parameters in mammography, digital breast tomosynthesis (DBT) and dedicated breast CT (BCT). Methods: The MC code was based on GEANT4 toolkit. The simulated compressed breast was either a cylinder with a semi-circular section or ad hoc shaped for oblique view (MLO). In DBT we studied the influence of breast models and exam parameters on the T-factors (i.e. the conversion factor for the calculation of the MGD in DBT from that for a 0-degree projection), and in BCT we investigated the influence on the MGD estimates of the ion chamber volume used for the air kerma measurements. Results: In mammography, a model representative of a breast undergoing an MLO view exam did not produce substantial differences (0.4%) in MGD estimates, when compared to a conventional cranio-caudal (CC) view breast model. The beam half value layer did not present a significant influence on T-factors in DBT (< 0.8%), while the skin model presented significant influence on MGD estimates (up to 3.3% at 30 degrees scan angle), increasing for larger scan angles. We derived a correction factor for taking into account the different ion chamber volume used in MGD estimates in BCT. Conclusions: A series of MC code modules for MGD estimates in 2D and 3D breast imaging have been developed in order to take into account the most recent advances in breast models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Normalized glandular dose coefficients in mammography, digital breast tomosynthesis and dedicated breast CT

et al. 2018

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“…No irregular lesions have been so far modelled and included in that breast model. Breast cancer computational models are important for the development of new breast imaging techniques, as well as for realistic models for X-ray breast dosimetry [23,24]. As a large number of different breast cancer models would be normally used, there is a strong need to develop a method for generating patterns of irregular formations, typically in the case of malignant tumours.…”

Section: Introductionmentioning

confidence: 99%

Models of breast lesions based on three-dimensional X-ray breast images

et al. 2019

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This paper presents a method for creation of computational models of breast lesions with irregular shapes from patient Digital Breast Tomosynthesis (DBT) images or breast cadavers and whole-body Computed Tomography (CT) images. The approach includes six basic steps: (a) normalization of the intensity of the tomographic images; (b) image noise reduction; (c) binarization of the lesion area, (d) application of morphological operations to further decrease the level of artefacts; (e) application of a region growing technique to segment the lesion; and (f) creation of a final 3D lesion model. The algorithm is semi-automatic as the initial selection of the region of the lesion and the seeds for the region growing are done interactively. A software tool, performing all of the required steps, was developed in MATLAB. The method was tested and evaluated by analysing anonymized sets of DBT patient images diagnosed with lesions. Experienced radiologists evaluated the segmentation of the tumours in the slices and the obtained 3D lesion shapes. They concluded for a quite satisfactory delineation of the lesions. In addition, for three DBT cases, a delineation of the tumours was performed independently by the radiologists. In all cases the abnormality volumes segmented by the proposed algorithm were smaller than those outlined by the experts. The calculated Dice similarity coefficients for algorithm-radiologist and radiologist-radiologist showed similar values. Another selected tumour case was introduced into a computational breast model to recursively assess the algorithm. The relative volume difference between the ground-truth tumour volume and the one obtained by applying the algorithm on the synthetic volume from the virtual DBT study is 5% which demonstrates the satisfactory performance of the proposed segmentation algorithm. The software tool we developed was used to create models of different breast abnormalities, which were then stored in a database for use by researchers working in this field. Anthropomorphic voxel breast phantoms with realistic tissue

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“…The DgN values of the 3D models were 5.4%-38.0% lower than those of the homogeneous model. Sarno et al 16 compared four types of homogeneous breast models with 20 patient-specific digital breast phantoms. The MGD differences were varied from 43% to −28%, inferring the importance of glandular distribution in the breast models.…”

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Three-layer heterogeneous mammographic phantoms for Monte Carlo simulation of normalized glandular dose coefficients in mammography

Chang

Lai

et al. 2020

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Normalized glandular dose (DgN) coefficients obtained using homogeneous breast phantoms are commonly used in breast dosimetry for mammography. However, glandular tissue is heterogeneously distributed in the breast. This study aimed to construct three-layer heterogeneous mammographic phantoms (THEPs) to examine the effect of glandular distribution on DgN coefficient. Each layer of THEPs was set to 25%, 50%, or 75% glandular fraction to emulate heterogeneous glandular distribution. Monte Carlo simulation was performed to attain mean glandular dose (MGD) and air kerma at 22-36 kVp and W/Al, W/Rh, and W/Ag target-filter combinations. The heterogeneous DgN coefficient was calculated as functions of the mean glandular fraction (MGF), breast thickness, tube voltage, and half-value layer. At 50% MGF, the heterogeneous DgN coefficients for W/Al, W/Rh, and W/Ag differed by 40.3%, 36.7%, and 31.2%. At 9-cm breast thickness, the DgN values of superior and inferior glandular distributions were 25.4% higher and 29.2% lower than those of uniform distribution. The proposed THEPs can be integrated with conventional breast dosimetry to consider the heterogeneous glandular distribution in clinical practice.Breast cancer is the second most common cause of cancer deaths among women 1 , and its incidence increases annually. X-ray mammography has become a primary tool for breast cancer screening because of the high sensitivity and specificity for microcalcification and mass detection 2 . Mammography is also adopted for the high effectiveness/cost ratio. However, the glandular tissue in the breast is sensitive to radiation. The radiation exposure during mammography may increase the risk of radiation-induced secondary breast malignancy 3 . Therefore, assessment of glandular dose in the breast is crucial.In modern breast dosimetry, one of the most critical parameters is the normalized glandular dose (DgN) coefficient, which is obtained using Monte Carlo simulation of mean glandular dose (MGD) in a breast phantom 4 . Boone used simple homogeneous breast phantoms to simulate the breast absorbed dose and applied a G factor to calculate MGD 5 . He further proposed monoenergetic DgN coefficients to facilitate rapid calculation of the DgN coefficient for arbitrary X-ray energy spectra 6 . Dance et al. 7 employed homogeneous semicylindrical phantoms to assess MGD conversion factor g, breast composition factor c, and X-ray spectrum factor s. The authors further supplemented the conversion factors for the high-energy spectra used for contrast enhanced digital mammography by using the same homogeneous breast model 8 . The above method is now the standard breast dosimetry protocols for mammography in the United Kingdom, European Union, and IAEA and is also adopted in the quality assurance protocols in USA. Sarno et al. 9 used homogeneous semi-cylinder and ad hoc shaped phantoms to exam MGD in mammography and calculate the t-factor (relative glandular dose, RGD) and T-factor in digital breast tomosynthesis (DBT).Three-dimensional (3D) imaging modalit...

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Homogeneous vs. patient specific breast models for Monte Carlo evaluation of mean glandular dose in mammography

Abstract: The homogeneous breast models led to maximum MGD underestimation and overestimation of 43% and 28%, respectively, when compared to patient specific breast phantoms derived from clinical CT scans.

Cited by 29 publications

References 34 publications

Normalized glandular dose coefficients in mammography, digital breast tomosynthesis and dedicated breast CT

Normalized glandular dose coefficients in mammography, digital breast tomosynthesis and dedicated breast CT

Models of breast lesions based on three-dimensional X-ray breast images

Three-layer heterogeneous mammographic phantoms for Monte Carlo simulation of normalized glandular dose coefficients in mammography

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